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A Framework to Map and Grow Data Strategy


Many companies start an enterprise data management program during one fiscal year only to abandon it the next. This is something we see very regularly, but there are ways to prevent it from happening at your company. It is possible for a data management professional to keep multiyear data projects on track and successful.

The "hierarchy of data needs" is one framework that can be used to guide multiyear data management programs. This simple framework, modeled after Abraham Maslow's hierarchy of human needs, helps organizations map and grow their data strategy and can be a useful decision-making tool to prevent enterprise data management program failures.

Data management programs are designed to improve the way critical data is created, maintained and applied to decision-making to meet business goals. The program is a holistic, multiyear, companywide approach that includes the launch and coordination of individual projects. The actual projects might be as simple as a data definition standardization project or as complex as a multidomain master data management project.

Many enterprise data programs fail because initial projects do not meet the required benefits or expectations, company executives do not see the business value in the program when compared to its cost, or IT project implementations do not ultimately work.

Regardless of the fail points, choosing the initial data management projects that correctly align with the company's data needs is critical to the success of the entire enterprise program. Oftentimes, the company's data needs are not as grandiose as the stated needs of the designed projects. This is where the hierarchy of data needs can help.

The approach is based on the premise that organizations behave like individual humans when trying to achieve their goals. To help us understand how to apply this framework to the data environment, let's begin with what Maslow theorized.

Maslow's model explains human development through multiple stages of motivation, ultimately resulting in the final stage of "self-actualization." A rule of this premise is that humans must satisfy each need completely before moving to the next level. Maslow's five stages of human development are outlined with the needs of each stage:

Stage 1: Biological and physical needs. This stage consists of the most basic life needs, which are the most potent. If these basic needs are not met, the human does not grow.

Stage 2: Safety needs. Once the basic needs in the first stage are met, humans work to secure safety, order and stability.

Stage 3: Belonging and love needs. Once basic biological and safety needs are met, the human seeks to belong to a family, another person or within a group.

Stage 4: Esteem needs. This represents the need to be respected by others and by oneself. Satisfaction of self-esteem leads to feelings of self-confidence, worth, strength and usefulness.

Stage 5: Self-actualization. The final stage of human development is the desire for personal growth and fulfillment. Specific forms of self-actualization vary by person. As a person becomes more self-actualized, he or she becomes wiser and knows what to do in a variety of situations.

A caveat in Maslow's model is that the progression is not necessarily linear. Each individual may face situations that cause them to traverse through these stages several times. For example, the sudden loss of a job may cause a person to put safety and basic needs above the need for esteem or self-actualization.

Organizational Stages of Development

Now let's see how these five stages of human development apply to planning and managing complex data projects within the organization.

Stage 1: Biological and physical needs. Here we begin with the basics: the organizational need for data required by employees to perform their jobs. At this level, the data can come from many sources and in many formats. Employees do not demand formality or process in gathering the data they need. At this stage, you commonly hear, "Just give me the data." Most often, employees get the data they need themselves. If starting a data management program at this stage, begin with with a data inventory and data dictionary project first.

Stage 2: Safety needs. Concerns and questions will arise about the safety, quality and security of the data being used. Concerns about how others are using the data and how to ensure data consistency will also surface. At this stage, companies begin to think about data programs at the departmental level. This is the optimal time to launch an enterprise data management program because departments recognize a need. However, the company overall may not yet be mature enough to see the need for an enterprise-wide approach. At this point, a strong executive sponsor for data is needed to be the focal point for collecting departmental requirements. Departmental leaders may continue to argue against an enterprise data program, so the enterprise data leader should profile the shared data and provide clear facts on the state of the data spread across multiple systems and departments.

Stage 3: Belonging and love needs. Data belonging refers to a firm's desire for high levels of data organization, data relationships and data working groups. Up to this point, the data programs of individual departments have not satisfied the needs of the entire organization. Many companies start their enterprise data management programs at this level, although at this stage, departments have already built some data programs and may be closely wedded to their own approaches. This stage also requires sufficient time for education and communication. While organizations may be ready for enterprise data management, the entire organization won't necessarily understand what this may imply for specific roles within the company. Oftentimes, enterprise data management programs address the topics of governance, standards and master data without addressing basic data quality needs first. Tackle these needs through tactical data cleaning fixes in the short-term while designing more strategic fixes for the root causes.

Stage 4: Esteem needs. Esteem needs include data achievement and status (metrics), data responsibility (business process value) and data reputation (data confidence). At this stage, an enterprise data management program should be in operation, and should have instilled a degree of confidence in data throughout the organization. The need for real-time data to support business performance management and dashboards emerges. Data is beginning to move from a purely operational, auxiliary function to a source of new growth opportunity. Continuing projects to maintain data quality and ensuring compliance to the data standards and data governance practices implemented in previous stages are essential to maintaining the needs established in previous levels.

Stage 5: Self-actualization. The final stage of data development values data as a strategic asset of the firm. At this stage, data is trusted, available and timely. The firm shifts to integration of analysis capabilities within processes. No longer is a daily dashboard or report sufficient. Satisfying these needs requires real-time, trusted master data and a solid data architecture that meets the performance needs of the request.

In the real world, many firms would like data integrated into their processes without going through all the stages required to build a solid foundation. Even worse, they ask their data and IT teams to produce these capabilities without the proper foundation, which should have been built between stages two and four. As a result, the enterprise data management program manager has to scale back expectations and build a roadmap that can communicate a plan to achieve these needs while providing early wins in data quality and analytics.

The hierarchy of data needs is a useful decision tool in the hands of business and data leaders who are contemplating launching an enterprise data management program. Program failure can be prevented by using the model to:

  • Explain the evolutionary needs of data within organizations,
  • Assess the real maturity needs of the firm versus the "stated needs" and prioritize actions to address the most pressing needs first,
  • Predict the data development needs of an organization, and
  • Monitor lower-level needs to ensure those needs continue to be met while advancing to other stages.

The best way to keep your multiyear enterprise data management program on track is to deliver an enterprise data roadmap that ensures that lower, basic needs are met before progressing to higher needs. To sustain the success of your program once the organization arrives at the self-actualization stage, understand that maintaining that level may require continued attention to lower-level needs.

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