The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that integrates your learning across all courses in the specialization. In response to business requirements presented in a case study, you’ll design and build a small data warehouse, create data integration workflows to refresh the warehouse, write SQL statements to support analytical and summary query requirements, and use the MicroStrategy business intelligence platform to create dashboards and visualizations. In the first part of the capstone course, you’ll be introduced to a medium-sized firm, learning about their data warehouse and business intelligence requirements and existing data sources. You’ll first architect a warehouse schema and dimensional model for a small data warehouse. You’ll then create data integration workflows using Pentaho Data Integration to refresh your data warehouse. Next, you’ll write SQL statements for analytical query requirements and create materialized views to support summary data management. Finally, you will use MicroStrategy OLAP capabilities to gain insights into your data warehouse. In the completed project, you’ll have built a small data warehouse containing a schema design, data integration workflows, analytical queries, materialized views, dashboards and visualizations that you’ll be proud to show to your current and prospective employers.
Executive Interview with Matthew Caton of Data Source Consulting
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Design and Build a Data Warehouse for Business Intelligence Implementation
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Курс 5 из 5 — Specialization Data Warehousing for Business Intelligence
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Data Visualization and Dashboard Design Requirements
Module 5 presents the data visualization and dashboard design requirements for the fourth part of the case study.
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Michael ManninoAssociate Professor
Business School, University of Colorado Denver
Jahangir KarimiProfessor
Information Systems University of Colorado Denver
Welcome to a supplemental executive interview from module five.
This interview provides a context for the entire capstone course.
You will learn about consulting practices from a member of a consulting firm
specializing in business intelligence and enterprise data management engagements.
Let me welcome Matt Caton, BI and
analytics lead at Data Source Consulting in Denver, Colorado, USA.
Data Source Consulting focuses on strategic and
implementation services with expertise in data architecture, data integration,
data quality, data governance, analytics, and master data management.
The firm is experienced in a wide range of industries, including healthcare,
manufacturing, retail energy, financial services, utilities, and government.
Matt, please tell the learners a little bit about your background,
your business background, and your experience at Data Source Consulting.
>> Thanks, Michael.
I am BI and analytics practice lead,
and I've been in the consulting business now for about 15 years.
I got my undergraduate degree from Indiana University's Kelly School of Business,
where I specialized in computer information systems.
Back in 2000, I graduated, and
I started to get some experience in consulting within business intelligence.
Early on as a data tester, doing a lot of data analytics,
working for a consulting company named Brown Consulting at the time, and then
really started to get my feet wet with report development, enterprise reporting,
using enterprise grade tools like Business Objects and Cognos.
It really came up as a report developer gathering requirements and
building reports for our clients, basically as a developer,
really getting into programming and customizing front end tools for
our clients and our consumers.
2004 to 2006, I went back for an MBA at CU Boulder.
Really wanted to get into an entrepreneurship and
learn about maybe starting a business on my own.
Came out of school and did a little bit of independent contracting.
And then I took a project with the managing partner of Data Source,
Steve Dine.
And we put together from the ground up a Greenfield data warehouse for
our clients, for our client at the time, a large merchant processing firm.
And he did a great job really stacking the deck with a very strong data architect,
very strong ETL developers.
And it was just an excellent experience to really see how valuable it was for
our client at the time to get this warehouse and
to really realize the return on that investment.
We were able to, about three months after delivery there, able to spot
some of their customers that they were spending a lot of money on that were
not producing for them, so they were able to really turn the corner.
And so that was such a great experience that Steve asked me to join the company,
and Steve being the first employee, I was actually the second employee to the firm.
And so, fast forward 2006, 2015, I went
through a lot of BI architecture, a lot of BI administration,
server set administration, standing up multi-server architectures for
our clients with enterprise grade BI tools.
That's really the front end of data warehouses that we had put together.
Our core competency kind of out of the gate was within ETL and data integration.
We've noticed just such a need throughout the marketplace for companies really to
bring data together to conform that data to put business rules around that data.
And really one of the main challenges we continue to see
is around departmentalized solutions,
a lot of siloed solutions within different departments and
different departments having different definitions for different data.
So for the same terms,
people are coming up with different answers to the same questions.
So the whole goal behind what we do within enterprise data management is that
conformity and really putting that metadata management,
that master data management, in the hands of the business.
So they can really start to master their data and conform that and
come up with the same answers to the same questions.
