Aligning strategy with data management
Over time many organizations have developed a piecemeal approach to their IT management. Applications are developed or acquired as needed. Over several years this can lead to data silos, incompatible applications, and inconsistent insight into data. In many cases, organizations add data sources as needed and expand or limit access accordingly. Over years this can lead to dozens of applications and data stores that do not integrate with one another. Consequently, if organizations do not align their business strategy with their data management framework overall value is difficult, if not impossible, to defend.
Data Management Strategy Involves More Than Data
Managing these types of environments require alignment between business processes and technology requirements. This means that all analytics projects should be business-oriented and data management requirements should support business goals and needs. The reality for many organizations, however, is that IT and business goals are kept and managed separately, or at least not aligned well enough to implement using an organization-wide approach.
The alignment of business strategy with data management is important to ensure that technology investments provide the insight needed for organizations to make the right decisions in a timely fashion. This requires selecting tools that support current and future strategic and business oriented goals. Unfortunately, this is easier said than done. The problem with data management is that it is messy. As more data ends up within an analytical database, data lakes or other multiple data sources, the way it is managed becomes pivotal to business success. Data quality always becomes an issue due to inconsistent data entry, combining different sources into one, etc.
Data quality challenges over time that aren’t dealt with can become exorbitant. Adding disparate applications and departments acting independently, creates additional challenges because each department may have a different view of what entails revenue or customer, making it difficult to create a single approach to data management and identify the importance of each information entity.
Looking at the business implications and impact related to these data challenges means being prepared for the following outcomes, which is by no means exhaustive:
Inaccurate decision making
Ineffective marketing initiatives
High customer churn
Lack of visibility into behaviors and patterns
Inability to adequately plan and forecast
Bad competitive advantage
Inability to identify opportunities, threats, and competitive environment
Creating an environment that supports the level of alignment needed for overall data management success requires evaluating a few key areas within the organization, and committing to the necessary change management to ensure long-term success. The following sections provide the first steps for organizations looking to make this transition.
How strategy and data management help overall quality
By understanding the business needs and importance of data, organizations can identify the rules associated with information. For instance, how information is structured, how it relates to other data and departments, what its business value is, and by understanding how different data elements relate more broadly across the organization. This in turn, can provide the basis for a set of processes that manage data quality over time.
By having good quality, organizations can ensure the information they access is valid. Instead of arguing over inconsistencies, organizations can ask questions and get the answers they need. Understanding the end business goals helps an organization commit to ongoing quality.