Aug 17, 2021
We didn't know what to expect when we sat down with Greg Beaumont, Senior Business Intelligence Specialist at Microsoft specializing in serving Microsoft's Healthcare space customers' technical Power BI issues. What we got was an insightful, delightful, and impactful conversation with a really cool and smart human!
References in this Episode:
The Future Will Be Decentralized-Charles Hoskinson
Episode Timeline:
Episode Transcript:
Rob Collie (00:00:00):
Hello, friends. Today's guest is Greg Beaumont from Microsoft. Like
one of our previous guests, hopefully, Greg has one of those
interface jobs. The place where the broader Microsoft Corporation
meets its customers at a very detailed and on the ground level. On
one hand, it's one of those impossible jobs. More than 100
customers in the healthcare space look to Greg as their primary
point of contact for all things technical, around Power BI. That's
a tall order, folks. And at the same time, it's one of those
awesome jobs. It's not that dissimilar, really, from our job here
at P3.
Rob Collie (00:00:45):
In a role that, first of all, you get broad exposure to a
tremendous number of organizations and their problems, you learn a
lot super, super quickly. When you're doing it right, your work day
is just nonstop magic. The power platform is magic and not really
because of the technology, but instead because of its impact on the
people who use it, who interact with it, who benefit from it, whose
lives are changed by it. And again, I can't stress this enough,
software usually doesn't do this. And as we talked with him, Krissy
and I just couldn't stop nodding, because we could hear it, he
lives it, just like we do. And I hope that just leaps out of the
audio for you like it did for us.
Rob Collie (00:01:32):
No surprises here, Greg didn't start his life as a data
professional. He's our second guest on this show, whose original
training was in biology. And so, some familiar themes come back
again, that good data professionals come from a wide variety of
backgrounds, that the hybrid tweeners between IT and business are
really where the value is at today. And I love this about Greg,
that we made a point of talking about how much easier it is today
to break into the data profession than it's ever been and what an
amazing thing that is to celebrate.
Rob Collie (00:02:06):
We talked about COVID and specifically its impacts on the industry.
How that has served as a catalyst for many organizations to rethink
their analytic strategy, the implications of remote work, data
privacy and security. And of course, it wouldn't be an episode of
Raw Data, if we didn't nerd out about at least one thing. So, we
get a little bit into genomics and the idea of DNA and RNA as forms
of biological computer code. And as you'd expect, and want, Greg is
far from a one dimensional data professional, just such an
interesting person, authentically human, a real pleasure to speak
with, so let's get into it.
Announcer (00:02:47):
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Rob Collie (00:02:51):
This is the Raw Data by P3 adaptive podcast with your host, Rob
Collie. Find out what the experts at P3 Adaptive can do for your
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data with the human element.
Rob Collie (00:03:13):
Welcome to the show, Greg Beaumont. How are you?
Greg Beaumont (00:03:17):
I'm doing well. How are you all?
Rob Collie (00:03:19):
I think we're doing pretty well.
Greg Beaumont (00:03:19):
Awesome.
Rob Collie (00:03:20):
Business is booming. Data has turned out to be relatively hot
field, but I think it's probably got some legs to it. And the
Microsoft platform also, well, it's just kind of kicking ass, isn't
it? So, business wise, we couldn't be better. I think personally,
we're doing well, too. We won't go into all that. What are you up
to these days? What's your job title and what's an average day look
for you?
Greg Beaumont (00:03:39):
So, I'm working in Microsoft and my title is Technical Specialist.
And I'm a Business Intelligence Technical Specialist, so I focus
almost exclusively on Power BI and where it integrates with other
products within the Microsoft stack. Now, I'm in the Microsoft
field, which is different from a number of guests you've had, who
work at corporate and we're working on the product groups, which is
that I'm there to help the customers.
Greg Beaumont (00:04:01):
And you hear a lot of different acronyms with these titles. So, my
role is often called the TS. In the past, it was called a TSP. It's
just a change in the title. Sometimes you might hear the title,
CSA, Cloud Solution Architect. It's very similar to what I do, but
a little bit different. But effectively from an overarching
standpoint, our goal in the field as Technical Specialists is to
engage with customers, so that they understand how and where to use
our products, and to ensure that they have a good experience when
they succeed.
Rob Collie (00:04:29):
Your job is literally where the Microsoft organism meets the
customers.
Greg Beaumont (00:04:34):
Yep.
Rob Collie (00:04:35):
That's not the role I had. I was definitely on the corporate side,
back in my days at Microsoft. I think the interaction between the
field and corporate has gotten a lot stronger over the years. I
think it's a bit more organic, that interplay, that it used to feel
like crossing a chasm sort of thing. And I don't think that's
really true anymore.
Greg Beaumont (00:04:54):
At a green, I think that's by design, too. So, with the more
frequent release schedules and also kind of how things have changed
under Satya, customer feedback drives the roadmap. So when these
monthly updates come out, a lot of it is based off of customer
demand and what customers are encountering and what they need. So,
we're able to pivot and meet the needs of those customers much more
quickly.
Rob Collie (00:05:15):
Yeah, you mentioned the changing acronyms, right? I mean like yes.
My gosh, a thousand times yes. It's almost like a deliberate
obfuscation strategy. It's like who's what? Why did we need to take
the P off of TSP? I mean, I'm sure it was really important in some
meeting somewhere, but it's just like, "Oh, yeah, it's really hard
to keep track of." It's just a perpetually moving target. But at
the same time, so many fundamentals don't change, right? The things
that customers need and the things that Microsoft needs to provide.
The fundamentals, of course, evolving, but they don't move nearly
as fast as the acronym game.
Greg Beaumont (00:05:52):
Right. I think that acronym game is part of what makes it difficult
your first year here, because people have a conversation and you
don't know what they're talking about. Right?
Rob Collie (00:06:00):
Yeah, yeah, yeah.
Greg Beaumont (00:06:00):
And if they just spelled it out, it would make a lot more
sense.
Rob Collie (00:06:03):
Krissy was talking to me today about, "Am I understanding what Foo
means?" There's an internal Microsoft dialect, right? Krissy was
like, "Is Foo like X? Is it like a placeholder for variable?" I'm
like, "Yes, yes." She's like, "Okay. That's what I thought, but I
just want to make sure."
Krissy Dyess (00:06:18):
That's why there's context clues in grade school really come into
play when you're working with Microsoft organization, because you
really got to take in all the information and kind of decipher it a
bit. And those context clues help out. Greg, how long have you been
in that particular role? Has it been your whole time at Microsoft
or are have you been in different roles?
Greg Beaumont (00:06:36):
So, I should add, too, that I'm specifically in the healthcare org,
and even within healthcare, we've now subspecialized into
sub-verticals within healthcare. So, I work exclusively with
healthcare providers, so people who are providing care to patients
in a patient care setting. I do help out on a few other accounts,
too, but that's my primary area of responsibility.
Greg Beaumont (00:06:55):
So, I started with Microsoft in 2016. I was actually hired into a
regional office as what's called the traditional TSP role and it
was data platform TSP. So, it was what used to be the SQL Server TS
role. A few months later, the annual realign happened, I got moved
over to Modern Workplace because they wanted to have an increased
focus on Power BI, and I had some experience in that area. Plus, I
was the new guy, so they put me into the experimental role. A year
later, that's when they added the industry verticals and that's
when I moved into what is kind of the final iteration of my current
role. And the titles have changed a few times, but I've effectively
been in this role working with healthcare customers for over four
years now.
Rob Collie (00:07:35):
And so, like a double vertical specialization?
Greg Beaumont (00:07:37):
Yeah.
Rob Collie (00:07:37):
Healthcare providers, where there's a hierarchy here?
Greg Beaumont (00:07:40):
Yeah, yeah.
Rob Collie (00:07:41):
Those are the jaw dropping things for me is sometimes people in
roles like yours, even after all that specialization, you end up
with a jillion customers that you're theoretically responsible for.
Double digits, triple digits, single digits in terms of how many
customers you have to cover?
Greg Beaumont (00:07:58):
I'm triple digits. And that is one of the key differences from that
CSA role that you'll see on the Azure team is they tend to be more
focused on just a couple of customers and they get more engaged in
kind of projects. And I will do that with customers, but it's just,
it's a lot more to manage.
Rob Collie (00:08:14):
Yeah. What a challenging job. If you think about it, the minimum
triple digit number is 100, right? So, let's just say, it's 100 for
a moment. Well, you've got 52 weeks a year plus PTO, right? So,
you're just like, "Okay." It is very, very difficult to juggle.
That's a professional skill that is uncommon. I would say that's
probably harder than the acronym game.
Greg Beaumont (00:08:37):
Yeah, there's been times I was on a vacation day and I got a call.
I didn't recognize the number. I'm like, "Okay, I'm going to have
to route this to somebody because I'm off today." And they're like,
"Well, I'm the VP of so and so and we need to do this." And I'm
like, "Okay, I got to go back inside and work now, because this is
an important call." So, you have to be flexible and you're correct,
that it makes it a challenge to have that work-life balance also,
but the work is very rewarding, so it's worth it.
Rob Collie (00:09:01):
Yeah. It's something that vaguely I have a sense of this. I mean,
transitioning from corporate Microsoft to, I mean, you can think of
my role now as field. I'm much, much closer to the customers than I
ever was at corporate. And yes, Brian Jones and I talked about it a
little bit. And this is a bit of an artifact of the old release
model that it was like every few years, you'd release a product,
which isn't the case anymore. But that satisfying feeling of
helping people, like even if you build something amazing back at
Microsoft in the days that I was there, you were never really
around for that victory lap. You would never get that feedback. It
even never make it to you.
