It is well known that organizations must view their data as a strategic asset, and treat it as such. Additionally, the data warehousing secret is out; business intelligence professionals have reflected at length on their abilities to deliver significant value. In fact, data warehouses are an excellent example of how companies use data to derive real business advantage. More recently, data professionals have begun to monitor the value their initiatives provide and ensure they produce expected results.
All of these steps represent sound data policy. However, most companies still fail to adhere to the most important data strategy guidelines of all: to consider data from a holistic perspective (i.e., from a business and technology standpoint), and align data with business objectives. This alignment, which helps companies increase revenue and decrease costs, is the fundamental goal of data strategy.
Effective use of data has been an issue companies have had difficulty grasping. The value proposition for superior data, for example, has often been ill-defined. Many executives realize they need better data quality, or a more unified view of data. However, only very rarely is the precise reason for such data enhancements tied directly to business value.
Additionally, even among business units, alignment is lacking. Different groups often have vastly distinctive ideas as to what data elements are important. With poor integration and communication among business units, data sharing cannot be leveraged optimally. In most cases, a more universal perception of key data elements would work wonders.
The Typical Response
Unfortunately, the response to these data strategy inadequacies has been overwhelmingly technology-oriented, and therefore, unsuccessful. IT’s data enhancement initiatives are undertaken in the proper strategic spirit. However, they are designed to remedy the imprecise reasons listed above, not linked closely to concrete business imperatives.
For example, data warehouses are often built to solve data problems; their inherent data quality improvements and broader access to critical information supposedly make the organization’s approach to data more strategic. On other occasions, ERP or EAI technologies are implemented to solve the problem.
However, these are pure technology solutions, and do not address data as a strategic business problem.
Gain Real Business Value
To leverage data optimally (and avoid failure), companies must create a sound data strategy, one that strikes a balance between business and technology requirements. An effective data strategy helps companies:
- Understand their business requirements and imperatives, and learn precisely how data can accommodate them.
- Highlight the business reasons for collecting, consolidating and using data, and define how it will be collected, stored and used.
- Understand the organizational needs. Requirements such as data stewardship and ownership, a team structure, and the skills to execute data are recognized as critical, and acquired.
- Deliver a method to enable the strategy itself. A thorough data strategy includes a roadmap-an initial plan and other specific, prioritized tasks-to migrate toward the new approach.
- Gain consensus. The data strategy will align the entire organization’s priorities, create a broad-based sense of urgency and include and executive mandate. These help ensure work is performed properly, on time and continuously.
A successful data strategy requires commitment from several important constituencies. As said earlier, both IT and business professionals must be steadfast in their dedication, and the strategy must be based on clear and specific business imperatives. Executive sponsorship-continuous investment and moral support-is also critical.
A series of small steps
As evidenced by numerous failures, creating a data strategy is difficult. A poor focus is one of the most significant data strategy problems companies face. While many are able to affirm a general direction for data initiatives, going the final step and establishing a precise, concentrated track is often too challenging. When these and other data strategy difficulties arise, it can be tempting to rely on technology, especially given the robust offerings available today. However, that approach leads data projects to go awry quickly.
By taking small steps in a common direction and focusing on flexibility and scalability, business intelligence professionals will produce an effective data strategy that goes deep enough to uncover potential issues.
Data professionals also must understand the organization’s current and desired states, and consider both tactical and strategic requirements. And finally, they must always remember that data strategy considerations call for a holistic approach, one that aligns technology and business, and retains a sharp focus on business considerations.
John Williams is Vice President of Technology at Collaborative Consulting.