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As digital transformation becomes mainstream and drives organizational growth, the enterprise information pipeline continues to expand, driven by a relentless acceleration in the scale and complexity of the underlying data driving the digital expansion.

 

This is less of an issue for organizations focused on a single platform or product.  However, for companies with multiple product value streams, platforms, and lines of business, achieving any sort of consistent view of their data ecosystem can be difficult at best.

 

This is not a new problem. At scale, data has to be managed, similar to any other corporate resource such as people, money or brand. Data Management has existed for years as a professional practice, with the Data Management Association (DMA) as the primary industry organization, which publishes a set of best practices known as the Data Management Body of Knowledge (DMBOK).

 

What exactly is meant by Data Management? Is it simply focused on the day-to-day activities of database administrators? Any Data Manager would answer with a resounding no; Data Management actually operates at a higher, “logical” level, and is concerned with such topics as:

 

  • Enterprise glossaries and ontologies, that is, describing the meaning of things
  • Defining the highest level structure and relationships of enterprise concepts
  • Describing which systems store which data (designating systems of record), as well as providing data feeds
  • Implementing Enterprise Master Data Management, which includes defining key code sets, abbreviations and numbering schemes
  • Ensuring that data quality is measured and maintained
  • Managing the security aspects of data, such as designating what data is restricted, confidential, etc.

 

Enterprise Data Management is also rapidly converging with Records Management, which is the traditional practice of managing official paper-based documentation, which itself is rapidly transforming into E-records Management. Since these records are stored as data, the associated databases must now comply with records management policies, such as identifying and applying retention schedules. This is a mandatory, legally driven requirement affecting broad swaths of the IT industry.

 

What does all this mean to today’s asset and configuration managers? Data Managers (and their close colleagues, the data architects) are invaluable in large-scale environments, or when the enterprise is faced with complex data-centric problems. As an example, when an organization considers integrating data from two disparate systems, data managers may be operating under the assumption that they are already integrated in an existing operational data store.

 

When designing a system to capture a certain data topic, data managers typically know what existing system is already designated as the system of record for that topic. However, if an enterprise has a continuing data quality problem, then a good data manager or architect can be consulted on areas such as exception reporting and auditable controls, which can help to continually improve data.

 

Is it possible the problem has become too complex? With multiple sources to integrate, it’s easy to see ripe opportunities for data overlap or redundancy. A capable data analyst should have the skills needed to solve such problems; he or she should know how to profile data sets, reconcile them and identify their strengths and weaknesses. A data analyst should also know how a given set of sources can be combined to become the required solution.

 

A data professional can certainly accelerate the implementation of a Data Quality Management solution; experienced data people should know the location of the source of truth for mission-critical data and how the integration and contextualization of that data can be leveraged to drive downstream value. They can then use this information to define enterprise data requirements and an overall data flow architecture.

 

Given the complexity of these issues, where do you find your friendly neighborhood data person? Often, he or she is associated with data warehousing, business intelligence or analytics groups, or less often, he or she may be aligned with a technical database administration team. It’s also important to keep in mind that database administrators (DBAs) are not data administrators.

 

The IT Service Management world, where Asset and Configuration Management lives, has often been oblivious to the concerns of data management. In a digitally transforming world, however, these two areas have every reason to become more integrated and provide important insights and lessons for IT professionals.

 

The flow of data into and through an organization, how that data is managed and how data quality can be optimized to drive value across the enterprise are mission critical considerations not just for DBAs and the IT organization, but also for the C-Suite as well. This is an area where Blazent has been leading the industry for years; for further information or to contact us, please click here.