Rob Collie (00:09:37):
It was years later muted whereas one of the beautiful things about
working closely with customers and our clients with Power BI, and
actually the Microsoft platform as a whole, is just how quickly you
can deliver these amazingly transformational like light up moments
that go beyond just the professional. You can get this emotional,
really strong validating emotional feeling of having helped. And
that is difficult to get, I think even today, probably, even with
their monthly release cycles, et cetera. By definition, you're just
further removed from the "Wow" that happens out where the people
are.
Greg Beaumont (00:10:15):
Yep. And I'm sure you all see that, too, with your business is that
a lot of work often goes into figuring out what needs to be in
these solutions and reports, but when you actually put it in the
hands of leaders, and they realize the power of what it can provide
for their business, in my case for their patients, for their
doctors, for their nurses, it becomes real. They see it's actually
possible and it's not just a PowerPoint deck.
Rob Collie (00:10:38):
And that sense of possibility, that sense of almost child-like
wonder that comes back at those moments, you just wouldn't expect
from the outside. I had a family member one time say, "Oh, Rob, I
could never do what you do." Basically, it was just saying "How
boring it must be, right?" It's so boring working with software,
working with..." I'm like, "Are you kidding me? This is one of the
places in life where you get to create and just an amazingly
magical." It's really the only word that comes close to capturing
it. You just wouldn't expect that, right? Again, from the outside
like, "Oh, you work in data all day. Boring."
Greg Beaumont (00:11:17):
I'd add to that, that I'd compare it to maybe the satisfaction
people get out of when they beat a game or a video game. That when
you figure out how to do a solution and it works and you put in
that time and that effort and that thought, there's that emotional
reward, you get that I built something that that actually did what
they wanted it to do.
Rob Collie (00:11:35):
Yeah. And after you beat the video game, not only did that happen,
but other people's lives get better as a result of you beating this
game. It's just like it's got all those dynamics, and then some.
All these follow on effects.
Greg Beaumont (00:11:46):
It's like being an athlete and enjoying the sport that you compete
in.
Rob Collie (00:11:50):
Yeah. We're never going to retire. We're going to be the athletes
that hang on way too long.
Greg Beaumont (00:11:56):
Yep.
Rob Collie (00:11:58):
So, unfortunately, I think our careers can go longer than a
professional athletes, so there's that. I can't even really walk up
and down stairs anymore without pain, so. So what about before
Microsoft? What were you up to beforehand and how did you end up in
this line of work in the first place?
Greg Beaumont (00:12:15):
Sure. And I think that's actually something where listeners can get
some value, because the way I got into this line of work, I think
today, there's much more opportunity for people all over the world
from different socioeconomic backgrounds to be able to break into
this field without having to kind of go through the rites of
passage that people used to. So, I was actually a Biology major
from a small school. Came from a military family. I didn't have
corporate contacts or great guidance counseling or anything like
that. My first job right out of school was I said, "Oh, I got a
Biology major. I got a job at a research institution." They're
like, "Okay, you're going to be cleaning out the mouse cages." And
it was sort of $10.50 an hour.
Greg Beaumont (00:12:53):
So, at that point, I said, "Okay, I got to start thinking about a
different line of work here." So, I kind of bounced around a little
bit. I wanted to get into IT, but if you wanted to learn something
like SQL Server, you couldn't do it unless you had a job in IT. As
an average person, you couldn't just go buy a SQL Server and put it
in your home unless you had the amount of money that you needed to
do that. Side projects with Access and Excel. Small businesses did
things probably making less than minimum wage and side gigs, in
addition to what I was doing for full-time work to pay the bills.
Eventually caught on with a hospital where I was doing some
interesting projects with data using Access and Excel. They
wouldn't even give me access to Crystal Reports when we wanted to
do some reporting. That was really where I kind of said , "Data is
where I want to focus."
Greg Beaumont (00:13:41):
We did some projects around things like Radon Awareness, so people
who would build a new house now, they're like, "Oh, I have to pay
$1500 for that Radon machine down in the basement." But when you
talk to a thoracic surgeon and their nursing team and you hear
stories about people who are nonsmokers, perfectly healthy, who
come in with tumors all over their lungs, you realize the value
there and by looking at the data of where there's pockets of radon
in the country reaching out to those people has value, right? I
think it's that human element where you're actually doing something
that makes a difference. So, that kind of opened my eyes.
Greg Beaumont (00:14:14):
I then after that job, I got on with a small consulting company. I
was a Project Manager. It was my first exposure to Microsoft BI. It
was actually ProClarity over SQL Server 2005 and we were working
with data around HEDIS and Joint Commission healthcare performance
measures for one of the VA offices. So, I was the PM and the Data
Architect was building the SSIS packages, built out kind of
skeleton of an analysis services cube. He asked me to lean in on
the dashboarding side, and that's also where I started learning MDX
because we were writing some MDX expressions to start doing some
calculations that we were then exposing in ProClarity. And at that
point, it was like, "This is magic."
Greg Beaumont (00:14:57):
From a used case perspective, what they were doing traditionally
doing was they'd send somebody in from some auditing agency, who
would look at, I think it was 30 to 60 patient records, for each
metric and then they take a look at where all of the criteria hit
for that metric, yes or no. And it would be pass/fail, how good is
this institution doing of meeting this particular expectation. So,
it would be things like, "Does a patient receive aspirin within a
certain amount of time that they've been admitted if they have
heart problems?" Something like that. With looking at it from a
data perspective, you can look at the whole patient population, and
then you could start slicing and dicing it by department, by time
of day that they were admitted, by all of these different
things.
Greg Beaumont (00:15:38):
And that's when I kind of said, "This is really cool, really
interesting. I think there's a big future here." And I kind of
decided to take that route. And from there, I got on with a
Microsoft partner, where I stayed for about six years. And that's
kind of where I was exposed to a lot of very smart, very gifted
people. And I was able to kind of learn from them and then that led
to eventually getting a job at Microsoft. But to make a long story
short, today, you could go online and get Power BI Desktop for
free. There's training resources all over the place, and you could
skill up and get started and get a great job. I'd like to tell
people take the amount of time you spend every night playing video
games and watching television, take half that time and devote it to
learning Power BI and you'll be amazed at how far you get in six to
12 months.
Rob Collie (00:16:24):
That's such good advice. I'm not really allowed to play a lot of
video games, so I might need more time than that. But I had my time
to do that years ago, learning DAX and everything. A couple of
things really jumped out at me there. First of all, you're right,
it was almost like a priesthood before. It was so hard to get your
foot in the door. Look, you had to climb incrementally, multiple
steps in that story to just get to the point where you were sitting
next to the thing that was SSIS and MDX which, again, neither of
those things had a particularly humane learning curve. Even when
you got there, which was a climb, you get to that point and then
they're like, "And here's your cliff. Your smooth cliff that you
have to scale. If you wanted a piece of this technology,"
right?
Rob Collie (00:17:11):
You wanted to learn MDX, you had to get your hands on an SSAS
server. The license for it. And then you had to have a machine you
could install it on that was beefy enough to handle it. It's just,
there's so many barriers to entry. And the data gene, I like to
talk about, it does. It cuts across every demographic, as far as I
can tell, damn near equally everywhere. Let's call it one in 20.
It's probably a little less frequent than that. Let's call it 5% of
the population is carrying the data gene and you've got to get
exposure. And that's a lot easier to get that exposure today than
it was even 10 years ago.
Greg Beaumont (00:17:50):
I'd completely agree with that. The people in this field tend to be
the type of people who likes solving puzzles, who like building
things that are complex and have different pieces, but who also
enjoy the reward of getting it to work at the end. You've had
several guests that have come on the show that come from
nontraditional backgrounds. But I'm convinced that 20 years ago,
there were a lot of people who would have been great data people,
who just never got the opportunity to make it happen.
Greg Beaumont (00:18:14):
Whereas today, the opportunity is there and I think Microsoft has
done a great job with their strategy of letting you learn and try
Power BI. You can go download the dashboard in a day content for
free and the PDF is pretty self-explanatory and if you've used
excel in the past, you can walk through it and teach yourself the
tool. I think the power of that from both the perspective of giving
people opportunity and also building up a workforce for this field
of work is amazing.
Rob Collie (00:18:42):
Yeah. I mean, all those people that were sort of in a sense like
kind of left behind, years ago, they weren't given an avenue. A
large number of them did get soaked up by Excel. If they're
professionally still active today, there's this tremendous
population of Excel people if they were joining the story today,
they might be jumping into Power BI almost from the beginning,
potentially. And of course, if they were doing that, they'd still
be doing Excel. But there's still this huge reservoir of people who
are still tomorrow, think about the number of people tomorrow, just
tomorrow. Today, they're good at Excel and tomorrow, they will sort
of, they'll have their first discovery moment with Power BI. The
first moment of DAX or M or whatever, that's a large number of
people tomorrow who are about to experience. It's almost like did
you see the movie The Game?
Greg Beaumont (00:19:36):
I have not.
