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Every organization needs a data strategy to develop and use the value of its data and information assets.  Developing a data strategy requires executive commitment and perseverance to an enterprise view of data’s capabilities.

If you speak with many CEOs, most will tell you they firmly believe data is a valuable corporate asset, which, if used properly, can deliver increased insight, opportunity, and a stronger competitive advantage.  However, most executives will readily admit their organization has no formal plan for the use or management of data across their organization.

In fact, few companies possess the framework (or knowledge) to manage this asset in the ways other corporate assets are managed (e.g., cash, investments, people, equipment, and facilities).  Sadly, most companies lack a basic inventory of their data, a map of where data is stored or located, and an understanding of the quality of their data, not to mention how to manage and use it to support competitive advantage.

The pattern is a familiar one: An organization undertakes a data initiative in a project-based, piecemeal fashion, and addresses only the needs of a particular business unit or department.  The result is a siloed approach, which provides a limited benefit to the rest of the organization. The repercussion is the creation of a sub-optimized solution that causes inconsistencies and a lack of integration with other systems or a lack of shared use of the data/ information from an organizational perspective.  Organizations end up with lots of fragmented data and multiple—often redundant—analytic silos. As the 21st century’s lifeblood and currency, data needs to be a key element of a company’s business strategy, yet too often, it is an afterthought.

The Need for a Data Strategy

For some organizations, an enterprise-wide data strategy is viewed as a strategic advantage.  This transformation in perception has been achieved due to a number of factors, which include the increase in corporate mergers and acquisitions, as well as the volume of new system purchases, and the need for data integration.  But the primary push is being led by Big Data—the exponential growth, speed, and availability of both structured and unstructured data.  It is forcing companies to make data strategy and planning a priority. The questions then become “What do I use this data for?  How can it bring business value to the entire organization?”  Organizations need an enterprise data strategy to be able to answer these questions and to uncover the value inherent in its data and information.

Several surveys have shown that only one in 10 organizations possess a data strategy, causing most companies to face the following challenges:

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Figure 1: Challenges from Lack of Data Strategy

These represent only a handful of issues that any organization can experience as a result of poor data practices; there are many more.  Following are three real-life stories of companies that did not possess a proper data strategy and suffered the consequences.

Example One: “I See Data Models (Everywhere)…”

Company XYZ was in the midst of updating and refreshing its core processing systems with new system modules.  The IT group noted that implementing each new system module required the design and development of a separate data model.  This activity strained project resources while increasing implementation time and expense.  Furthermore, the increase in the number of data models added unnecessary complexity and confusion when maintenance or enhancements to the new system were needed.

What could Company XYZ have done differently?  If the company had an enterprise data strategy that included the development and use of an enterprise data model, it would have eliminated a lot of repetitive design and implementation by using the enterprise data model as the basis for its data design.  Any necessary application models would have been based on the enterprise data model, thereby significantly reducing interface coding and complexity/confusion.

Example Two: “If You Build It, They Will Come…”

Organization PDQ expressed the need for a data warehouse to centralize its reporting and analytical data.  The IT department assigned one of its team members to design and develop an enterprise data warehouse without business involvement or support.  The project was completed over the course of several years and the data warehouse was loaded with the applicable data. Unfortunately, when the final environment was unveiled to the business, most business staff showed a great deal of apprehension and reluctance about using this warehouse.  Usage reports later showed that almost no one accessed the expensive data warehouse and all reporting and business intelligence continued to come from applications that pre-dated the data warehouse

This lack of business involvement led to a lack of credibility between IT and the business areas.  Even worse, the time and money spent to build the warehouse was, for all intents and purposes, wasted.

Example Three: “I Just Want to Do Some Simple Analytics…but Where?”

Enterprise RLM wanted to develop an analytics model for price and market segmentation.  They selected an outside consulting firm to create and implement the analytics model.  It also hired an internal data scientist (at a high salary) to collaborate with the outside consulting firm on the project.

Unfortunately, once the model was implemented, the project work—and the need for the internal analyst—came to a halt.  Sadly, there was no plan to leverage the previous analytical effort and to address other corporate needs or company pain points using data and analytics.  The company was forced to identify potential projects after the fact and “on the fly” as this high-priced analyst spent significant time waiting for new projects to be identified.  If the organization had an enterprise data strategy that included an analytics plan, projects would have been identified and prioritized in accordance with the strategy’s guidelines.

Bringing Data to the Forefront

A unified approach and strategy for data/information development, implementation, and management would have provided these organizations with a solid, enterprise-wide framework for using data to support organizational goals and missions.  The difference between organizations that are crippled by data problems and those that control and manage their data for competitive advantage lies with the creation of a solid data strategy that is reinforced by executive and business awareness, support, business collaboration, consensus, partnership and participation.

The Business Intelligence Leadership Forum surveyed 96 BI managers about the data challenges they faced.  Overwhelmingly, they cited business and leadership issues above technical ones. The major consensus was there is a need for strong commitment from top management and strong centralized data governance throughout the organization, otherwise data will always take a back seat to other corporate initiatives.


If the organizations cited here had implemented an enterprise data management approach and a data strategy that included business and IT representatives, they would have been able to “row in the same direction,” avoiding many of the obstacles and failures they encountered.  A properly developed and consistently deployed enterprise data strategy provides structure, collaboration, and communication needed to ensure the realization of value of data and information assets.


William B. Jenkins

William B. Jenkins is an accomplished data management and analytics executive and consultant, primarily in insurance and financial services. Bill has held a number of insurance executive level positions including CIO and COO, VP of Human Resources, senior Data Management and Project Management positions, and president of an IT/management consulting firm. Also, he has been appointed to several advisory boards in leadership roles, including ACORD, IDP, OMG, and A.M. Best Rating Services. Bill earned degrees from Temple, Drexel, and St. Joseph’s universities and is a Certified Property and Casualty Underwriter ( CPCU).

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