Data Governance Litmus Test: Know goals behind KPI’s

This is the third blog post in a series of blog posts geared towards addressing “Why, What and How?” of getting executive sponsorship for data governance initiatives. In my last post Data Governance Litmus Test: Know thy KPIs I explored importance of knowing KPIs to be able to build link between data governance initiatives outcomes and the organizational strategy. In this post I’m going to explore why it is important to know specific goals of the KPIs which are monitored on periodic basis by executives towards fulfilling organizational strategy.

Data governance initiatives typically will span multiple organizations, key business processes, heterogeneous systems/applications and several people from different lines of businesses. Any time when one is dealing with such a complex composition of players and stakeholders, it is extremely crucial to be articulate about business goals and the impact of the actions on hand on the goals. Once people understand the magnitude of impact, and how they will be responsible for such an impact, getting their co-operation, alignment becomes relatively easy.

Once you understand the KPIs which are important organizationally, you need to drill down one level below to understand what specific goals are important? The process of understanding specific goals will undoubtedly reveal many contributing factors to the fulfillment of the overall goals.

For example:

If one of the major KPIs which executives are tracking is overall spend. At this stage it is important for the data governance initiative team to understand specific goals around this KPI. For example the specifics goals around this KPI could be:

1.     Chief procurement officer has been asked to reduce spend by 2% within four quarters

2.     2% reduction across the board represents $80 million savings.

3.     This savings alone would allow organization to improve its profitability by almost a penny per share. This ultimately will reflect positively in share price improvement and will benefit all the employees of the organization.

Once such details are known, establishing a dialogue with chief procurement officer and his/her key advisers might further reveal that

1.     Their focus is going to be in three specific areas (specific products/raw materials)

2.     Not having singular view of suppliers is a key concern. Because of this issue they are not able to negotiate consistent pricing contracts with the suppliers. They believe that streamlining contracts based on overall spend with suppliers; their subsidiaries will help them achieve more than 70% of their goal.

3.     Supplier contracts are not being returned consistently resulting in higher costs in terms of minimum business guarantees and price point guarantees.

Equipped with this information, it will be much easier for data governance team to highlight and link their efforts to overall goal of reducing spend. For example, with some of this information gathered, one can already pinpoint that teams which are working with suppliers/supplier development, contract negotiations, pricing etc…. are going to be critical to get on board data governance with this initiative. Also, it is clear from these nuggets of information that the overall spend, number of suppliers, number of materials/products being procured will be some of the key metrics and interrelationship between those metrics will be critical to link any ROI from initiatives to clean supplier data, build supplier MDM etc…

With this information data governance team now can not only communicate to their team members but also the executives, that X percent of duplicate data in supplier master would potentially represent Y dollars off excessive spend. Data governance team will be able to explain not only how this can be fixed but what is required to maintain this hygiene on an ongoing basis because of the impact it will have on overall excess spend.

In summary, it is really important to understand the goals behind “what?” of the organizational strategy. Other indirect benefits of this kind of exercise are

1.     Establish communication and contacts with the business stakeholders.

2.     Understand areas where you can focus upfront for the highest impact.

3.     Understand and learn the language which you could use to effectively communicate ROI of data governance back to the executives.

In my next post, I will explore who is behind putting together these KPIs for executive in the current situation. These people are ‘the most critical’ players in the Data Governance team at both execution and implementation levels as the initiatives are kicked off.

Previous posts on this or related topics:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Suggested next posts:

Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

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Purpose Built Analytic Applications

There were many predictions in the Software industry for 2010. One of Industry thought leaders Nenshad Bardoliwalla  had his predictions in the area of “Trends in Analytics, BI and Performance Management.” His predictions about how vendors will have packaged/strategy driven execution applications, slice and dice capabilities from the BI vendors returning to its decision centric roots and advance visualization capabilities got me thinking about my favorite topic about purpose built Applications.