Rob Collie (00:19:37):
There's this moment early in the movie where Michael Douglas has
just found out that his brother or something has bought them a pass
to the game. And no one will tell him what it is. He meets this guy
at a bar who says, "Oh, I'm so envious that you get to play for the
first time." Also, this is really silly, but it's also like the
ACDC song For Those About To Rock, We Salute You. For those about
to DAX, we salute you, because that's going to happen tomorrow,
right? Such a population every day that's lighting up and what an
exciting thing to think about. Do you ever get down for any reason,
just stop and think, "Oh, what about the 5000 people today who are
discovering this stuff for the first time." That is a happy
thing.
Greg Beaumont (00:20:16):
Yeah, I actually had a customer where one of their analysts who
turned out to be just a Power BI Rockstar, he said, "I'd been
spending 20 years of my life writing V-lookups, and creating giant
Excel files. And now, everything I was trying to do is at my
fingertips," right? And then within a year, he went from being a
lifelong Excel expert to creating these amazing reports that got
visibility within the organization and provided a ton of value.
Rob Collie (00:20:42):
And that same person you're talking about is also incredibly
steeped in business decision-making. They've been getting a
business training their whole career at the same time. And it's
like suddenly, you have this amazingly capable business tech
hybrid, that literally, it just like moved mountains. It's crazy.
We've talked about that a lot on the show, obviously, the hybrids,
just amazing. And a lot of these people have come to work for
us.
Rob Collie (00:21:09):
That's the most common origin story for our consultants. It's not
the only one. I mean, we do have some people who came from more
traditional IT backgrounds, but they're also hybrids. They
understand business incredibly well. And so, they never really
quite fit in on the pure IT side, either. It's really kind of
interesting.
Greg Beaumont (00:21:26):
Yeah, I think there's still a gap there between IT and business,
even in kind of the way solutions get architected in the field.
It's understanding what the business really wants out of the tool
is often very different from how IT understands to build it. And I
think that's where people like that provide that bridge, to make
things that actually work and then provide the value that's
needed.
Rob Collie (00:21:47):
There's such valuable ambassadors. It's just so obvious when IT is
going to interact with a business unit to help them achieve some
goal. It's so obvious that, of course, who you need to engage with
IT. IT thinks, "We need to engage with the leaders of this business
unit." They've got the secret weapon, these hybrid people that came
up through the ranks with Excel. The word shadow IT is perfect.
These people within the business, like they've been Excel people
for their entire careers, they have an IT style job.
Rob Collie (00:22:22):
Almost all the challenges that IT complains about with working with
business, you take these Excel people and sort of put them in a
room where they feel safe. They'll tell you the same things.
They're like, "I had exactly the same problems with my 'users,' the
people that I build things for." And yeah, there's such a good
translator. And if the communication flows between IT and business
sort of through that portal, things go so much better. That's a
habit. We're still in the process of developing as a world.
Greg Beaumont (00:22:51):
Yeah. And in healthcare that actually also provides some unique
challenges. With regulation and personal health information, these
Excel files have sensitive data in them, and you have to make sure
it's protected and that the right people can see it. And how do you
give them the power to use their skills to improve your
organization, while also making sure that you keep everything safe.
So, I think that's a hot topic these days.
Rob Collie (00:23:15):
Yeah. I mean, it's one of those like a requirement, even of the
Hello World equivalent of anything is that you right off the bat
have to have things like row level security and object level
security in place and sometimes obfuscation. What are some of
the... we don't want to get to shop talky, but it is a really
fascinating topic, what are the handful of go-to techniques for
managing sensitive healthcare information? How do you get good BI,
while at the same time protecting identity and sensitivity. So
often, you still need to be able to uniquely identify patients to
tie them across different systems, can identify them as people.
It's really, really, really tricky stuff.
Greg Beaumont (00:24:02):
And I think just to kind of stress the importance of this, you can
actually go search for look up HIPAA wall of shame or HIPAA
violation list. When this information gets shared with the wrong
people, there's consequences and can result in financial fees and
fines. And in addition to that, you lose the trust of people whose
personal information may have been violated. So, I think a
combination of you said things row level security and object level
security as a start, you can also do data masking, but then there's
issues of people export to Excel. What do they do with that data
afterwards?
Greg Beaumont (00:24:37):
And then there's going to be tools like Microsoft Information
Protection, where when you export sensitive information to Excel,
it attaches an encrypted component. I'm not an MIT expert. I know
how it works. I don't know the actual technology behind it. But it
attaches an encrypted component where only people who are allowed
to see that information can then open that file. So, you're
protecting the information at the source and in transit, but you're
still giving people the flexibility to go build a report or to
potentially use data from different sources, but then have it be
protected every step of the way.
Greg Beaumont (00:25:11):
So like you said, without getting too techie, there's ways to do
it, but it's not just out of the box easy. There's steps you have
to go through, talk to experts, get advice. Whether it's workshops
or proof of concepts, there's different ways that customers can
figure that out.
Rob Collie (00:25:28):
Yeah. So because of that sort of mandatory minimum level of
sensitivity handling and information security, I would expect, now
that we're talking about it, that IT sort of has to be a lot more
involved by default in the healthcare space with the solutions than
IT would necessarily be in other industries. Another way to say it,
it's harder for the business to be 100% in charge of data modeling
in healthcare than it is in other industries.
Greg Beaumont (00:26:02):
Yep. But you can have a hybrid model, which is where the business
provides data that's already been vetted and protected and there
might be other data that doesn't have any sensitive data in it,
where it's game on, supply chain or something like that. But having
these layers in between, the old way of doing things was just
nobody gets access to it. Then there was kind of canned reporting
where everybody gets burst in the reports that contain what they're
allowed to see. But now, you can do things in transit, so that the
end users can still use filters and build a new report and maybe
even share it with other people. And know that whoever they're
sharing with will only be able to see what they're allowed to see.
It gets pretty complex, but it's definitely doable and the
customers that are doing it are finding a lot of value in those
capabilities.
Rob Collie (00:26:48):
That's fundamentally one of the advantages of having a data model.
I was listening to a podcast with Jeffrey Wang from Microsoft and
he was talking about it. And I thought this was a really crisp and
concise summary, which is that the Microsoft Stack Power BI has a
model-centric approach to the world whereas basically, all the
competitors are report centric. And what does that mean? Why does
that even make a difference? Well, when you build a model, you've
essentially built all the reports in a way. You've enabled all of
the reports. You can build many, many, many, many, many like an
infinite number of different reports based on emerging and evolving
business needs without having to go back to square one.
Rob Collie (00:27:28):
In a report-centric model, which is basically what the industry has
almost always had, almost everywhere, outside of a few notable
examples, Power BI being one of them. When a report centric model,
every single change, I remember there being a statistic that was
just jaw dropping. I forget what the actual numbers were, but it
was something like the average number of business days it took to
add a single column to a single existing report. It was like nine
business days, when it should just be a click. And that's the
difference. And so, preserving that benefit of this model centric
approach, while at the same time, still making sure that everyone's
playing within the right sandbox that you can't jump the fence and
end up with something that's inappropriate. Very challenging, but
doable.
Greg Beaumont (00:28:15):
Yep. That reminded me of an old joke we used to tell in consulting
and this was back in the SharePoint Performance Point with Analysis
Services days is there be a budget for a project, there'd be change
requests along the ways, they discover issues with the data. And at
the very end of the project, they rushed the visualization to
market. And they're like after six months, with 10 people dedicated
on this project, "Here's your line chart."
Rob Collie (00:28:39):
Yeah. I had a director of IT at a large insurance company one time,
looking me in the eye and just brutally confess. Yeah, my team, we
spent three months to put a dot on a chart. And that's not what you
want.
Greg Beaumont (00:28:59):
Right, right.
Rob Collie (00:29:01):
That was unspoken. This was bad. To the extent that you're able to
tell, what are some of the interesting things that you've seen in
the healthcare space with this platform recently? Anything that we
can talk about?
Greg Beaumont (00:29:15):
Yeah, so I think I'd start with how everything changed with COVID.
Just because I think people would be interested in that topic and
kind of how it changed everything. I actually had a customer
yesterday at a large provider who said, "COVID was the catalyst for
us to reconsider our investment in analytics, and that it spurred
interest from even an executive level to put more money into
analytics because of the things that happened." So obviously, when
it hit everybody was, "What in the world is going on here?" Right?
"Are we even going to have jobs? Is the whole world going to
collapse or is this just going to be kind of fake news that comes
and goes?" Everybody was unsure what was going on.
Greg Beaumont (00:29:50):
At the same time, the healthcare providers, a lot of them were
moving people to work from home and these were organizations where
they had very strict working conditions because of these data
privacy and data security considerations, and all of a sudden,
you're in a rush to move people home. So, some of my counterparts
who do teams, they have some just amazing stories. They were up all
night helping people set up ways to securely get their employees to
a work-from-home type experience, so that they only had essential
workers interacting with the patients, but then the office workers
were able to effectively conduct business from home.
Greg Beaumont (00:30:25):
Additionally, there were use cases that were amazing. So, Microsoft
has now what's called the Cloud for Health where we're effectively
taking our technology and trying to make it more targeted towards
healthcare customers and their specific needs, because we see the
same types of use cases repeat from customer to customer. One of
those use cases that came out of COVID was called Virtual Visits.
And I actually know the team that built that solution, but because
of patients who were on COVID, they didn't know how contagious it
was.
Greg Beaumont (00:30:56):
There were people being put on ventilators, who weren't allowed to
see their families and they were setting up a team's application,
where people were actually able to talk to their family and see
their family before they went under, right? There were chaplains
who were reading people their last rites using video conferencing,
and things like that. So, it was pretty heavy stuff, but I think
from a healthcare perspective, it showed the value technology can
provide.