And there's really endless needs through out the market with mergers and
acquisitions, and just as companies get bigger and
bigger, it just continues to be a challenge.
So we've continued to grow and really blossomed from coming in and
doing tactical solutions to coming in and
helping at a higher level within trying to really promote the I
programs to get them really into that enterprise data management space and
really start to master the data that they collect as there are just so
many different sources available.
>> Let's focus on sort of a typical consulting engagement involving business
intelligence and maybe perhaps data integration.
What types of skills does your team bring to a client, and
what types of skills did you think are in more short supply?
>> One of the main skills, and
it's a fairly basic skill if you studied business intelligence and
data modelling and data architecture, but from a relational perspective, one of
those main skill sets is just having the ability to write SQL, to understand SQL.
Structured Query Language is really at the core of what
most of our developers have to be able to do.
And whether that developer is an ETL developer, or a BI front end developer, or
a data modeller, or a data architect,
it's really the language we speak when it comes to relational data warehousing.
So that is very fundamental.
Another real key skill in the consulting business is that likeability,
it's that skill of walking into a new engagement and
presenting yourself and bringing yourself down.
Not down to the business' level, but
being able to speak their language and not throw a bunch of acronyms around.
You just really have to be able to talk that same language.
And so you gotta be able to learn very quickly and learn about new businesses and
new environments very quickly, so that you can understand where those
challenges reside, and then you can help pinpoint problems.
So there's a softer side of consulting that is often overlooked that we find,
and we push our consultants to really focus on that human aspect to get
to know their users and to understand what their needs are.
And we found that the closer you get to your business community,
to the end users of that system, the more that you work in conjunction with those
folks, the more success that you're going to have.
And the solution is always going to be a lot
more well received when it's something that you've built as a team together.
And it kind of goes back to
the whole agile methodology that's really taken shape in the last five to ten years
in that you want to deliver solutions a little bit faster, so
that people aren't waiting at the end for a big bang solution.
You want to produce results very quickly and
do that litmus test and see how well received it is,
if there's any issues, so it allows you to get in front of problems a lot quicker.
>> Thanks for talking about a typical engagement.
In the skills your team brings to an engagement,
can you provide some lessons, especially
lessons to learners who might be interested in getting into BI consulting?
>> Indeed, as a consultant, you're really brought in as that expert,
so you really have to master your trade.
You have to have a good understanding of the marketplace,
the tools that are available in such a fast-moving arena there.
If you take a look and you do a little research on enterprise reporting tools and
BI analytics and applications, you'll find there is just so many to pick from.
You kind of bubble that up, and
you noticed that at the top of the food chain, there's really four or five main
players in what I like to call the traditional enterprise reporting space.
And those are tools like IBM, COGNOS, and Business Objects, and MicroStrategy,
Information Builders, tools that really require a pretty large server
side footprint in that when you put those infrastructures in place,
they demand a data based server back end, a web based server for the presentation
layer, and then an application server that does a lot of the processing.
So that's really the class of tools that a lot of the bigger
corporations gravitate to because they can serve such a large community of folks.
Over the last probably five to ten years, another suite of tools that's been
very prevalent and really making a splash are the advanced analytic tools,
like the Tableau's and the Qlik views, the ones that are a lot quicker to stand up.
And they allow folks to just immediately get into their data and ask questions and
get results.
So, when you really differentiate those tools to tool sets,
one major challenge that you find is that the newer class of tools,
it causes a lot of disruption.
When you look into what a lot of folks within the financing arena, accounting
arena, marketing, a lot of folks that are very savvy with data and numbers,
they really gravitate toward Microsoft Excel because it's just so easy to use.
But that in itself presents a number of challenges when you're trying to master
data, and data definitions, and conformity, and
get away from spreadmarts and those solutions,
where business rules get captured in these spreadsheets, and they don't get shared.
So, not to say that you shouldn't use a tool like Tableau,
but you need to really be very cautious about how you present it to folks,
and you've gotta put a lot of governance around a tool like Tableau,
if you really want to realize the benefits.
So you want to enable
consumption and you want to get people to really get their hands dirty, but
you also don't want to continue to have those department alias solutions.
And the best way to get in front of that is with data governance and
IPI program strategy, that it brings everybody together on a common page.
>> I want to thank Matt Caton for
sharing his consulting experiences at Data Source Consulting.
You can find some brief supplemental slides about this interview in
the class website.
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