Suits The C-Suite

Digital technologies offer new opportunities to create value by leveraging data captured while companies carry out business as usual. However, while most companies embark on data-driven digital transformation, many still struggle to 1) determine the right data strategy that allows them to become a truly data-driven organization and 2) properly manage and govern their data. In fact, data management and governance ranked as the second top IT challenge identified by business tech leaders after IT security and privacy. As data increases in scale and complexity, some organizations remain fragmented and still work in silos to collect, transfer, process, analyze and store their growing data. Many companies cannot adapt to these changes and find themselves stuck in the archaic way of managing data.

As companies work to innovate and go digital, management must strike a balance between the need to implement information security mechanisms and effective data management, including but not limited to data quality, data governance and data protection. Based on research, consumers of data spend 80% of their time looking for and cleansing data, and only spend 20% of their time analyzing and transforming data into valuable information to drive sound decision-making. This highlights the need for companies to reassess their data management strategy as well as their governance structure to better manage their data.

Initiating data management and governance can seem daunting, considering how these cannot be confined to one corner of an organization. They can only be effectively managed through collaborative efforts between business departments and IT. Companies also need to govern their data environment regardless of the type of data and where it resides. A sound data management and governance program helps an organization achieve its desired targets over time to support its business objectives, while upholding data integrity and consistency accelerates the deployment of business activities and can reduce the cost of owning data. With this, companies should start by looking at their data sources and make sure that there are sound strategies and robust policies in place to protect the integrity of their data.

There are many reasons why data management and governance programs fail, or at least, underperform. A company’s data governance strategy and policies may not be established nor well-defined, or data management itself is either viewed as an academic exercise or treated like a finite project. Executives may also isolate data as an “IT issue,” leading to business units and IT not working together to manage data in a structured and repeatable manner. It’s possible that the company’s unique culture is not taken into account, or that company personnel are already overloaded and can no longer handle governance activities.

To revisit their data management strategy and governance mechanisms, companies can take the following items into consideration.

Companies must first define what data management and governance mean to their business. There should be a clear understanding of their business goals, since these will drive the company’s data strategy and scope. The scope is then defined based on priorities and the level of governance that fits the company culture.

Establishing data governance will impact the whole organization. Placing strong focus on the company’s change management and communications approach is vital for successfully implementing and sustaining a data governance strategy. Everyone in the organization needs to understand the purpose of treating data as a strategic asset as well as their role in this shift. Lack of ownership can be a very challenging issue, especially during the early stages of implementation.

Companies should also formalize their data governance committee and clearly define roles and responsibilities while ensuring that the responsibility does not rest solely on IT. Since data governance requires the collaboration of the entire organization, management support is the most significant component when starting a governance program.

Policies intend to establish ground rules that must be followed within the organization. They should enable the right people and the right steps to be taken at the right time.

Data must be managed as an important asset of every organization. Formal accountability should be put in place while compliance is ensured with the relevant regulations, especially on data privacy and security. Data quality must also be consistently managed across the entire data life cycle. Management should establish a periodic review and approval cycle to ensure that data governance policies stay relevant and responsive to the fast-changing business landscape. Proper key performance indicators (KPIs) must be agreed upon and put in place when the data strategy is implemented.

A company should be aware of what data it has on hand, making it imperative to establish a data catalogue which becomes the heart of the data governance framework. As a living document, the data catalogue is subject to changes to accommodate the organizational (business and technology) landscape. Companies must know where they use their data as well as why it captures, stores and uses the data.

A data catalogue should help entities define their data, identify data owners and a data custodian to establish accountability, and define data quality measures to ensure data integrity, confidentiality and availability. This allows management to rely on a single source of truth to support their decision-making.

There are several technologies available that can provide visualization of the quality of data that a company decides to master. This can be achieved by utilizing efficient design technology that provides accessibility and the seamless integration of data across all systems. It should also be noted that in selecting a design solution, cybersecurity is a key area of consideration.

Establish checks and balances to monitor data quality on a regular basis, the frequency of which depends on the required availability of top critical data elements. One way to achieve this is to implement audits and to monitor KPIs, as well as to continuously evaluate and improve the company’s data governance program.

Companies can no longer ignore data as a resource nor overlook its management to properly maximize its value. As companies continue to become more data-driven, their success will ultimately depend on their ability to manage and utilize a coherent view of their data. Better data — and a clearer view of what that data means — can give valuable insights that ultimately allow companies to make well-informed decisions in the face of change and growth.

This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.


Conrad Allan M. Alviz is a Senior Director from the Advisory Service Line of SGV & Co.