Greg Beaumont (00:31:21):
And from our perspective in the field, it's like we're not just out
there talking about bits and bytes. It kind of hit home that
there's real people who are impacted by what we're doing and it
adds another kind of layer of gravity, I'd call it, taking what you
do seriously, right? I had another customer, they were doing some
mapping initiatives with some of the COVID data because they wanted
to provide maps for their employees of where the hotspots were.
Greg Beaumont (00:31:46):
And we were up till I think 11:00 at night one night working
through a proof of concept. And they said, "Yeah, what's next is we
also want to start mapping areas of social unrest." I said, "Wow,
social unrest. Why are you worried about that?" And they said,
"Well, we expect because of this lockdown, that eventually there's
going to be rioting and issues in all different parts of the
world." And at that time, I just kind of didn't really think about
that, but then a lot of those things did happen. It was kind of
just interesting to be working at night and hearing those stories,
and then seeing how everything kind of unfolded.
Greg Beaumont (00:32:18):
Another example, look it up, there's an Azure COVID Health Bot out
there and then there's some information on that, where you can ask
questions and walk through your symptoms, and it will kind of give
you some instructions on what to do. Another one that is even
popular now is looking at employees who are returning to work. So,
when people return to work find out vaccination status, "Are you
able to come back to work? Are you essential? Are you
nonessential?" I don't think a lot of customers were prepared to
run through that scenario when it hit.
Greg Beaumont (00:32:48):
So, having these agile tools where you can go get your list of not
only employees, but maybe partners that refer people to your
network, because you might not have all the referring doctors in
your system. So with Power BI, you can go get extracts, tie it all
together and then build out a solution that helps you get those
things done. I'd say it was eye opening. I think for customers and
also for myself and my peers, that we're not just selling widgets.
We're selling things that make a difference and have that human
perspective to it.
Rob Collie (00:33:20):
Yeah, that does bring it home, doesn't it? That statement from an
organization that COVID was the catalyst, evaluating and investing
in their analytic strategy?
Greg Beaumont (00:33:29):
Yep.
Rob Collie (00:33:30):
Being in BI, being an analytics is one of the best ways to future
proof one's career because at baseline, it's a healthy industry,
there's always value to be created. But then when things get bad,
for some reason, whatever crisis hits, it's actually more necessary
than ever because when you've been in a groove when a an industry
or an organization has been in an operational groove for a long
time, any number of years, eventually, you just sort of start to
intuitively figure it out. There's a roadmap that emerges slowly
over time. Now, even that roadmap probably isn't as good as you
think it is. If you really tested your assumptions, you'd find that
some of them were flawed and analytics could have helped you be a
lot more efficient even then.
Rob Collie (00:34:14):
But regardless, the perception is that we've got a groove, right?
And then when the world completely changes overnight, all of your
roadmaps, your travel roadmaps, none of them are valid anymore. And
now, you need a replacement and you need it fast. And so, what
happens is, is that analytics spending, BI spending, whatever you
want to call it, or activity, actually increases during times of
crisis. So, you got a healthy baseline business. It's an industry
that's not withering and dying in good times, but it actually it's
like a hedge against bad times.
Rob Collie (00:34:47):
When I saw that research years and years ago, when I was working at
Microsoft Corporate, we just come out of the dot-com crack up, we'd
seen that BI spending it across the IT industry was the only sector
that went up during that time where everything else was falling.
It's like, "Oh, okay." So, not only do I enjoy this stuff, but I
really should never get out of it. It's like one of the best future
proofing career moves you can make is the work in this field. And
so, I mean, we've seen it, right? The early days of the COVID
crisis, you're right when no one knew the range of possible
outcomes going forward was incredibly wide. The low end and the
high end were exponentially different from one another.
Rob Collie (00:35:29):
And so, we experienced in our business, sort of a gap in spring and
early summer last year. We weren't really seeing a whole lot of new
clients, people who are willing to forge a brand new relationship.
Again, what happens when a crisis hits? You slam on the brakes. No
unnecessary spending first of all. Let's get all the spending under
control, because we don't know as a company what's going to happen
in the industry, right? You see a lot of vendor spending freezes
and of course, to other companies, we're a vendor, right? So, our
existing clients, though, doubled down on how much they used us and
how much they needed us.
Rob Collie (00:36:08):
And then later in the year, the new client business returned, and
we actually ended up, our business was up last year, despite that
Q2 interruption and sort of making new friends. And this year, holy
cow like whatever was bottled up last year is coming back big time.
And so, yeah. You never really want to be the ghoul that sort of
morbidly goes, "Oh, crisis." From a business perspective, yeah,
anything that changes, anything that disrupts the status quo tends
to lead to an increased focus on the things that we do.
Greg Beaumont (00:36:43):
Yeah, I think something you said there, too, was when you don't
know what's going to happen was when the business intelligence
spending increased. I mean, the intelligence and business
intelligence, it's not just a slogan. The purpose of these tools is
to find out the things you don't know. So when there's uncertainty,
that's when BI can provide that catalyst to sort of add some
clarity to what you're actually dealing with.
Rob Collie (00:37:06):
Yeah, I've been using, even though I'm not a pilot, I've never
learned to fly a plane or anything. I've been using an aviation
metaphor lately, which is windshield is nice and clear. You might
not be looking at the instruments on your cockpit very much, right?
You know there's not a mountain in front of you, you can see how
far away the ground is. And you could sort of intuit your way
along, right? But then suddenly, whoosh clouds. And oh, boy, now,
you really need those instruments, right? You need the dashboards,
you need the altimeter, you need the radar. You need all that stuff
so much more.
Rob Collie (00:37:37):
And so, and our business has kind of always been this. The reason
I've been using this metaphor is really for us, it's like given how
fast we operate, and I think you can appreciate this having come
from a Microsoft partner consulting firm before Microsoft years
ago, our business model, we move so fast with projects. We're not
on that old model with the original budget and the change orders
and all of that. That was all dysfunctional.
Rob Collie (00:38:01):
It was necessary, because of the way software worked back then, but
it was absolutely dysfunctional. It's not the way that you get
customer satisfaction. So, we've committed to the high velocity
model. But that means seeing the future of our business financially
two months in the future is very difficult relative to the old sort
of glacial pace, right? If there's a mountain there, we're going to
have months to turn around it.
Krissy Dyess (00:38:26):
To add a bit to your analogy there, Rob. I am married to a pilot
and I have gone up in the small tiny airplane. And before the
gadgets, there's actually the map. The paper map, right? So, you
had the paper map, which my husband now would hand to me. And he'd
tell me, "Okay, let me know the elevations of different areas to
make sure we're high enough, we're not going to crash into the
mountains."
Krissy Dyess (00:38:47):
What's happened is people just they got used to different ways that
they were doing things. They were forced into these more modern
ways. And I think even now, this wave of seeing this catalyst we
can change and how are other people changing is also driving the
people to seek help from others in terms of getting guidance,
right? Because even though you've had the change, it doesn't
necessarily mean that the changes that you made were 100% the right
way and you can learn so much from others in the community and the
people that are willing to help.
Krissy Dyess (00:39:24):
And I think that's one of the things too, that our company provides
as a partner, we're able to kind of go alongside. We've seen what's
works, what doesn't work, what are some of those pitfalls? What are
those mountains approaching? And we're really able to help guide
others that want to learn and become better.
Rob Collie (00:39:42):
Yeah. I mean, this is us getting just a little bit commercial, but
you can forgive us, right? That high velocity model also exposes us
to a much larger denominator. We see a lot at this business that
accumulates. The example I've given before is and this is just a
really specific techy, so much of this is qualitative, but there's
a quantitative. It's sort of like a hard example of like, "Oh,
yeah, that's right. This pattern that we need here for this food
spoilage inventory problem is exactly the same as this tax
accounting problem we solved over there, right?" As soon as you
realize that you don't need to do all the figuring out development
work, you just skip to the end.
Rob Collie (00:40:22):
And really, most of the stuff that Krissy was talking about, I
think, is actually it's more of the softer stuff. It's more of the
soft wisdom that accumulates over the course of exposure to so many
different industries and so many different projects. That's
actually really one of the reasons why people come to work here is
they want that enrichment.
Greg Beaumont (00:40:38):
Yeah, that makes sense. Because you see all these different
industries and you actually get exposed to customers that are the
best in the business for that type of, whether it be a solution or
whether it be a product or whether it be like a framework for doing
analytics or something like that. So, you get that exposure and you
also get to contribute.
Rob Collie (00:40:55):
Even just speaking for myself, in the early days of this business,
when it was really still just me, I got exposure to so many
business leaders. Business and IT leaders that, especially given
the profile of the people who would take the risk back in 2013, you
had to be some kind of exceptional to be leaning into this
technology with your own personal and professional reputation eight
years ago, right? It was brand new. So, imagine the profile of the
people I was getting exposed to, right? Wow, I learned so much from
those people in terms of leadership, in terms of business. They
were learning data stuff from me, but at the same time, I was
taking notes.
Greg Beaumont (00:41:33):
Everybody was reading your blog, too. I can't count the number of
times I included a reference to one of your articles to help answer
some questions. And it was the first time I was introduced to the
Switch True DAX statement. And then I'd print that.
Rob Collie (00:41:47):
Which-
Greg Beaumont (00:41:48):
Sent that link to many people. "Don't do if statements, do this.
