Data.Governance.Data.Management.2016.jpgThis is the first in a series of data management blogs targeting trends, strategies and insights in enterprise data governance, risk management, digital transformation, enterprise performance management and information management for today's progressive enterprises. 

Internet of Things (IoT). Monetization of Data. Information Security. Data Governance Framework. The rise of Enterprise Data Governance is targeting today's data-driven digital enterprises. Are you ready for the demands and opportunities of next-level data management? 

To build the digital enterprise, a progressive framework for data management - one that transcends organizational silos and application or business area-specific data strategies - must be integrated at the enterprise level. With trends such as the monetization of data, the expectation for metrics-driven business analytics and intelligence, and the process improvements that they drive, as well as new roles and accountability at companies, including chief information governance officer, it is obvious a calculated and coordinated data governance framework is the clearest pathway to data management superiority for competitive organizations. 


Why? Data governance is a framework, a set of processes and procedures, bound by policy and with clear ownership and accountability, designed to ensure that essential corporate data assets are properly maintained and managed throughout the enterprise. According to the MDM Instituate, data governance is the formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset. Just as a factory is a key corporate asset that enables the organization to produce goods, so too is information an asset that enables the collection and use of information to understand consumers and their behaviors to drive true value for both the consumer and the enterprise. If a factory is unable to produce goods, it should come as no surprise that revenues will decrease and will impact the business.

If data is not governed appropriately, the expected knowledge gains will be inaccurate or missing entirely and so too will the value chain be disrupted.

Data governance touches on all internal and external IT systems and provides the structure and accountability an enterprise requires to operate. Data governance involves enterprise-wide policies, program objectives, guiding principles, operating procedures, as well as data governance champions - people who can own the information problem, coordinate information-related functions, and balance and prioritize the costs and value of information. Today's data governance goes beyond traditional records management by incorporating legal, IT, information security, privacy, compliance, risk management, eDiscovery, archiving and more to manage information at the enterprise level in order to support current and future business, legal, and regulatory requirements. 

Read Also: What is a Chief Information Governance Officer?

Data.Governance.Data.Management.jpgGoverning Enterprise Data 

There are four critical components to data management stewardship when it comes to the development and deployment of data governance policies and procedures for the benefit of enterprise operations. These four components create a data management framework for the execution of a data governance strategy.

  • Identifying Corporate Drivers for Data Governance:
    There are many drivers for data governance in today’s digital enterprises including assuring, complete, accurate and transparent financial data is collected and managed, compliant management of data privacy and security of information, improving integration of, or the ability to integrate key business systems and applications across organizational silos to gain new insights into internal and external business process and to void duplicating efforts in data generation and curation, and the aforementioned need to drive revenue by gaining a better understanding of customer needs, wants and behaviors.
  • Identifying Data Governance Objectives: 

    Data governance objectives may be different from organization to organization depending on how far along a company is in their digital transformation journey.  Some organizations may focus on data quality or cleanliness of a system before moving on and establishing clean up activities at an enterprise level; others may focus on building processes and rules for generating data, perhaps adding key approval gates for collection and use of data in their SDLC to assure proper curation as new systems are implemented; still others may focus on data management technology and being able to manage master data, or metadata that is routinely used to describe key enterprise or business function-critical information.

  • Identifying Data Governance Methods, People & Processes 
    Whether documented and easily articulated or not, all organizations, from the smallest to largest have some level of data governance methods, people and processes, but these are not always easily seen or exposed.  Small organizations have “that access database” or customer and supplier database with instructions and tipsheets to assure consistent data is entered into these critical systems.  Other examples as companies get larger are ERP systems, SAP, Oracle, Microsoft SQL and other databases where only administrators can add new metadata values for users to pull into records and these administrators have processes to follow.  More mature digital enterprises may have technology to manage key master data, thesauri to manage variations of the same information, taxonomies and schemas for organizing data in systems, a person or group working across functions to approve additions and subtractions of metadata from the model(s) at the organization and manage the policies, processes and procedures for these updates.
  • Identifying Data Governance Technologies: 

    Data governance technologies as you might expect may be simple or complex, from assuring consistent and controlled drop-down boxes or fields are used in platforms like SharePoint, to complex where several data models are being managed and applied in the aforementioned systems, e.g. corporate taxonomy, master data management, thesauri, and the like. Control of data in the corporate world has a cost that must be weighed against the benefits this management brings, whether increasing compliance, identifying and exposing new revenue streams, making it easier to enter information into systems or turning around and searching and retrieving it on the back end, these must all be considered when determining a strategy and developing a framework in your organization.

Today, data management, with an emphasis on data governance, is an escalating objective of strategic organizations. 

Enterprise data governance is seen by many information management experts across all markets as the most vital component in enterprise digital transformation as businesses scramble to become data-driven organizations in today's global economy. 

The demand for Enterprise Data Governance is directing technologies, strategies and framework objectives for Data Management today. Review additional Paragon blogs on Data Management and Data Governance.