How to build a data management strategy in the era of compliance - image credit -Gerd Altmann from PixabayDespite the importance of data in all its forms to businesses everywhere, many organisations still fall short in meeting their compliance obligations. It is a particularly important issue, given the enormous numbers of data breach incidents and cyberattacks taking place almost daily. For many organisations, it’s time for a data management strategy re-think. In doing so, IT teams need to focus on several key areas:

Get the foundations right

To build a robust data management strategy, it is essential to understand precisely how data is handled across the organisation from the point when it is created, and from the edge of the network perimeter to the data centre.

Consider these questions:

  • Does it need to be retained?
  • If so, for how long?
  • If not, what happens to it?
  • Where should it be stored?

Data retention requires an approach that balances business with compliance legislation. It ensures the correct levels of accessibility and management of data can be delivered. Any data retention rules and principles should, in turn, guide the development of appropriate processes to identify important data and then manage it in the most efficient and cost-effective way. It proves that, once again, knowing more about the data is essential.

The choice and use of a data management platform needs to support data management and compliance strategies. Engaging with data management and storage service providers, organisations can optimise data management, data quality and data governance. It supports efforts to remain compliant and helps organisations leverage more value from their data through analytics and intelligence.

Focus on data quality

One of the biggest challenges businesses face in building a successful data management strategy is the ‘noise’ created by the large volumes of data many now hold. Applying data analytics to focus on what’s important is essential if enterprises are to improve efficiency, business decision-making and secure that crucial competitive edge while remaining compliant. Big data can add significant value to the decision-making process. Equally, supporting and managing large volumes of unstructured data can be complex. Inadequate data management and data protection can introduce unacceptable levels of risk.

Further supporting the requirement for enhanced data management is DataOps, an automated and process-oriented methodology aimed at improving the quality of data analytics. It provides faster and more comprehensive analytics to leverage more value from data. It can only be accomplished if data is managed correctly, the right governance protocols are in place, and data quality is kept to the highest standard.

Another data quality issue is that of dark data. This means any data owned by an organisation that it has not categorised or is unaware of. As such, it should be seen as the enemy of data intelligence. If an organisation doesn’t know what data it has or what it means to its business, it is worthless. It is also a source of risk, causing organisations to be in breach of data privacy legislation such as the Protection of Personal Information (PoPI) Act or the General Data Protection Regulation (GDPR).

However, a centralised data management platform can ensure that data is correctly captured, categorised, and supports compliance obligations. The best solutions can analyse data and determine its value to the business. They identify what to keep, what can be overwritten, and what requires additional review. Focusing on data quality, therefore, translates into cost savings. Businesses do not need to purchase unnecessary storage space, maintain costly cloud resources or keep data they don’t need.

Keep pace with emerging trends

Challenges for data management are evolving all the time. As data volumes continue to increase, and technology changes, compliance becomes more complicated. For example, protecting serverless applications needs to be handled via Application Programming Interfaces (APIs) across API vendors, irrespective of where the data resides.

Similarly, edge computing is also changing the way data needs to be handled. It moves data creation and processing to the edge of networks. Security is an important issue. Managing the velocity and volume of data creation at the edge, and doing so in line with regulations, means data management is increasingly challenging. Protecting data in the multi-cloud is another growing requirement. Hybrid environments have many benefits, but ensuring data security across increasingly complex infrastructure is dependent on finding the right data management partner.

In the current era of compliance, having a flexible data strategy management in place is vital. It is more than just keeping pace with regulatory requirements. It is about meeting the challenges and opportunities of cost control and competitive advantage.

Mark Jow

Commvault was formed in 1988 as a development group within Bell Labs, and later designated as a strategic business unit of AT&T Network Systems. In 1996, it was incorporated as an independent company. In the 20 years since, we’ve experienced tremendous growth, pioneered numerous industry-shaping innovations, and established ourselves as a respected leader in data and information management.


  1. You define dark data as “any data owned by an organisation that it has not categorised or is unaware of”. That is certainly one kind of dark data, but dark data can also come in other forms. More generally, dark data are other relevant data of which you are unaware, perhaps because you have collected but not used it, but also perhaps because you failed to collect it. Dark data can have huge adverse consequences for organisations, and can lead to commercial and financial disasters, even fatalities. Many examples are given in my recent book, “Dark Data: Why What You Don’t Know Matters“, published by Princeton University Press earlier this year (UK link). The book might be of interested to your readers because it points out how ignorance of dark data can lead to problems but also shows how to detect and overcome the problems. Indeed, it then goes further and shows how through the strategic application of ignorance one can take advantage of unknown dark data.

    (Posted by Steve Brooks on behalf of Professor David J. Hand)


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