Just read this article."
Rob Collie (00:41:53):
And even that was something that I'd saw someone else doing. And I
was like, "Oh, my God, what is that?" My head exploded like, "Oh."
Yeah, those were interesting days. I think on the Chandu podcast, I
talked about how I was writing about this stuff almost violently,
couldn't help it. It was just like so fast. Two articles a week. I
was doing two a week for years. There was so much to talk about, so
many new discoveries. It was just kind of pouring out in a way.
Krissy Dyess (00:42:24):
Greg, you came in to the role around 2016. And to me 2017 was
really that big year with the monthly releases where Power BI just
became this phenomenon, right? It just kept getting better and
better in terms of capabilities and even the last couple years, all
the attention around security has been huge, especially with the
health and life science space. And last year, with this catalyst to
shift mindsets into other patterns, working patterns using
technology, do you feel like you've seen any kind of significant
shifts just compared to last year or this year?
Greg Beaumont (00:43:05):
Yeah. And so something that burns my ears every time I hear it is
when people call Power BI a data visualization tool. It does that
and it does a great job.
Rob Collie (00:43:11):
I hate that.
Greg Beaumont (00:43:12):
But it's become much more than that. When it launched, it was a
data visualization tool. But if you think about it at that time,
they said, "Well, business users can't understand complex data
models, so you have to do that in analysis services." Then they
kind of ingested analysis services into Power BI and made it more
of a SaaS product where you can scale it. There's Dataflows, the
ETL tool, which is within Power BI, which is an iteration of Power
Query, which has been around since the Excel days. So, now you have
ETL. You have effectively from the old SQL Server world, you have
the SSIS layer, you have the SSAS layer. With paginated reports,
you have the SSRS layer. And you have all these different layers of
the solution now within an easy to use SaaS product.
Greg Beaumont (00:43:55):
So this evolution has been happening, where it's gobbling up these
other products that used to be something that only central IT could
do. And now, we're putting that power by making it easier to use in
the hands of those analysts who really know what they want from the
data. Because if you think about it, the old process was is you go
and you give the IT team your requirements, and they interpret how
to take what you want, and translate it into computer code.
Greg Beaumont (00:44:21):
But now, we're giving those analysts the ability to take their
requirements and go do it themselves. And there's still a very
valid place for central IT because there's so many other things
they can do, but it frees up their time to work on higher valued
projects and I see that continuing with Power BI, right? But like
we're adding AI, ML capabilities and data volumes keep increasing
then capabilities I think will continue to expand it.
Rob Collie (00:44:46):
Greg, I used to really caused a storm when I would go to a
conference that was full of BI professionals. And I would say that
something like, "What percentage of the time of BI project,
traditional BI project was actually spent typing the right code?"
The code that stuck, right? And I would make the claim that it was
less than 1%. So, it's like less than 1% of the time of a project,
right? And everyone would just get so upset at me, right? But I
just didn't understand why it was controversial.
Rob Collie (00:45:19):
Like you describe like yeah, we have these long requirements
meetings in the old model. Interminably long, exhausting, and we'd
write everything down. We'd come up with this gigantic requirements
document that was flawed from the get-go. It was just so painful.
It's like the communication cost was everything and the iteration
and discovery, there wasn't enough time for that. And when I say
that the new way of building these projects is sometimes literally
100 times faster than the old way. Like it sounds like
hyperbole.
Greg Beaumont (00:45:53):
It's not. Yeah.
Rob Collie (00:45:54):
It can be that fast, but you're better off telling people, it's
twice as fast because they'll believe you. If you tell them the
truth, they'd go, "Nah, you're a snake oil salesman. Get out of
here."
Greg Beaumont (00:46:07):
Yeah. And I think the speed of being able to develop, too, it's
going to basically allow these tools to be able to do things that
people didn't even dream of in the past. It's not just going to be
traditional business use cases. I know in healthcare, something
that's a hot topic is genomics, right? Genomics is incredibly
complex then you go beyond Power BI and into Azure at that point,
too and Cloud compute and things like that.
Greg Beaumont (00:46:31):
So, with Genomics, you think about your DNA, right? Your DNA is
basically a long strand of computer code. It is base pairs of
nucleic acids, adenine, thymine, and guanine, cytosine that
effectively form ones and zeros in a really long string.
Rob Collie (00:46:46):
Did you know it effortlessly he named those base pairs? There's
that biology background peeking back out.
Greg Beaumont (00:46:52):
I did have to go look it up before the meeting. I said, "Just in
case this comes up, I need to make sure I pronounce them right,"
so.
Rob Collie (00:46:59):
Well, for those of us who listen to podcasts at 1.5x speed, that is
going to sound super impressive, that string there.
Greg Beaumont (00:47:05):
Yeah. I should call out, too, though that I'm not a genomics
expert, so some of what I'm saying here, I'm paraphrasing and
repeating from people I've talked to who are experts, including
physicians and researchers. So, this long string of code, if you
sequence your entire genome, the file is about 100 gigabytes for
one person, okay? At 100 gigabytes, you can consume that, but if
you want to start comparing hundreds of people and thousands of
people in different patient cohorts, all of a sudden, it gets to be
a lot of information and it gets very complex.
Greg Beaumont (00:47:35):
If you think of that strand of DNA as being like a book with just
two letters that alternate, there's going to be paragraphs and
chapters and things like that, which do different things. So, one
of the physicians I spoke to worked with Children's Cancer. Here's
kind of where the use case comes in. So, you take something breast
cancer where there's BRCA1 or BRCA2, BRCA1, BRCA2 genes where if
you have it, there's a measurable increased probability that you'll
get that type of cancer within a certain age range. There's a lot
of other diseases and cancers, where it might be 30 genes. And
depending on different combinations of those genes, it changes the
risk of getting that specific type of cancer.
Greg Beaumont (00:48:17):
But this physician told me that there are specific children's
cancers, where they know that if they have certain combinations of
genes, that they have a very high probability of getting this
cancer. And when the child actually feel sick and goes to the
doctor, it's already spread and it's too late. So, if you can do
this sequencing, basically run it through machine learning
algorithms, so it will determine the probability, you could
effectively catch it at stage zero. Because these cancers, it's
something that could be related to growth hormones and as you're
growing up, and as you become an adult, you're then no longer at
risk of getting that childhood cancer. So, if they could identify
it early and treated at stage zero, instead of stage 4, it sounds
sci-fi, but the tools are there to do it.
Greg Beaumont (00:49:01):
It just never ceases to amaze me that you watch the news and they
talk about self-driving cars and identifying when a banana is ripe,
and things like that. But it's like, you know what? These same
tools could be out there changing people's lives and making a
measurable difference in the world. I think just especially post
COVID, I'll expect to see a lot more investment in these areas. And
also, interest because I think that might be one of the positives
that comes out of this whole experience.
Rob Collie (00:49:27):
I do think that the sort of the worlds of Medicine and Computer
Science are on a merging course. Let's not call it collision
course. That sounds more dramatic. There is a merging going on.
You're right DNA is biologically encoded instructions by an RNA.
The mRNA vaccine is essentially injecting the source code that your
body then compiles into antibodies. It's crazy and it's new.
There's no two ways about it.
Rob Collie (00:49:56):
mRNA therapies, in general, which of course they were working on
originally as anticancer and sort of just like, "Oh, well, we could
use it for this, too." And there's all kinds of other things too,
right? Gosh, when you go one level up from DNA or some point of
abstraction, you get into protein folding. And whoa, is that...
Greg Beaumont (00:50:15):
Crazy, yeah.
Rob Collie (00:50:16):
... computationally. We're all just waiting for quantum computers,
I think.
Greg Beaumont (00:50:20):
Now, I'll have to call out that I'm making a joke here, so people
don't take me seriously. But if you think about it, the nucleus in
each of your cells contains an important model of that DNA, right?
There isn't just a central repository that everything communicates
with. You have a cache of that DNA in every cell in your body,
except red blood cells, which perform a specific task. There may be
more of the power automated the human body. But cheap attempt at a
joke there, so.
Rob Collie (00:50:44):
Well, I like it, I like it. Let's go in with both feet. I've also
read that one of the reasons why it's difficult to clone adult
animals is because you start off with your original DNA, but then
you're actually making firmware updates to certain sections of the
DNA throughout your life. And so, those edits that are being made
all the time are inappropriate for an embryo.
Greg Beaumont (00:51:09):
Yep.
Rob Collie (00:51:10):
And so, if you clone, you create an embryo, right? And now, it's
got these weird adult things going on in it. That's why things kind
of tend to go sideways. It can all come back to this notion of
biological code and it's fascinating. A little terrifying, too,
when you start to think of it that way. I've listened to some very
scary podcasts about the potential for do-it-yourself bioweapon
development. There was this explosion back, in what, in the '90s
when the virus and worm writers discovered VVA. Remember that? We
call them the script kiddies that would author these viruses that
would spread throughout the computer systems of the world. And a
lot of them, the people writing these things were not very
sophisticated. They weren't world renowned hackers.
Greg Beaumont (00:51:53):
For every instance where you can use this technology to cure
cancer, you're right that there's also the possibility of the
Island of Dr. Moreau, right? You go look up CRISPR Technology,
C-R-I-S-P-R, where they can start splicing together things from
different places and making it viable. And 10 years ago, they had
sheep that were producing spider webs in their milk and it's just,
there's crazy stuff out there if you kind of dive into the dark
depths of Biology. Now that we went down the rabbit hole, how do we
correct course, right?
