Five Data Management and Analytics Best Practices for Becoming Data-Driven

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Analytics has hit the mainstream. The vast majority of organizations believe in the power of data and analytics to drive insight. Yet, there is a difference between using data to glean insights and analyzing data to drive decisions and actions—that is, to becoming data-driven. At TDWI, we see that organizations are at various stages of maturity when it comes to achieving this goal. For instance, in a recent TDWI Best Practices Report, only a third of respondents stated that they are data-driven.1 There are organizational and technology components critical for a business to succeed in becoming data-driven. On the organizational side, a key component to succeeding with data and analytics is to create a culture that supports these efforts. Companies that succeed are typically goal-driven, transparent, empowering, and collaborative. They have strong leadership that believes in data and they are governance oriented. On the technology side, the company takes steps to ensure sound data quality and has operationalized analytics to take action. Datadriven organizations often have an integrated analytics and data management strategy that spans the entire analytics life cycle from problem identification to data access and manipulation through analytics development, deployment, and monitoring.

It is impossible for an organization to become data-driven if team members don’t collaborate. In fact, TDWI research often cites collaboration between groups as a critical ingredient to analytics success. In addition, collaboration is linked to improved market performance and innovation.

Collaboration can be hard, though. We often hear from business groups that IT has too many complex processes and doesn’t understand that the business needs results quickly. IT feels that the business doesn’t understand its priorities in terms of data management. Politics often enter the picture. 

The key is for these groups to appreciate each other’s perspective in order to move forward effectively. Of course, having executive support can help because executives set the tone and vision for the organization. However, it is important for other data and business leaders to come together as well. That means sitting down and communicating. A typical comment we hear from organizations looking to become data-driven and collaborative is, “Once IT understood what we were trying to do, it became easier to work together.” 

That is not to say that groups don’t have individual roles—they do. In fact, there are numerous roles that come together as part of a team to make sure that analytics is successful. These roles include IT and the business as broad categories, along with more-defined roles in each category such as architects, data scientists, business analysts, and business sponsors, to name a few. Defining roles and responsibilities is important; a study by Harvard Business School found that collaboration improves when the roles of individual team members are clearly defined and understood.3 A more recent study performed by Google (as an extension of its Project Aristotle), determined the same thing.

At an organizational level, role definitions include: 

IT/Architecture owns the data strategy. Previous TDWI research indicates that in an analytically mature organization, IT owns data management. That includes creating the enterprise data management strategy, increasing data sharing, dealing with metadata, and owning data quality. In this way, IT can help ensure data integrity to support business decisions.

The business is responsible for aligning projects with organizational objectives. Although collaboration between business and IT is essential to ensure data exists to answer the questions, the business typically owns the decisions and performs the analysis. Business users understand the kinds of questions they want answered and should therefore identify analytics objectives. This includes identifying metrics the organization should measure from the results of its analytics efforts to determine whether it achieved its goals.

•  Roles within these functions. There are numerous roles within the business and IT functional areas. These include business sponsor, business user, business requirements analyst, data architect, data steward, data analyst (develops data models), business analyst (performs analytics), data scientists, data engineers, DevOps, and many more, with new roles becoming the norm. 

 

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