Process-driven analytics yields much higher value from an IT investment than focusing on the data alone.

By Jeff Kanel and Tom Uvjagi

Many years ago, we realized that the traditional approach to analytics and data warehousing failed to create requisite business value. We would see efforts focused on loading operational data into a data warehouse, with IT usually attempting to vacuum up as much data as possible.

Who doesn’t want more data, right? I’m often reminded of this joke:

  • “Knock, knock” … “Who’s there?”… “Broken Pencil.”
  • “Broken Pencil, who?”… “Never mind, it’s pointless.”

While “pointless” may be extreme, it turns out that simply loading all your data into a warehouse yields remarkably little value. This is counter-intuitive, especially to IT champions who believe in the intrinsic value of data.

How Data Leads to Business Intelligence

The rub is that data is not intelligence. Intelligence is applying information to improve your business, hence the often-used term “business intelligence.” How does an organization orient to creating intelligence rather than data? Understanding the transformation process may help.

Data must go through several changes to be useful for intelligence purposes:

  1. Data must be cleansed, integrated and conformed: All like data must be comparable apples-to-apples in a single table. Data from across the enterprise must be connected and conforming to definitions established by business stakeholders (not IT).
  2. Data must be enriched for analysis: Enrichment includes categorization, roll-up, interpretation, and consolidation into common values, banding and other enhancements needed for specific analysis. For example, marketing may want to regularly group customers into age groups for channel attribution: 18-25 years, 26-32 years, 33-50 years, 51+ years. This kind of banding should be built into the analytics platform.
  3. Metrics must be formulated and developed: Metrics by their nature cannot be maintained in databases, but must be formulaic and calculated on-the-fly using analytics tools. Examples include: Customer Retention Rate, Rolling 12-Month Sales, Supplier Performance KPI.

These changes share a common thread: To be accurate, they require significant explanation and input from actual users of the information.

An Example of Process-Driven Analytics

At Centric, we also discovered that when a decision-making context is taken into consideration, the benefits achieved from 1, 2 and 3 are amplified and much more easily articulated.

For example, a purchasing organization recently needed analytics to help them negotiate better parts pricing and rebates from their vendors. They initially requested average price and order quantity.

We asked the team: “How are you planning to use part prices during the negotiation process?”

They responded: “We are going to compare prices to other potential vendors, though we also consider volume, on-schedule delivery and component quality.”

This led to a series of additional enrichments and metrics that would never have been realized without considering the context of analysis.

Process-driven analytics yields much higher value from an IT investment than focusing on the data alone.

When organizations target business benefits rather than technology, the platform that emerges will be smaller, but much greater in its contribution to the bottom line.

Which reminds me of another joke:

  • A horse walks into a bar. The bartender says, “Hey.”
  • The horse says, “You read my mind, buddy.”

Ultimately business stakeholders will be delighted because this approach empowers them to drive results from their corner of the business. It’s like we were reading their mind.

Go Further:

Jeff Kanel is Centric’s National Data & Analytics Practice Lead, which includes Business Intelligence and Big Data. He is a business-oriented leader who brings nearly 20 years of industry experience in IT project and team management. Jeff has performed in a variety of implementation roles ranging from hands-on BI implementation activities to strategic BI advisement.

Tom Ujvagi manages Centric’s Business Process Management (BPM) practice with a strong focus on developing tools and methodologies that can be leveraged across multiple companies and program/projects. Tom has served in numerous roles over his nearly 20-year career and has always maintained a focus on continuous improvement, whether through process, technology, or the convergence of the two. 

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