Rob Collie (00:52:23):
Well, we did go down a rabbit hole, but who cares? That's what we
do.
Greg Beaumont (00:52:26):
Even you kind of step it back up to just kind of easy use cases in
healthcare, so one of the ones that we use as a demo a lot came
from a customer, and this was pre-COVID. But something as simple as
hand washing, you don't think about it much. But when you're in the
hospital, how many of those people are washing their hands
appropriately when they care for you. And there's some white papers
out there, which are showing that basically, there are measurable
amounts of infections that happen in hospitals due to people not
washing their hands appropriately. So, a lot of healthcare
organizations will anonymously kind of observe people periodically
to see who's doing a good job of washing their hands.
Rob Collie (00:53:04):
I was going to ask, how is this data collected?
Greg Beaumont (00:53:06):
This customer actually had nurses who were using a clipboard and
they would write down their notes, fax it somewhere, and then
somebody would enter it into Excel. So, there was this long
process. And with another TS, who covers teams, we basically put a
PLC together in a couple days, where they enter the information
into a power app within teams, so they made their observation,
entered it in. It did a write back straight to an Azure SQL
Database at that time. Now, they might use the data verse. And then
from Azure SQL DB, you can immediately report on it and Power BI.
It even set up alerts, so that if somebody wasn't doing a good job,
you could kind of take care of the situation, rather than wait for
two days for the Excel report to get emailed out, and maybe lower
the infection rates in the hospital.
Greg Beaumont (00:53:53):
So, it saved time from the workers who are writing things down and
faxing things just from a sheer productivity perspective. But it
also hopefully, I don't know if it will be measurable or not, but
you'd have some anticipated increase in quality, because you're
able to address issues faster. And that's the simplest thing ever,
right? You can spend a billion dollars to come up with a new drug
or you can just make sure are people washing their hands.
Rob Collie (00:54:17):
Both data collection and enforcement, they happen to be probably
the same thing. There's like, "Oh, I'm being watched." The
anonymity is gone. That's a fascinating story. Okay. What kinds of
solutions are you seeing these days? What's happening out in the
world that you think is worth talking to the audience about?
Greg Beaumont (00:54:38):
We're seeing this ability to execute better where the tools are
easier to use, you can do things faster, but there's still
challenges that I see frequently out there. So, I know something
that you all are experts in its data modeling and understanding how
to take a business problem and translate it into something that's
going to perform well. So, not only do you get the logic right, but
when somebody pushes a button they don't have to go to lunch and
come back, they get a result quickly. That's still a challenge. And
it's a challenge, because it's not always easy, right? I mean, it's
the reason cubes were created in the first place was because when
you have complex logic and you're going against a relational
database, the query has to happen somewhere, but like that
logic.
Greg Beaumont (00:55:19):
So take for example, if somebody wants to look at year over year
percent change for a metric and they want to be able to slice it by
department, maybe by disease group, maybe by weekend versus
weekday, and then they want to see that trend over time. If you
translate that into a SQL query, it gets really gnarly really fast.
And that problem is still real. One of the trends I'm seeing in the
industry is there's a big push to do everything in DirectQuery
mode, because then you can kind of manage access, manage security,
do all of those necessary security things in one place and have it
exist in one place.
Greg Beaumont (00:56:00):
But when you're sending giant gnarly SQL queries back to relational
databases, even if they're PDWs with multiple nodes, it gets very
expensive from a compute perspective, and kind of when you scale
out to large number of users, concurrency is still an issue. So
that's something where you look at recently what Power BI has come
out with aggregations and composite models. That's some of the
technology that I think can mitigate some of those problems. And
even if we think about something like Azure synapse, right? You can
have your dedicated SQL pools then you can have a materialized
view. A materialized view is effectively a cache of data within
synapse, but then you can also have your caches in Power BI, and
kind of layer everything together in a way that's going to take
that logic and distribute it.
Greg Beaumont (00:56:46):
Does that make sense?
Rob Collie (00:56:47):
It does. I think this is still a current joke. The majority of
cases where we've encountered people who think they want or need
DirectQuery, the majority of them are actually perfect poster
children case studies for when you should use cash and import mode.
Right? It turns out the perceived need for DirectQuery, there is a
real percentage of problems out there for which DirectQuery is the
appropriate solution and it is the best solution. But it's the
number of times people use it is a multiple of that real ideal
number.
Rob Collie (00:57:17):
I think part of it is just familiarity. Still, I've long talked
about how we're still experiencing as an industry the hangover from
most data professionals being storage professionals. Everyone
needed a database, just to make the wheels go round. The first use
of data isn't BI. The first use of data is line of business
applications. Every line of business application needed a database,
right? So, we have minted millions of database professionals. this
is also why I think partly why Power BI gets sort of erroneously
pigeonholed as a visualization tools, because people are used to
that. They're used to, we have a storage layer and reports layer,
that's it, right?
Rob Collie (00:57:56):
Reporting services was Microsoft's runaway successful product in
this space. Paginated reports is still around for good reason. And
I think that if you're a long-term professional in this space with
a long history, even if you're relatively young in the industry,
but you've been working with other platforms, this storage layer
plus visuals layer is just burned in your brain. And this idea of
this like, "Why do you need to import the data? Why do you need a
schedule? Why do you need all this stuff?" It's like as soon as
people hear that they can skip it, and go to DirectQuery, they just
run to the comfort zone in a way, right?
Greg Beaumont (00:58:32):
Yeah.
Rob Collie (00:58:32):
I'm teaching DAX and data modeling to the Excel crowd. I have a
real tortured relationship with the related function. Should I tell
them about it in their first class? Because I know what's going to
happen. They're going to use it and they're going to gravitate
right back to that one giant Franken table model where they use the
relationships and then use the related function to turn them all
into one big wide table and miss the whole point. And so, it's
like, "Do I even tell you about it?" It's like, "Do I even tell the
IT director that DirectQuery is a thing?" Because, again, it has
its purpose. I'm glad it's on the platform, but it's overused.
Greg Beaumont (00:59:09):
I think people confuse single source of truth with a single source
of data.
Rob Collie (00:59:13):
Totally. I've heard people say, "How many copies of the data do I
need in my organization?" Right? In a very folksy combative tone.
Well, you like caches? What about caches? Are you okay with
caches?
Greg Beaumont (00:59:24):
And this is another analogy I sometimes use and it's intended to be
humorous and keep people's attention. I'm not trying to make a
direct comparison here. I just want to call that out. But I call it
the Bitcoin problem. So, with Bitcoin, it can handle I think it's
4.7 transactions per second. And people want to use it as a
currency the way you use a credit card where Visa may be handled
1700 transactions per second. So there's a problem with going
DirectQuery against Bitcoin and that it can't handle the
concurrency and the scale.
Greg Beaumont (00:59:55):
And so, there's a lot of these crypto projects out there that are
trying to create basically ways to kind of resolve all the
transactions and then periodically true up with the source. And I'm
not an expert in that area either. I just, I think it's fun to read
about. During COVID, I watched some things on Bitcoin when I was
stuck at home. I saw a presentation, if anybody gets a chance to
check it out, called The Future Will Be Decentralized by Charles
Hoskinson, who was the founder of Cardano. And that's when it kind
of clicked that they're not just creating fund money, they're
creating effectively a whole new economic system or they're trying
to create a whole new economic system. And some of the technologies
might actually someday replace the Cloud. It's really interesting
stuff that they're doing.
Greg Beaumont (01:00:36):
But kind of circling back to where I started, it's kind of the same
thing with the database. If you just try to run all the logic
directly against the source, you're running massive amounts of
logic for massive numbers of users in parallel. And caching reduces
some of that pressure and it also allows people to have kind of
specialized use cases where you're not doing 20 joins every time
you select a filter. You do it once, and then you filter from those
results.
Rob Collie (01:01:03):
The Vertapak Engine, the end memory column store, all my years at
Microsoft, that was the only thing I was ever close to that felt
like what you would expect from a software company in a movie. This
was science fiction. This technology was developed. It was sci fi
and it was real, and it's still sci-fi today. It's so amazing what
it is capable of. It is mind blowing the performance aspects of
what it can do, and how effortlessly it can perform them.
Rob Collie (01:01:36):
And to leave that out of your implementation like this magic piece
of software, it's impossible what it does. It's still impossible. I
still, I don't even remember how it works anymore. To leave that
out, you're really leaving a lot on the table. And so, let's talk
about what would be some cases where DirectQuery is the right
answer?
Greg Beaumont (01:01:54):
Near real time, so when you need data quickly, and you need it to
be in the hands of the users without anything in between
DirectQuery is absolutely the best use case in that scenario.
There's other solutions. I know you've dug deep with Denny Lee
recently on Big Data, where when there's just massive amounts of
information, you don't want to cache that information. The purpose
of caching is not to go get everything. It's to reduce the
complexity of the logic. So, if you have a gigantic database, and
you need to go get details from it, absolutely DirectQuery is the
best option.
Greg Beaumont (01:02:27):
And just when you kind of hit the technical limit of the caching
within a tool like Power BI, you have to go to DirectQuery. I mean,
there's just a certain point where you get up in the hundreds of
gigabytes for a cube and it's going to perform better on
DirectQuery mode. Just because the technology kind of hits that
limit, where the benefits you get kind of max out and start to
degrade.
