I tell clients and students that technology can do whatever
you want…if you know what you want.
While knowing what you want – the questions you want answered – is the
first and most important step, preparing the information to answer those
questions is 90% of the work. The good
news is that you don’t have to “boil the ocean” to get started. Like building anything of quality, the
architect must know what it’s going to be when it’s finished, so everything
developed and delivered builds on what has already been developed to further
leverage the Analytics investment.
It’s really logical when the process is considered:
- Decisive action is enabled by questions answered – the right information at the right time.
- Questions are answered by business people using Analytics tools
- Analytic tools require organized information that is current, consistent and accurate.
- Organized information is develop by integrating data from inside and outside the organization.
Let’s expand on this simple process with a little reality. In most organizations, business users don’t
drive Analytics initiatives, so reporting projects become long and expensive
because the technology team may not have a good understanding of the business questions
that need to be answered, so they work to answer every possible question, i.e.,
“boil the ocean.” In the process:
- The business user communities don’t have control or much participation in the process, so they don’t feel ownership of the Analytics deliverables. “It’s another IT initiative.”
- Report requests must be submitted to the IT group, where a report development backlog grows, making business users frustrated with IT, so they continue using spreadsheet reports with inconsistent data definitions and calculations.
- Information (i.e., reports and analytic deliverables) are “pushed” to business users who don’t understand the information or how to access the data to develop answers to their real questions.
- Management receives untimely (i.e., monthly) historical reports with marginal information, allowing opportunities to pass and problems to grow.
- Calculations and terms are not defined consistently across all business units causing confusion and arguments in management meetings about which report is right.
- Incomplete information from a single application data source provides possibly misleading customer and product segmentation and behavior profiles, which leads to inaccurate analyses and bad decisions.
- …and the list goes on.
Providing business users
with immediate access to relevant, accurate, consistent business information is
the requirement. While Analytics Initiatives
are guided by a continuous stream of increasingly insightful questions from
across the organization that build on answers to previous questions, the foundation
performance analytics architecture must be planned and in place to maximize
an organization’s return on intellectual assets. The architecture includes:
1.
Enterprise
Data Warehouse Architecture
In a perfect world, this is an enterprise data warehouse (EDW)
architecture with all of the key components. In reality, there are a lot of “variations on a theme.” It doesn’t happen at once, but having the
vision of the model will lead to making fewer mistakes.
Automating and iteratively tuning the model and processes as
analytic requirements evolve provides a high return on investment.
2.
Extract,
Transfer and Load (ETL) Processes
Extracting the application system data required to answer
questions, and then transferring it to a staging database, and then
converting/standardizing the various data types and definitions as it is
consolidated and loaded into the EDW and then on to the functional data marts
is a complex, but necessary process to establish a single source for enterprise
analytics. An understanding of the
desired data model and data types makes the process a lot easier. Automating
the process makes it fast, invisible and cost effective. This is a good thing.
3.
Data
Governance
The most challenging part of the process is getting lines of
business and strategic business units to 1) share their data and 2) agree on a
common dictionary of terms and calculations. This is why all successful
Analytics Programs are sponsored by senior executives.
4.
Data
Marts
Functional data marts provide the control, security and
perspective to maximize business user information access and productivity. Access to everything, when security isn’t an
issued, will be overwhelming to business users, who need to focus on their
business objectives, rather than everything that is going on in the world. Defining the data requirements and automating
the ETL update processes makes data marts a very productive platform.
5.
Security
Security profiles make business user self-service
possible. When individual and team
performance scorecards are employed, secure access to performance dashboards
that are updated daily is an incredibly productive consideration. Do it!
6.
Analytics
Tools
“How do I love thee?
Let me count the ways!” Sorry for
waxing poetic a little, but I’ve been lovin’ reporting and analytic tools for
many years…and I still do. They are the
answer to my questions…and yours. I used
Pilot Software, IBI Focus and IRI Express when I started in 1990. I’ve been a Cognos Partner since 1995, a
Microsoft Partner since 2001, recently designed and developed a Business
Objects-based enterprise reporting platform for a $13B global technology
client, and taught Visual Analytics at a large New England University,
featuring Tableau and QlikView.
The good news is that they have become a commodity – they can
all, basically, do the same thing. There
are new ‘data discovery’ tools, like QlikView, Tableau and others, who have
very good visual capabilities, but it all goes back to: “What do we want to
know?”
It’s interesting that the top 3 vendors in Gartner’s 2014 Business
Intelligence and Analytics Magic Quadrant are Microsoft, Tableau and QlikView,
the latter two being relative newcomers to the game. But they’re all good, depending on the
questions that need to be answered. The
really interesting thing is that the Microsoft BI platform is not being
marketed as a business user platform…when it’s an excellent and very
cost-effective reporting and analytics capability. More on this another time…
7.
Analytics
Library
Redundant report development is more of a problem than using
spreadsheets. While trying to answer the
same question, but using variations in data and presentation graphics, most
organizations have many reports that answer the same question. An Analytics Library helps organizations
improve productivity and save time and expense by not recreating the wheel
(i.e., developing a new report to answer the same question).
8.
Business
User Self Service
This is where the rubber meets the road. Reporting and analytics is not an IT
initiative – it’s a business initiative that empowers everyone in the
organization to make the best possible decisions regarding customer, product
and employee processes…which result in optimized quality, productivity and
expense management outcomes. Providing
business users to access information that provides them with a context to
understand how each of their jobs contributes to the success of the
organization is HUGE!
~
This is a journey with a changing destination. As dynamic business requirements change, so
do Analytic requirements. Using an
architecture that anticipates and accommodates these changes will save a lot of
time and expense, while increasing Organization IQ and shareholder value.
Think about it.
What questions do you have?
