RE:think | Organic Data Governance

Data as a corporate asset

Data is a critical and strategic asset for any organization, making essential that the right information is available at the right time to the right people to enable the organization to gain the insights for better business decisions, increase efficiency and productivity and promote a consistent and effective use of data across the organization.

Understanding that data is a corporate asset it’s easy, handling data as a corporate asset is tendentially a more complex problem.

Considering data as a corporate asset involves controlling all its life cycle from acquiring data, maintaining it, to retrieving actionable insights from it.

As with every other type of asset, organization need to keep track of it, so that every stakeholder within the organization will know which assets are available to be employed to provide optimal returns.

How important is to govern this asset?

The capability to have control of the data existing in an organization, enables that organization to reduce risks associated with data, to reduce costs with data related processes, it’s critical for regulatory compliance and of course will work as an enabler for analytical applications of data, assuring that timely, consistent, and trusted data is provided business to support critical decisions.

Why Organic?

Having a data governance framework in place will not have an immediate impact on the business, but will improve trust, transparency, and reliability when meeting customer and stakeholder expectations.

Unfortunately, although the awareness of the strategic importance of data exists, most organizations are slow adopters of data governance frameworks, risking poor strategic decision making and misallocation of critical resources.

The reasons behind this slow adoption are many, and failure can be associated with lack of leadership buy-in and commitment from the top management and poor cross organization involvement, lack of alignment with business goals and benefits or lack of focus on strategic data, but also for frequently being approached from a technological perspective.

These are all structural problems faced by almost every data governance program during their development stages, some are unavoidable, but these risks and their effects can be somehow minimized.

As any process that is introduced into an organization it will create some disruption of the status quo, it will generate resistance to any change, a success approach to data must be able to overcome these and the challenges mentioned above.

Organic, because allowing data governance to grow from within, will allow data into new dimension, walking into a more data and digitally driven approach, where data plays a new role as a business asset, creating value to the organization.

This approach will produce long-term benefits, creating traction and increasing the awareness across the organization and will end-up acting as the motor from within the organization for a Data Governance structure that will grow organically out of the initial iteration.

Guiding principles

Data governance is about people, processes, and technology.

Is about combining these factors to create business value from data.

• Data strategy is business strategy. As any other asset in the organization data’s purpose is to create value, so any data strategy must be oriented towards the organization's strategic priorities and key business objectives.

• Business Cases. From here it is possible to identify how data may be used to deliver those priorities and objectives. These will be the business cases for the data strategy. In an early stage, for effectiveness purposes, there should not be more than five business cases, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.

• Start small, think big. Always aligned with the data strategy start with a small, targeted initiative, where the impact and value of data can be clearly identified and working with a business stakeholder that can passionately and effectively articulate the impacts of data in their business processes and that will be eager to defend the project.

• Measure and communicate. Setting up a set of metrics that can be linked to data governance and communicating them across the organization, a success story, that even at a small scale will create the awareness and act as a motor to leverage the replication of that story in other business units.

• Business on the driver seat. All the program and initiatives must be driven and oriented by the business units. Data governance is not an IT function, it is a business function, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.

• Agile mindset. Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow that visible results are presented in short time lapses.

• Integrated. Data governance is only part of the process of managing the organization’s data assets, it must be integrated with other initiatives, as Master Data Management (MDM), data quality, data stewardship workflows, data catalog, business glossary and metadata management.

• Data Minimalism: All the data being collected and processed in the organization within a specific context, either operational, regulatory, and its collected and analysed with an end in mind, sustained by a business case and aligned with the business objectives.