Rob Collie (01:02:49):
I worked with Chris Finland, when he was in the field on a project
where we ended up with, at that time, it was 2013 and maybe, it was
2014. Anyways, SSAS tabular and 3 billion records in the biggest
fact table. And this thing was running on 32-gigabyte VM and it was
all loaded in RAM. It was having no trouble at all. And this was
despite it having an incredibly complicated fact record structure,
such that every single fact record in the model, all 3 billion
rows, every single one of them was an inception to date number. Not
what happened to that month, or that day. As of that day, the
number in that database was this was what has happened in this
corner of our business rewinding 50 years. This is the grand total
over time.
Rob Collie (01:03:37):
And so, even to get the current activity in a particular timeframe,
it was a time intelligence measure. The most basic measure in the
entire cube like, "What happened that month? How much revenue came
in that month?" It was time intelligence, right? You had to take
current number and subtract the yesterday number to know what it
was. It was like the lights should be flickering every time someone
touches this thing. It worked great. I was just, it was
stunning.
Greg Beaumont (01:04:05):
So, the one thing I hear where people I work with are going to
strongly disagree with me on this is a lot of people still think
that caching and middle layers are a Band-Aid until the DirectQuery
technology gets better. This is just my personal opinion based on
what I've seen and what I've experienced. I see over time where I
mean, just imagine this scenario, okay? So, you have a solution
that requires row level security and you can have a little note of
compute on your local computer that contains just the rows that
you're allowed to see with kind of a distributed tabular model.
Greg Beaumont (01:04:37):
That doesn't exist today, but it could potentially in the future
versus taking all that data and putting it in one place inside of a
data center somewhere and having everybody communicate. To me, it
just seems like it would be at least something to consider, right?
I'm not an expert in the area, but I don't think that caching and
distribution of kind of the logic is going to go anywhere soon. I
think it's here to stay for some time.
Rob Collie (01:05:00):
And then you've got this technology whose primary purpose is
storage and retrieval. And then you've got this technology that its
primary purpose is analysis. And they're going to make different
tradeoffs. They have to make different tradeoffs. In fact, one of
the reasons why you consider not near real-time, right? Why is near
real-time a good use of DirectQuery? Well, because you can't
rebuild the Vertapak model multiple times a second. That's a
tradeoff, right? It can't be updated at the individual record level
like a regular database can be because it made tradeoffs.
Rob Collie (01:05:33):
I think you could almost mathematically prove that the Vertapak
engine is close to theoretically optimal, in terms of how fast it
is at what it does. You just can't sideline that thing. And it's
not like the storage engines are ever, ever, ever, ever going to
support a mode of DirectQuery that's going to be that fast. So,
yeah, I think that's the way to look at it, right? Is it like you
want to use the magic engine, sometimes you just can't. And you
should be disappointed at those times. And then happy that
DirectQuery is an option, but you should be disappointed that you
weren't able to use the magic thing that's going to make everything
better.
Greg Beaumont (01:06:09):
I'd add it's usually less expensive, too, but usually the cost of
doing it that way is less expensive for the organization and the
query performance is still usually better.
Rob Collie (01:06:19):
Yeah. It's a funny story that when I was working on that solution
with Chris, back in the day. This was a Christ's reaction. I'm
pretty sure that somewhere in the account team, there was a bit of
like, "Oh, really?" When we found out that we were able to, because
this is back in a very different licensing model. The world back
then was very, very different in terms of how Microsoft licensed
their software and it was per CPU per machine, whatever, right? And
the fact that this gigantic model, with the entire financial
history of this Fortune 500 firm, been around forever, was stored
in this one model, and was able to be run on a single 32-gigabyte
VM was a bit of a bummer to the people who are trying to sell
software, right?
Greg Beaumont (01:07:04):
Yeah.
Rob Collie (01:07:05):
It's like this absolute apex predator of a project. We get one VM
of additional footprint, are you kidding me?
Greg Beaumont (01:07:14):
Yep. Unreal.
Rob Collie (01:07:16):
Hey, Microsoft's loss is your gain, customer.
Greg Beaumont (01:07:24):
I do see still challenges with creating those cache layers. You
look at a tool like Aggregations, where it's allowing you to have
hidden summary tables sitting behind your fact tables or alongside
your fact tables. It's, you really have to understand data modeling
to set those up. And you have to understand how it works within the
context of the tool and the context of what people are using, but
if you go look at the roadmap, within Power BI, you'll see auto ags
on the public roadmap, which is the Automatic Generation of
Aggregations based upon query patterns. I'll be interested to see
how that actually looks when it comes out. I don't have access to
anything that's not publicly available. That's out there in the
public.
Greg Beaumont (01:08:03):
And then on the synapse side, materialized views is kind of the
same thing. And you'll also see a roadmap item for the query
accelerator with synapse where it's going to look at the queries.
It's getting from Power BI and then spin up materialized views,
which for all practical purposes, as I mentioned before, another
version of a cache, that will then kind of self-tune the model to
get better over time. And hopefully alleviate some of the need for
people to actually learn how to do it manually. Again, it's moving
from PaaS to SaaS as the other components have.
Rob Collie (01:08:34):
And those sorts of improvements that are in the works. I mean, this
is where some of your colleagues get the idea that we're just sort
of sitting around waiting for the day of DirectQuery parity. There
are developments being made to improve forever, right? We can
always improve. We're going to get to Vertapak level.
Greg Beaumont (01:08:48):
Yeah. I see those demos where they say that NLP will replace the
data analyst. When you're younger, it's like, "Oh, no, I'm going to
be out of a job." Now, it's like, "No, that's I'll be long dead
before that ever happens."
Krissy Dyess (01:08:59):
So, Greg, do you have any hobbies outside of work?
Greg Beaumont (01:09:03):
Yeah, we actually kind of live off the beaten path a little bit.
It's effectively kind of a small, almost a hobby farm. It used to
be a horse ranch, so I spend a lot of time doing stuff in the yard.
And this last weekend, residing my garage. I spend a lot of time
doing family stuff and things like that. When I'm not working, I
don't to be sitting in a desk. It's like I want to go build
something with my hands or I want to go somewhere and do something
and travel that kind of thing.
Krissy Dyess (01:09:32):
Have you always been in Minnesota, too?
Greg Beaumont (01:09:34):
No. So, I actually came from a military family. I think we moved
seven times when I was a kid. Lived all over the country, but we
ended up kind of landing here. And my wife's family is from here,
so I've got roots that aren't going to be severed. We could be here
for some time. Yep.
Krissy Dyess (01:09:50):
Yeah. I kind of have the same thing here in Arizona. I mean, I was
able to move around until again, I found my family here and it does
make it hard to move when you had set your roots.
Greg Beaumont (01:10:00):
Yeah, with things like teams, everything's virtual now.
Krissy Dyess (01:10:03):
Yeah. It's not the same.
Greg Beaumont (01:10:06):
Yeah, yeah, yep.
Rob Collie (01:10:07):
What is it with Minnesota and BI, though? There's something to it,
right? We have more consultants, full-time consultants working for
us from the State of Minnesota than any other state.
Greg Beaumont (01:10:17):
Yep. I know a lot of your employees are from Minnesota, and also a
lot of people I work within Microsoft are from Minnesota in the
data world. I don't know the full answer to that question. I do
know that we have a lot of industries here that are very
data-centric, right? So, you have a lot of device companies. You
have large, probably one of the largest insurance companies that is
based out of Minnesota.
Greg Beaumont (01:10:39):
There's a lot of kind of medical innovation happening in Minnesota
with the Mayo Clinic down in Rochester and University of Minnesota.
And there's also a lot of schools that have very good math
programs, and very good engineering programs, even all the way up
to Fargo and North Dakota. They upgrade engineering schools up
there. So I think there's just kind of a hub of education and
technology and industry that kind of combines to kind of find those
5% that you talk about and give them that opportunity.
Rob Collie (01:11:11):
Yeah. On a per capita basis, Minnesota has got serious game in the
data space. It's tempting to think of it as, "Ah, it's just small
sample size." But I don't think so. I think there is something in
the water or equivalent. Maybe, it's the absolutely brutal winters.
You've got to find something to... it's like, "Where do you go for
football players, we go to the places where it's warm all year.
Florida, Texas, California. What do you go for data people? Well,
you need to go someplace where if you step outside, four-month
window, you just die."
Krissy Dyess (01:11:41):
It's longer than four months.
Greg Beaumont (01:11:44):
It is, yeah. It's probably a five-month winter. The ideal situation
would be able to stay here until New Year's, and then probably come
back in April, right? I have friends and family, they're going to
hate me for saying that because they snowmobile and ice fish and my
neighbors across the street will put up an ice house, out on the
lake. And so, some people actually love-
Rob Collie (01:12:06):
They're building structures on a lake. Let that sink in.
Greg Beaumont (01:12:12):
Yep. I've heard of stories where up on Lake Mille Lacs, you'll
drive a mile or two out on the lake to get to your ice house. And
I've heard stories of people who aren't from here going out to the
ice house and saying, "Where's the lake?" And they're like, "Oh,
the shore is two miles that way."
Rob Collie (01:12:26):
Yeah. That dawning moment of, "Oh, my God. "
Greg Beaumont (01:12:30):
Yep, yep.
Rob Collie (01:12:33):
There were street signs.
Greg Beaumont (01:12:35):
Well, yeah. They actually do have street signs on the lake in the
winter. Some of these were posted.
