“How you gather, manage and use information will determine whether you win or lose.” That’s what Bill Gates envisioned future corporate strategies to be like. If you look around, that’s what some of the dominating organizations around the globe are using as a strategic weapon. Organizations like Google, Amazon, P&G, Wal-Mart, Harrah’s and Capital One are all dominating their fields by deploying rigorous business analytics to manage and mine information for running their businesses.

Capital One, for instance, runs multiple experiments round the year to identify the right offers for its customers and price them appropriately. Harrah's deploys sophisticated analytics to drive loyalty and customer service. Wal-Mart is using vast amounts of data to improve its supply chain and optimize inventories across the network.

However, not everyone is competing on analytics at the highest level. Our own experience as a third party analytics services provider shows companies are deploying analytics to varying extents and in different manners within their businesses. We refer to the extent of a company's deployment of processes that capitalize on data driven decision making capabilities as Analytic Maturity (AM).

Companies high on Analytic Maturity, like Capital One or Harrah’s, capture and manage lots of transaction data across the enterprise in a centralized manner. Information linkages are transparent and flexible across the enterprise. They have a culture of fact-based decision making at an enterprise level using sophisticated statistical analysis, predictive modeling, dashboards and related tools.

On the other hand, companies that are low on Analytic Maturity have been using analytics inconsistently. For example, analytic specialists such as statisticians might be focused only in specific departments. Analytics is inconsistently deployed across departments, and an integrated analytic vision is not aligned with the corporate strategy group or senior management. Individual departments select their own tools, control their own data warehouses and train their own people on different analytics processes. Needless to say, this does not lead to the optimal solution at the corporate level!

It is not surprising that in Gartner’s 2006 CIO survey, it came out that “Two-thirds of CIOs believe their competitors make better use of information,” and strengthening the ‘information value chain’ is their number one challenge.

A compelling solution  lies in creating a centralized analytics pool (“Analytics Unit”) at an enterprise level which aggregates and fulfills the analytics demand across departments. This Analytics Unit is aligned with  a centralized data warehouse having information linkages across all departments in the organization, hence ensuring effective company-wide impact.

The operating model of the Analytics Unit would be to treat every department as its “client” and allocate resources in a dedicated manner based on appropriate scoping of departmental requirements. This pool functions as a corporate resource  serving and coordinating the various needs of client departments throughout the organization.

When effectively implemented, this can result in a high quality and reliable support mechanism that will enable accurate decision making, both at the department and the whole enterprise level. Key factors include:

1. This central unit will build an integrated view of the business which it can then effectively use to guide departmental decision infrastructures, and
2. It will ensure that critical data and other resources are well managed and that different parts of the organization can share data easily, without the impediments of inconsistent formats, definitions, and standards.

The challenges are substantial. Setting up a corporate-level Analytics Unit requires a lot of commitment, resources and capital investment to ensure successful integration with the broader organization. It does not stop here. Once the Unit has been set up, running it and managing it is like running a parallel organization. Depending upon the industry,  enterprises will need to adopt different strategies; for example, qualifications, job descriptions, training needs, career paths and incentive programs will all be industry-specific. The initial investment and necessary scope of organizational transformation are sufficient to discourage  many companies from attempting to evolve towards or achieve  this “utopian” state.

A good alternative that is being increasingly deployed  is the utilization of third party Analytics vendors to provide these services. The mantra is simple: Allow this unit to be run by those whose core competency and bread-and-butter is “Analytics”. Meanwhile, enterprises can focus on their own business / core competencies.

The Analytics vendor would be a “strategic partner” with the enterprise and would build a “virtual captive” for serving the analytics needs of all departments across the enterprise. The primary responsibility of the strategic partner would be to “extract” a pool of resources from its existing resource pool and provide them on a dedicated basis to the different departments of the enterprise. Everything related to ramping up to the desired scale, training, retaining, managing attrition, etc., would be the responsibility of the strategic partner. The partner would be aligned to the overall business objectives of the enterprise through an appropriate Service Level Agreement (SLA) in quality and timeliness of delivery.

It has to be kept in mind, that this alternative is not a simple turnkey solution.  As in any other strategic partnership, Enterprises need to bring the appropriate level of executive sponsorship to the arrangement. It will have to identify key stakeholders across different departments who will work jointly to enable this construct to come live. They will have to work closely with the partner to jointly develop protocols of accessing data, running the unit and delivering output to “client” departments. In some situations, enterprises might have to depute a Program Manager at the partner site for 3-6 months to move the Unit to steady-state operations. However, at an overall level, the investment and bandwidth of setting up and running the Analytics Unit through this alternative will be far lower than in trying to do the same internally.

Hence the concept is catching on fast. Recently several multi-million dollar analytics outsourcing deals (including offshoring to India) running across multiple years have been announced.  Several third party Analytics vendors are cashing in on this mega trend that is sweeping enterprises around the world. Our own experience has shown tremendous growth in the Analytic Maturity of our clients, thereby validating the value of the approach.

We believe we are still seeing only the tip of the iceberg, with many more such deals to follow soon as their value gets recognized.   Our expectation is that as enterprises progress along the Analytic Maturity spectrum the scale and scope of these constructs is only going to get bigger. It is for you to decide when you want to jump on this bandwagon. Our suggestion is: the sooner the better!

Bibliography:

1. www.cio.com
2. www.gartner.com

Acknowledgements:
The author is grateful to the following people for their continued support in writing the whitepaper.

1. Govind Choudhary, Manager, Analytics Services, Inductis
2. Vivek Bansal, , Programmer Analyst, Analytics Services, Inductis
3. Saurabh Mathur, Programmer Analyst, Analytics Services, Inductis