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.

Small Things about Big Data

Common questions that bother most IT decision makers today are:

• Do I need a Big Data solution?
• How will my organization benefit from it?
• Should I implement one now or wait for a more opportune time?
• If I implement one, what is the scale I should look at?

Before answering these questions, it might be helpful to understand some “Small things about Big Data.”

An obvious requirement of any IT solution is the ability to provide reports on a regular basis. These reports represent anything from financial health, project status, and customer data to audit & security related information.  

Most times, organizations tend to generate reports during low peak periods to ensure their infrastructure is highly utilized and gives better ROI.  However, some of these reports if available at more regular intervals in a 24 hour period will benefit the organization much more than just generating them once in a 24 hour period.  This is nothing but Analytics.

Any workload that analyzes static or streaming data to produce reports useful for business & IT are essentially called “Analytic Workloads.”  

As evident from this case study at Coca Cola, timely delivery of reports can make a huge difference to an organization’s business and strategy.

http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=AB&infotype=PM&appname=STGE_TS_ZU_USEN&htmlfid=TSC03243USEN&attachment=TSC03243USEN.PDF

               

Therefore, for organizations to understand if they need to ride the Big Data wave or not, they first should identify the analytical pieces that form their IT backbone.  Important questions here would be:

1. What are the critical reports generated?
2. When are these reports generated? (time of day)
3. Are they based upon accurate and timely data?
4. If the information from these reports can be generated at a higher frequency or on-demand, will it enable quicker decision making or faster go-to market strategy?
5. If the answer to 4 is, “Yes” for certain reports, what stops the organization from generating these reports at a higher frequency? 

If the answer to question 3 is “No,” the first step is to address data quality with the business.  This gets into data structure and data governance.  It also requires cooperation with the business as the data owners.

Moving down the list, most often, the organizations I work with say the answer to question 5 lies in availability of IT resources needed to generate these reports.   A common reason to generate reports in the night (or during low peak window) is to avoid performance issues during peak load and to use resources effectively.

To address this, I see the need for a thought process shift to view analytics as an investment that can help bring higher efficiency to an organization’s business, thereby staying ahead of the competition.  Business cases that are developed around the opportunities these insights will bring to the business tend to be the most successful ones.  They justify investing in the right tools to facilitate this versus trying to limp along with analytics workloads taking second priority over other workloads.  

From the standpoint of balancing optimization of resources with business value potential, my experience has been the best way to get better and timely analytical reports is to create a separate set of analytics workloads that can work in real-time using faster systems & storage.   Compute and Storage systems have matured in terms of performance to an extent where one can run real-time reports without impacting the performance of online workloads.  And if the data analyzed contain untapped insights, the business value usually justifies using this approach.

Having identified analytics workloads and the need, the next step is to understand “Why Big Data?”  For any organization that benefits from real-time data sources like social media, complaint records, and service requests etc., Big Data is the way to go. These data sources helps organizations arrive at quick decisions and react quickly to the market or customer needs.

A common example is a customer recording their experience in a public domain which can either help or harm an organization’s reputation. In such cases, when real-time data are made available to the customer service team, they can quickly step-in, understand the situation and help manage the expectation with the respective customer.   However, there are many more.  Customers use these insights to target marketing to customers more effectively, identify patent infringements, identify trademark misuse, improve product design, gauge overall product and brand sentiment, etc…  There are countless strong business uses.

The CIO can initiate the Big Data discussion with the business in business terms.  Often this is a way for the CIO to earn a “seat at the table” with the other business executives.  But, it’s good to be ready by thinking about some practical “small things about big data” before you do.

Article by Subramaniam Meenakshisundaram, a guest contributor and IBM Executive Consultant in the Systems Group Lab Services organization. 

Many thanks Subbu for your guest column!

When do you need better IT Governance?

It’s rare to find an IT organization with more people and funding than they have things to do.  Usually it’s the opposite…far more things to do than time or money.  Numerous individuals and groups have great ideas that involve IT, creating an extensive backlog of projects and tasks which aren’t always prioritized across the organization.  Add to that people experiencing unplanned technical difficulties seeking IT’s help plus executives who expect any of their bright ideas to receive IT’s immediate attention and the IT department can be a very busy, intense place to work.  Sometimes despite all of IT’s hard work and personal heroics, business leaders still see IT as more of an impediment than partner to the business.  

Lots of things can cause this but the governance model frequently is a major one.  

Governance is about establishing a common understanding of

1.       Who has authority to make what decisions or undertake what actions

2.       Who must be consulted before making a decision or taking an action

3.       How are decisions made

4.       Who needs to be informed of decisions

5.       Who is accountable

6.       How are conflicts resolved

How do you know if your governance model is a culprit?   

Look for the following symptoms:

1.       Collisions occur frequently.  The colloquialism is “people are stepping on each other’s toes."  Multiple people think they have the authority to decide or do things, resulting in contention or redundant efforts.  Few companies and employees can afford duplication of effort.  And, quite frankly, many people get a bit ticked off if they are working on something only to discover someone else is already doing it or has already done it.  The result?  In addition to people wasting their time, often it's accompanied by a little workplace drama.

2.       Circular ownership occurs frequently.  The colloquialism here is “things are falling on the floor.”  In this case, people assume other people own something but those people also assume someone else owns it.  Consequently, no one ones it and important things don’t get done due to the gap in responsibility.  The expectations based upon incorrect assumptions can be the source of frustration because people recognized these tasks needed to be done and unjustly blamed others when they don't get done.  This also introduces counter-productive drama sometimes.

3.       The staff spends more than 10% of their time running around in react-mode.  A good question to ask is, “What percentage of the day do my employees spend doing what they planned to do each day versus having to fight the day’s fires?”  In a well-governed organization, the average day runs according to plan versus a non-stop merry-go-round of emergencies and urgent tasks.

4.       There’s a history of making poor decisions.

Some of the common sub-optimal areas of governance are portfolio management, project management, offering management, strategy and planning, financial accounting, audit, risk, and business continuity.  Having a plan or procedures is not enough.  There has to be uniform understanding of and respect for roles, accountability, rules of engagement, decision-making processes, and communication.  This must occur not only within the IT organization but between IT and the business.

Stay tuned for ideas for creating a harmonious governance model between IT and the business.

And a shout-out of thanks to my colleague Gisbert for giving me some ideas for this article.