From foundation to innovation

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Data is very much in the spotlight in today’s business environment. We se it in how organizations are moving towards automating their data-related processes in order to minimize their output error rate, reduce the cost of data remediation, and maximize insights. There has also been a surge in the demand for data professionals such as data scientists and engineers to better analyze unstructured pieces of data and turn them into valuable information (e.g., reliable trends, forecasts and projections).

Data’s celebrity status, however, brings its own share of risks. Issues such as data privacy breaches, inaccurate reporting and the unmanageable volume of data have caused big hits to organizations across the globe. One may wonder — what are organizations doing to address such risks? While organizations institute controls to minimize these risks, a lot of them (from what I’ve observed) set their sights and focus their efforts and resources on famous technologies and tools for data analytics, robotic process automation and artificial intelligence/machine learning.

Although there are endless possibilities as to how far our data tools and technologies can take us, nothing useful and reliable can come out of them if we do not have quality data. Are organizations really putting their efforts and priorities in the right order? Are they able to address the core issues concerning their data or are they just going with the trend?

There are no right or wrong answers, but one thing is clear: data will continuously reshape the business and economic landscape. As such, organizations must take steps to ensure that “data” is accurate, complete, consistent, timely and uniformly defined by the stakeholders, who in turn should have the proper mindset so as not to be “consumed/governed” by the data.

Taking the necessary steps does not require organizations to run before they have learned how to walk. Before organizations can start to run and take the big leap, strengthening their foundations is crucial if they are to achieve the targeted benefits. Accordingly, data governance would allow organizations to view data innovations holistically from both a control and growth perspective.

The journey towards data innovation requires a proper survey of current capabilities and state, against a benchmark or aspired state. Organizational awareness of data governance maturity level is necessary in leveraging strengths and addressing weaknesses. A comprehensive maturity assessment will help to increase organizational awareness on how it performs against the critical components of data governance.

The maturity assessment requires an organization to answer significant questions. Are policies, processes, and standards in place to support a data-driven culture? Is the data architecture capable of specifying the ‘golden source of truth’? Is data quality measured through defined metrics where data can be described as accurate, complete, timely and adaptable? How are tools, technology, and methodology aligned with the overall data strategy to support organizational growth? These are just some among the list of many questions looking at the components of data governance. Determining the answers to these questions before diving into big technological investments and hiring data specialists will help in addressing the right requirements and maximizing the benefits of data to the entire organization.

Depending on the results of the data governance maturity assessment, organizations should then prepare a Roadmap defining the necessary steps to address the noted gaps while leveraging the strengths. The roadmap serves as the plan that defines the business case, strategic direction and scope as agreed with data stakeholders. Once this roadmap is approved by senior management and the board, organizations can proceed to the design of the organization’s data governance framework. Details of the data governance components are crafted in more detail: roles and responsibilities, and organization, policies and processes, change management and data culture, data architecture, data quality and metrics, and tools technology and methodology. The design will also show how data looks like for the organization, how it will flow and be used and, more importantly, how the data governance transformation will affect the organization’s overall financial and human investments.

One of the key challenges in the data governance transformation journey is cost. Managing the trade-off between what’s best and what’s cost-effective is nothing new. As the best solutions usually require commensurate investments, organizations should budget time and resources to ensure that the data governance transformation roadmap and design will sail towards the expected direction that will ultimately be beneficial to the entire organization.

Roadmaps and designs are merely dreams if not implemented. For these dreams to translate into reality, an organization must commit its time, resources and executive sponsorship in the Implementation phase. Knowing how to do it and actually doing it are two different things. One’s confidence in the theories and prototypes at hand will only be enhanced once these are implemented and put to practical test.

Implementation will vary across organizations as data governance transformation roadmaps depend on their respective maturity levels. Despite these variations, successful implementations require board and executive support to oversee and direct the data governance journey. To enable the board and senior management carry out their respective roles, we have seen organizations appoint a Chief Data Officer and establish a data management office to drive the transformation. They also ensure that the organizational efforts from various stakeholders are aligned with the organization’s data governance strategy.

Does data governance end in implementation? Definitely not. It’s a cycle requiring continuous improvement to ensure its effectiveness and adaptability to the organizational context. Sound data governance is self-aware, dynamic and improving: capable of knowing when to change and adapt to a progressing organization with evolving data requirements.

From changing your mindset to “us governing the data” to knowing the “data governance with control and growth perspective,” now is the time to start your data governance journey: assess your data governance maturity, develop your roadmap, design and implement your data governance framework.

The views or opinions expressed in this article are solely those of the author and do not necessarily represent those of PricewaterhouseCoopers Consulting Services Philippines Co. Ltd. The content is for general information purposes only, and should not be used as a substitute for specific advice.


Aya Zelline L. Gevaña is a senior associate with the Risk Consulting practice of PricewaterhouseCoopers Consulting Services Philippines Co. Ltd., a Philippine member firm of the PwC network.

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