In my previous post, I described how organizational success depends on certain building blocks that work in alignment with common business objectives. These building blocks include business activities, data and analytics.
Governance is also one of the required building blocks because it provides cohesion in the standards to align people, processes, data and technology for successful and sustainable results. Although it has been somewhat of an abstract concept, data governance is foundational to helping organizations use data as a corporate asset.
Assets are acquired and used to help organizations execute their business models. Principles of asset management require that assets be cataloged, inventoried, protected and accessible to authorized people with the skills and experience to optimize them.
Assets typically generate more value if they have high levels of utilization. In the context of data, this means governed data assets will be more valuable if they strengthen existing operations and guide improvements, supported by analytics.
As organizations seek to unlock more value by implementing a wider analytics footprint across more business functions, data governance will guide their journeys.
Becoming a data-driven enterprise means making decisions based on empirical evidence, not a “gut feeling.” This transformation requires a clear vision, strategy and disciplined execution. The desired business opportunity must be well thought out, understood and communicated to others – from the C suite to the front lines.
Organizations that want to succeed in the digital age understand that their cultures and therefore their decision-making processes must become more proactive and collaborative. Of course, data is at the core of business performance and continuous improvement.
In this modern era of Big Data, non-traditional data sets generated externally are being blended with traditional data generated internally. As such, a key element of data-driven success involves changing the long-held perspective of data as a cost center, with few if any investments made to unlock its value to the organization.
Being data-driven, based on analytics, changes this mindset. Business leaders are indeed starting to realize that making data more accessible and useful throughout the organization contributes to the results they want to achieve – and must report to their boards.
If traditional asset management concepts are applied to data, then objectives for security, quality, cataloging, definition, confidence, authorization and accessibility can be defined and achieved. These areas then become the performance criteria of the new data asset class.
So transforming an organization’s leadership and the rest of its culture to perceive and treat data as an asset changes its classification from “cost” to “investment.” Valuable assets earn a financial return and fuel productivity. They also can be re-invested or re-purposed.
Data governance is key to this new perspective of data as an asset.
Data governance is important to the modern economy because it enables the transformation of data into valuable assets to improve top- and bottom-line performance. Well-governed data is accessible, useful and relevant across a range of business improvement use cases.
But in the early stages of implementing data governance, organizations tend to have trouble defining it and organizing it, including determining which tasks are involved.
At its core, data governance is a cross-functional program that develops, implements, monitors and enforces policies that improve the performance of select data assets.
Implementing data governance ensures that “asset-grade” data is available to support decision-making, based on advanced analytics. Using this rationale, potential objectives to meet the strategic intent of the organization can be defined to derive value.
Following is a list of possible objectives for a data governance program:
The scope and structure of a data governance program are important to determine and include responsibilities, accountabilities, decision rights and authority levels, in addition to how the program fits into the existing corporate structure in terms of virtual or physical teams.
Structural options include top-down command and control and bottom up collaborative networks. Executive accountability also should be outlined.
It’s common for a data executive, such as the chief data officer, to be identified as accountable for overall data governance results. Data owners are business leaders who manage the processes that generate critical data. They’re responsible for defining the polices that support the program’s objectives.
Data stewards report to the data owners and are responsible for translating data policies into actions assigned to data specialists. The data specialists execute projects and other workflows to ensure that the governed data conforms to the intent of the policies.
Data stewards form the backbone of a data governance initiative. They influence how data is managed by assigning tasks to the specialists. Data stewards are responsible for cataloging, defining and describing the governed data assets.
These roles may be full-time or part-time, depending on the scope of the work.
Key processes carried out by the data governance team include:
Data governance is based on a strategy that defines how data assets should look and perform, including levels of quality, security, integration, accessibility, etc. The design and implementation of a data governance program should start with a limited scope and then gradually ramp up to support the overall business strategy. So think big, but start small.