Rob Collie (01:12:41):
I grew up in Florida. And I thought when I went to school in
Tennessee, I thought, "Oh, my God, is it cold here," right? And
then eventually, I ended up living in Cleveland, I'm like, "Yeah,
this is really cold." And then I took a couple of trips in the
winter, in February, to Minneapolis. Like, "Oh, my God." I couldn't
even keep the ice off of the highways underneath the overpasses. No
amount of salt was going to do it. No, no, this is super frozen.
Whatever that is, I don't know. Yeah.
Greg Beaumont (01:13:10):
I had a co-worker once, who was born and raised in India, had never
left Southern India, and came up here on assignment without ever
having seen snow. And it was below zero when they got off the
plane. So, I mean, you can imagine the shock, because it is
something you have to acclimate to.
Rob Collie (01:13:29):
I can't imagine. I would need, I would need [crosstalk 01:13:31].
And then I remember sitting there going, "Oh, that's right. There's
an entire country north of here. What is wrong with those
people?"
Krissy Dyess (01:13:38):
It's just-
Rob Collie (01:13:41):
It just seems like the absolute northern edge of human expansion,
and then you realize, No, there's a whole industrialized nation up
there.
Greg Beaumont (01:13:49):
Yeah. People think of this as being the arctic tundra, but all of
Canada is basically north of us.
Rob Collie (01:13:54):
My friend, David Gaynor, who is going to be on an upcoming episode
of the show, he grew up in Alberta, you know it's?
Greg Beaumont (01:14:00):
Yeah.
Krissy Dyess (01:14:01):
Every year we go up in Phoenix. We can go up north, just a couple
hours in the Flagstaff in December, maybe January, get a little bit
of snow. And the kids, they come up. Families, they bring their
children to see snow for the first time and they all do it. They
all stick their hands in it. And then that sensation, that burning
sensation starts to kick in, and then two minutes later, they're
all crying, going back into where they came from. And it's just
like every year, you go up and you see these kids for the first
time when they touch the snow, right? Like touch it and then
immediately in tears.
Rob Collie (01:14:35):
Right. Yeah, it's cool. Hi, Greg, so here as we're sort of closing
up, what are some of the things that you see coming, whether
they're new technologies or adoption trends that you think are most
significant or perhaps you also find particularly personally
exciting?
Greg Beaumont (01:14:51):
Yeah, so if we look at some of the new capabilities we've seen in
both Power BI and on the Azure side, there's a lot of focus on
Machine Learning. AI And combining data from different places to
get insights. Something that I think is kind of extremely valuable,
but it's just not as prevalent in demos and presentations and
things like that is the integration between something like Azure
Machine Learning and Power BI, where it's still hard to create a
good machine learning model. You probably want, especially in
healthcare, you want real data scientists creating your machine
learning models. But it used to be really hard to then put that
into practice, right? You might have something that does a great
job of predicting, but then how would an analyst use that data
unless somebody else is just providing it to them.
Greg Beaumont (01:15:38):
Now, you can literally go into Power Query your data flows, and
select a machine learning model that you have access to, and then
take the corresponding columns of data and map them to the inputs
of that machine learning model. Hit go, it will do all the work for
you. You don't have to configure any APIs or write any code. And
then you're getting access to that predictive technology at your
fingertips.
Greg Beaumont (01:16:02):
There's also Auto ML if somebody wants to learn about machine
learning, where you can start building simple machine learning
models right in Power BI. What I found, though, is that by the time
somebody really understands Auto ML, they're usually ready to
graduate to the Azure side of the house. But I see that integration
of, not only being able to get all of this data from all these
different places and tie it together, but then be able to go beyond
doing simple math and using machine learning algorithms is kind of
the next big thing in both healthcare and beyond.
Rob Collie (01:16:36):
That's a fascinating topic. Long time ago, when I was first working
with PowerPivot, I had some friends who had left Microsoft and gone
off and formed a machine learning startup, and some of them are
back at Microsoft now. Really, really, really smart people. And it
was natural for me to try and to collaborate with them and vice
versa at that time. None of the PowerPivot models that I was
building, it turned out none of them had anything interesting to be
found with machine learning.
Rob Collie (01:17:04):
And it was a hard lesson, which was by the time you're done
aggregating, overwhelming majority of Power BI models and reports
operating at an aggregate level, by the time you're done
aggregating, you've kind of lost all of that grain level variation
that is interesting to machine learning. So, I learned at that
moment that a lot of these technologies are meant to operate at,
you can think of it as being like operating at the fact record
level, not on the aggregates.
Rob Collie (01:17:33):
And so, whenever I hear about machine learning and Power BI coming
together, my brain immediately goes back to those old days of, "Oh,
no, these two are incompatible." And that's my first instinctive
response. I have to think about it a little bit longer before I go,
"Okay, there's actually ways they can interplay." And I haven't
tried this thing that you were talking about, but it sounds
amazing.
Rob Collie (01:17:54):
At the Power Query level, you could be importing additional
columns, you're mapping columns that I'm assuming that you would
get back an additional column or multiple columns, with some sort
of predictive score, right? Maybe like the percentage chance that
this customer is going to be leaving. Attrition risk or whatever,
or things of that nature. What's the easiest way to get started
with that stuff?
Greg Beaumont (01:18:18):
I think I'd add two things there. So, the easiest way to get
started is right in Power BI Desktop. If you open Power Query, it's
in the ribbon on the far right hand side, you might have to enable
it. But you could start with cognitive services. And you could just
say, "For each row of data, for this column, tell me what language
that comment was written in?" And you can count how many responses
are in Spanish versus English versus Portuguese or whatever it may
be.
Greg Beaumont (01:18:41):
Another example would be sentiment analysis, right? And this one is
always funny in healthcare, because sentiment analysis is looking
at words and then saying positive, neutral, negative from I think
zero to one. But in healthcare, the words mean different things, so
there was one that came out as being extremely positive. It was
tenderness, right? Because in healthcare, it means you're sore and
you hurt. But outside of healthcare, it's a positive emotional
word, right? Yeah.
Rob Collie (01:19:08):
There's also doctor speak in general, which is like it requires a
completely different sentiment filter. I had a salivary gland tumor
removed recently, which I'm fine now. But if you read the pathology
report on what was going on with me, right? As a human being,
non-trained professional.
Greg Beaumont (01:19:25):
Scary, yep.
Rob Collie (01:19:25):
And you read that, you'd be like, "Oh, man, Rob, you're going to
die." So, I don't... yeah. I wouldn't want to the sort of the
vanilla sentiment analysis looking at that.
Greg Beaumont (01:19:37):
I wanted to add one more use case, too. So, you referred to doing
predictions on aggregations. One use case where that might actually
be applicable is let's just say somebody wants to do a simple
forecast. Right? You can do this right in Auto ML. I'm actually
working on a demo on for it right now. I don't have it ready to go
where the analyst comes in and says, "I want to forecast at the
level of the individual provider by day, by disease category, by
department," something like that. And then you do the forecast and
you find it's not very accurate.
Greg Beaumont (01:20:09):
Well, you can maybe make an aggregation where you roll up the
forecast to the level of by physician by week, rather than day, so
on and so forth. And change the level of granularity. Rerun the
Auto ML test to see how accurate it is. And then you could go back
to your data science team and say, "Maybe we want to do the
predictions at this level of granularity, because that's the
accuracy level that I'm looking for." I agree with you that 99
times out of 100, you want the most granular data for those types
of efforts. There are those scenarios we're kind of...
Rob Collie (01:20:41):
Totally.
Greg Beaumont (01:20:41):
... coming up with summary tables to do the predictions to have
that be more agile. I think it's going to create a lot of
value.
Rob Collie (01:20:47):
I'm mostly just reflecting my frustration from that era. We were
failing to find anything useful. My friends at startup were telling
me, "Yeah, Rob, you just don't understand how this stuff works yet
or you can't aggregate like that." I was still very stubbornly
insisting that "Okay, come on." There's still entities in the
world, for example, like a store. Let's say you're a chain with 500
locations. You have all kinds of interesting attributes at each of
those locations. "Is it a two-story store? Is it the deluxe store?
It's blah, blah, blah, blah, blah. Does it have the pharmacy built
in or not?" But all kinds of these aspects, right?
Rob Collie (01:21:18):
And you would never predict future transactions on a transaction
level? Doesn't make any sense, right? What would you forecast this
store's revenue to be next month, right? So, completely valid
machine learning problem. And so, I'm glad we did circle back to
this because I never had the right kind of data to drive that sort
of analysis, that sort of machine learning analysis. It just didn't
happen to exist in the models I was using at that time. I wouldn't
want people to come away from this going, "Oh, no, you can never.
Machine learning and aggregate level are incompatible." That would
be the wrong conclusion. It was just harder to get to that point
than I had expected. I sort of naively expected it to just like,
"Okay, here we go jump off the page." And it didn't.
Greg Beaumont (01:22:03):
I agree with you. If they could ever find a way to combine
multidimensional compute with predictive technologies that would be
kind of the Holy Grail.
Rob Collie (01:22:12):
Greg, I can't thank you enough. You brought so many really
interesting perspectives. I'm really grateful for the thoughtful
approach that you've taken and I think people are really going to
appreciate this episode. So, many, many, many thanks. Thanks for
being here.
Greg Beaumont (01:22:26):
Yeah. And thanks for the opportunity. And I was listening to the
show even before Krissy reached out. The service you're doing for
the community here is absolutely fantastic. Thank you.
Rob Collie (01:22:35):
Thank you very much. That's really gratifying to hear.
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