What is a purpose driven/built analytic application (PDAA) after all? It is an analytic application which addresses a much focused business area or process and provides insight into the opportunities (for improvements), challenges (performance). In order for such analytic application to provide insight…

1. It needs to be designed for a specific purpose (or problem in mind), and that purpose or focus really needs to be narrow (to be able to provide holistic insight)
2. It needs to rely on purpose built visualization and needs to use Web 2.0 style technologies to make analytic insight pervasive (some examples to follow)
3. It needs to provide descriptive, prescriptive and predictive capabilities to provide holistic insight
   1. Descriptive capabilities will provide view into state of current affairs
   2. Prescriptive capabilities will provide what users need to focus on as a follow up, it also helps in guiding users as to what questions they should ask next to build the holistic insight
   3. Predictive capabilities will facilitate what if analysis and provide insight into what situation business might expect should the current situation continue.
   4. It implicitly provides users with what questions users should ask in a given situation and provides either complete answers or data points leading up to those answers…

Many a times, because of the very specific purpose and narrow focus, most of the insights provided by purpose built analytic applications can be manifested right into the operational application via purpose built gadgets or even purpose built controls. Single dashboard with a interactivity around the widgets/gadgets in the dashboard will typically provide complete insight into the focus/purpose of the analytic application.

Let us discuss an example of what a purpose built analytic application could be…Every organization which has sales force actively selling products/services of the organization has a weekly call to review the pipeline. This is typically done region by region basis and the data is then rolled up at a global level.  A purpose driven analytic application in this situation would be “Weekly Pipeline Review” application. In this application rather than providing free form slicing dicing/reporting capabilities around pipeline data (which will be traditional way), this type of application will focus on:

1. Current Pipeline
2. Changes to the pipeline from last week (positive, negative: As this is what is really watched closed in this call to make sure forecast numbers can be achieved)
3. Indicate impact of the changes to the pipeline on achieving goals/forecast. Based on these changes, extrapolate the impact on Sales Organizations plans…. (what-if)
4. Provide visibility into deals which might be problematic based on the past performance and heuristics (this is what I call prescriptive)
5. Provide visibility into deals which are likely to move faster and close faster, again based on past performance. (Again prescriptive)
6. Provide account names in which incremental up sell can be done (again based on past performance in similar accounts) but there are no active deals/opportunities etc…
7. Provide visibility into individuals and regions which are at risk of missing their forecast based on their past and current performance.

There are different visualizations which can be used to build such type of application. Focus of this analytic application is to help Sales VP’s and Sales Operations to get through weekly pipeline review call quickly by focusing on exceptions (both on the positive and negative side) and provide full insight into the impact of the changes, areas which they should focus into etc…. Hopefully this explains in detail the difference between purposes built analytic application vs. traditional data warehouse or traditional analytic application.

Let us now briefly look at how purpose built UI supports some of the important aspects (holistic insight) of the purpose built applications. Many of you have used Google portal and have uploaded iGoogle gadgets. One can look at iGoogle gadgets as purpose built applications which focus on one specific area of interest to you.  Take a look at one of the samples put together by Pallav Nadhani to demonstrate Fusion charts visualizations. This gadget is a perfect example of how purpose built UI helps in creating the focus and holistic insight of the analytic Application. This gadget provides complete Weather picture for a location for today or for future.

 There is a company out of New Zealand, Sonar6 which provides product solution around performance management (much focused, purpose driven)/Talent Management. They have done fantastic job of building purpose built application and delivered that application through purpose built UI. I especially like the way they have provided analytic and reporting capabilities (helicopter view) around performance management. You can register for their demo or can look at their brochure/PowerPoint presentations.

There are several other vendors who have made purpose built analytics pervasive in our day to day lives. Recommendation engine built by Amazon is a perfect example of “Purpose built Analytics”

In the end, I truly believe that purpose built analytic applications can and will maximize the value/insight delivered to the end users/customers while keeping the focus of the analytics narrow.

I would love to know your thought around purpose built applications. What has been your experience?

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Data Integration Strategy For Cloud and On Premise Data

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