Shari would like to thank Umesh Hari, senior director, Data Management and Architecture, Acccenture, for his contribution to this month's column.
Recent research from Accenture has revealed that in many organizations, reliable information is not available when and where it is needed. The problem is that too little focus has been on the underlying data itself and the architecture that delivers and manages this vital raw material.
Most organizations have gone through decades of acquisitions, divestitures, reorganizations and regulatory changes, which generate more data. Rarely is this data fully managed and controlled. The management of data across its lifecycle will bring order to this situation. At Accenture, we define this as data management and architecture (DM&A). A good example is the lack of standards for common customer and product master data in applications acquired from various mergers.
What does it take to achieve excellence in DM&A? Based on client work, it is clear that there are two key criteria. The first is that a people-centric governance model and data management processes must be implemented in order to successfully manage data within an organization. New technology may be part of the solution, but successful DM&A requires excellence in people and processes, especially around data governance. People - their roles and responsibilities and the way they work together - are critical. So, too, are the roles of data owners as data moves through an organization. A good example is the creation and governance of standards by different data owners for product master data and its various attributes, such as design (i.e., design modification number) and operational attributes (i.e., serial number and color). The data management processes - the governance procedures, definitions, rules, standards and models that shape and guide data from its creation to its obsolescence - are vital in managing data.
The second criterion is that data management requires six key capabilities that span the lifecycle of structured and unstructured data (including how data is created, stored, moved, used and retired). These six capabilities are part of a detailed framework shown in Figure 1.
Data governance is defined as data ownership, data stewardship, data policies and data standards. These are the roles and responsibilities, rules and policies that guide the overall management of an enterprise's data.
Data structure is how data is organized in a specific enterprise, from overall corporate data models down to the level of an individual system. This capability includes data modeling and data taxonomy.
Data architecture is the processes, systems and people required to store, access, move and organize the data. This capability includes data migration, data storage, data access, data archiving and data retirement.
Master data and metadata is the language of doing business. Master data includes the business objects and classifications that describe overall business information; metadata is structured information about data. Within this capability, you should focus on master data management, reference data management and metadata management.
Data quality is the ability of data to satisfy the organization's requirements, typically measured in terms of the data's accuracy, completeness and compliance. Data quality is accomplished through data profiling, data cleansing, data monitoring, data compliance and data traceability.
Data security is the protection of the data from unauthorized access, viewing, modification or deletion, whether accidental or intentional. Data security includes data privacy and data retention.
When organizations begin to examine these six capabilities, they will uncover the root causes for key business issues specific to their organization. To achieve excellence across all components of the DM&A capabilities, an organization will have to adopt a roadmap that will be a multiyear process - a data management journey that is prioritized based on business value. To be truly in control of data management, an organization's journey must incorporate three vital elements for success:
- A data management diagnosis is required to evaluate the organization's capabilities at the outset in each of the six capabilities. Where is the organization in achieving these capabilities? What are the capabilities required to resolve the business issues?
- A portfolio of DM&A projects based on the areas targeted for improvement is needed. They may be standalone projects (for example, a data quality project to address a significant business issue) or part of a larger project, such as an SAP implementation. The capabilities are prioritized and reflected in the portfolio.
- A roadmap for implementation that provides the sequence for these projects is vital. In laying out this roadmap, important factors to consider include the organization's business model, past actions and future plans, business imperatives, technical dependencies and the requirements of projects already underway.
An organization's roadmap and its portfolio of projects should be revisited periodically. Organizations that aspire to high performance are taking an innovative approach to data management. Rather than defining data management narrowly as a technology challenge, they take a holistic approach that encompasses the people and processes that are also critical to success. They understand that excelling in data management will ultimately require strength in each of six capabilities. And they are willing to embark on a journey, knowing it will provide rewards at each milestone in the implementation roadmap, which will bring them greater success in all areas of information management and new heights of business performance.
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