Building an Analytics Ecosystem to help the business and change the image of IT...

I recently attended a large family holiday gathering and as per usual, those relegated to the “kids’ table” joked about forever carrying the image of “kids” despite all of them being adults.  It reminded me of something I’ve previously mentioned where some CIOs struggle for a seat at the executive table, seeming to forever carry the image of “techies” rather than business leaders.  I actually think it might be easier to alter the “techie” image than it is to graduate from the kids’ to the adults’ table because despite having my own children, I still sometimes find myself at the kids’ table. Yet, I’ve helped several CIOs move from the “techie” to the business leader table.

In addition to the ideas mentioned in that previous blog article, I think working with business leaders to create an analytics ecosystem and roadmap is an excellent way for CIOs to develop their business leader street creds.  To get started, if you don’t have a working knowledge of the various analytics categories, I recommend reading a book such as, “Big Data Analytics Infrastructure for Dummies.”

There are four basic types of analytics, each appropriate for different business uses and each using different tool sets.  Building an effective ecosystem and realistic roadmap requires understanding these business uses.

Four Basic Types of Analytics

1. Descriptive:

•       Used to provide basic statistics such as averages, totals, frequencies, causal relationship

•       Probably the most commonly used type of analytics done today

2. Predictive:

•       Helps see what the future may bring by using statistical models

•       Helps forecasts future revenue, profits, or operational outcome by modeling relationships between variables

•       Mostly used for planning

3. Prescriptive:

•       Optimizes predictive analytics scenarios for the best future outcome

•       Considers new inputs or constraints specific to a given situation

•       Recommends actions

•       Used for tactical planning

4. Cognitive:

•       Uses techniques and a high-performance infrastructure to identify non-intuitive relationships

•       Typically analyzes diverse sets of data

•       Often used for break-through ideas

Regardless of the type of analysis, the speed to gaining insight from data is the key to business impact.  It can mean the difference between leading and following in an industry.  Therefore, don't underestimate the business value of having systems, storage and databases that work well together as a team.  However, let's not dive into important nuances of technical solutions but return to discussing the business.

Most C-level executives have multiple analytics requirements.  For example Marketing typically wants analytics to help with client segmentation, understanding client sentiment and reducing client churn.  Finance wants help with planning and forecasting, automating financial and management reporting and improving visibility, insight and control.  Meanwhile, Risk needs analytics to improve risk-awareness in decision-making, manage financial and operational risks and reduce compliance costs.  And Operations might use analytics to optimize the supply chain, deploy predictive maintenance processes and improve fraud identification processes.

  

Often those executives will each buy niche solutions to help them answer their specific questions.  The result is a disconnected set of tools, capable of addressing a myriad of pin-point business questions but that yields sub-optimal business insight because the toolset fails to connect insights across business units. For example, analytical insights in risk management can and should improve insights in financial management, marketing and operations and vice versa.  But unless those analytics solutions were architected and designed for integration, chances are low that they will work well together.

The CIO can provide tremendous business value in this area because the CIO sees across all parts of the business and sees potential integration points between different groups.  The CIO probably is in the best position to gather the full spectrum of analytics requirements, help prioritize them and order their implementation schedules according to business impact, and then build an integrated analytics ecosystem to manage, integrate, govern and analyze data in the most effective way for the business.  This feat alone often earns a CIO the right to move from the techie to the business table. 

If you’d like help planning your approach to building an integrated analytics ecosystem, feel free to contact me.  Unfortunately, if you’re looking for help strategizing how to move from the little kids’ to the adults’ table at your next family holiday gathering, I’m probably not going to be much help.  I’m still trying to figure that one out myself.