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Data governance will shine in the Data Economy

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Information quality, accessibility and control have always been somewhat important in companies. But in this digital age, what was somewhat important now screams for comprehensive, outcome-oriented and business-driven data strategy and execution, because of increasing competition from globalization, new information-centric business models and a shifting regulatory landscape, such as privacy regulations.

Business data was once the internally generated and managed data from POS and ERP systems, an artifact of the business. Now it's big data, open data, the merging of internal and external data, and effectively using data to predict, detect and act on opportunities and shifts in the business landscape. This is what Forrester refers to as "the data economy," and companies vary widely in their preparation for this transformation.

While "data" has technical connotations strongly associated with "data processing" and the realm of IT, business leader awareness of the value and importance of data to business is rising. While regulation compliance is the top driver, followed by executive decision-making for strategic planning and customer relationship management, the new awareness springs from the pervasive value of data in business operations and execution, as shown by the priority given to time-to-market, opportunity identification and improvements to the efficiency of end-to-end business processes and business models. Data is no longer just about insight, it is what creates agility and speed for businesses to be competitive.

As data becomes a critical factor for more strategic and operational business outcomes, it's reasonable to expect that data management and governance would be competencies in which companies would want to excel. Unfortunately, the data doesn't show this. In fact, only 15 percent rate the data governance maturity as high or very high—defined as incorporating both business and IT with top-level support and spanning major parts of the organization. Download chart. For the rest, data governance is centered on IT, with moderate to no business engagement and with no executive-level business support. Why? Information governance is in silos and still mainly an IT-owned function.

A hallmark of mature data governance is that it recognizes that data is both fuel and currency. The ability of an organization to both manage and govern across the enterprise and various scenarios of use and states of data creates data wealth. However, only data security, privacy, compliance and one rule for all—the aspects of data governance most focused on risk—have that enterprise focus. Quality, master data, standardization and life cycle management remain localized-and likely disconnected across the enterprise.

Productive approaches

Effective governance, which fosters business ownership and responsibility, should emphasize the measure of business value from governance, the documentation of "business-friendly" data definitions and relationships, the integration of governance into planning processes and the alignment with strategy. However, with the exception of strategy alignment, all those other processes are rated as ineffective. The most effective processes are the ones most embedded in IT: project review, compliance with procedures and identifying data owners.

When data governance is viewed as an IT thing, investment is seen purely as an IT responsibility to justify. But when investment budgets are tight, a business-sponsored investment that relates to revenue or to business cost too often trumps the case for an IT-sponsored investment. The result is the need to sneak data investments into project budgets—or do without until the next budget cycle.

The perception that data governance is about roles, responsibility and process to enforce compliance with data rules misses the mark and turns data governance into a tactical tool. To develop a data governance practice that focuses on business performance, delivers business value and fuels business innovation, you must secure business executive commitment. Sustaining the governance program requires executive commitment and empowered business owners who focus on continuous quality and process improvement. The link between executive commitment and data governance is apparent; 80 percent of business leaders at top-performing organizations see data as a competitive or strategic tool, while only 37 percent of business leaders do at other organizations.

Additionally you need a strategic leader for the program. Accountability and empowerment are achieved when data governance leadership is established to create sustainability. More than half (54 percent) of top-performing organizations have or are in the process of hiring a data governance manager, while in typical organizations only 26 percent are making that investment. Invest in a data governance head who can bring a strategic focus to data governance efforts and foster business leadership, not just involvement. That person lays the foundation and drives performance management that services IT operations and business outcomes.

How to succeed

Organizational commitment starts with an executive sponsor who provides visibility to the senior team on the importance and accomplishments of the governance program. Successful and sustainable governance programs:

  • Create data citizens by emphasizing responsibility. An effective data governance program establishes clear data responsibilities. Successful organizations resolve the confusion over the role of business versus IT by establishing guiding principles and objectives defined by the business that become a framework for responsibilities, processes and procedures. That is important because the business is usually in the best position to define data rules and processes.
  • Define data policies to span service levels, consumption and quality expectations. Data governance teams need to understand how employees, customers and partners will use data and make decisions with it to specify the types of controls around it. As more data enters your organization through new channels, it varies in its reliability, accuracy and auditability. Business leaders need indications as to how much they can trust the quality of the data being used in the decision-making process. Helping workers understand where data falls on the spectrum of control and trust makes the risk and reward of working with data more transparent.
  • Enact a performance management approach to data governance. Achieve success through performance management monitoring and measurement. Use monitoring and measurement to track business outcomes, technology investment, operational efficiency and project management. Don't use monitoring and measurement solely to track tactical efforts and as an enforcement tool for accountability. Create measurements that will provide key statistics on the effectiveness of the program. Connect data governance effectiveness to business defined key performance, key risk and key value indicators as defined in the overall enterprise architecture practice. Data governance offers the checks and balances to guarantee that the data creation or ingestion process provides the level of quality and trust required by the consumption processes.
  • Drive business outcomes with a focused agenda and strong facilitation. A successful governance council includes empowered business owners capable of making the necessary decisions at the governance meeting, alleviating the need to defer a decision to another time. An agenda focused on performance management and decision-making to drive data governance council meetings keeps strategic focus. One government agency found itself struggling with data discussions that focused on technology management, project details and budget requests. Processes to manage data became highly informal and caused conflicts in prioritizing projects, meeting service levels for analytic requests and budget requests. By instituting a formal agenda focused on the big picture and business objectives for information needs, the agency could now better plan for modernization and reduce risks to delivering timely and trusted analysis to stakeholders.
  • Formalize planning processes to stay aligned to information needs. Incorporate planning processes that extend beyond IT architecture and strategy. Business stakeholders should contribute business plans and drive objectives and priorities that IT then translates into data management investment. Today, most organizations emphasize and create planning processes that are aligned to IT operational goals, not business goals. For example, a global machinery manufacturer instituted a project submission and review process that required line-of-business executives to provide expected return on investment across various strategic and operational performance areas. However, data projects were treated separately, which caused the organization to under-invest in data management. Because of information needs within its financial services division, the company had to more formally address the data impact. It is now including data governance oversight in its project submissions and reviews.

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