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DataIQ Leaders briefing – Is data management an IT-managed or standalone function?

Is data management turning into IT, given the extent of the supporting technology required by the data and analytics function? With new tools constantly being launched, this whitepaper, based on a DataIQ Leaders discussion, considers key decision points in where to site this function and what its true purpose should be.
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Overview

Few organisations now operate without IT at their heart. As a consequence, data is now threaded through every process which ensures the organisation is open for business. For many senior decision-makers, the view of data management begins and ends with ensuring that systems are operational and the information they process is accessible.

Large organisations with hundreds of legacy systems are as likely to limit their view of data management to this perspective as digitally-native or start-up operations. Small organisations may only have half a dozen systems or may be managing key data sets in spreadsheets meaning they lack the visibility of how important data really is.

A number of factors tend to trigger a change in this view and drive a new focus on data management as a specific issue which may require a standalone function:

  • New eyes in the C-suite: whether a data-literate CEO or a change agent CIO, fresh thinking in the. boardroom is one of the most powerful catalysts. As well as fuelling investment in data management, however, it can also be the cause of political in-fighting (see “CDO v CIO – the battle for the boardroom”).
  • New technology strategy: migration into a cloud environment increasingly highlights the need for specific data management processes to support both D&A activities and the business processes they deliver to. An API-based environment makes this more feasible, although proprietary data models in the cloud can be a complicating factor, especially where third-party data sources are important.
  • Data breaches: the impact of a data hack or data loss is often the moment at which an organisations recognises it has lacked central data governance and controls (albeit too late). Using this as an opportunity to embed a new function based on risk reduction is a genuine opportunity. Similarly, regulatory change can be used to make the business case.

Recognising data as an asset

If an organisation understands the importance of managing capital and shareholder value, or has recognised the impact of effective HR on its human capital, then adopting a view of data as an asset is the next logical step. From the perspective of risk, value and even business-as-usual resilience, this approach can only yield benefits.

Recent events, such as the introduction of the European Union’s General Data Protection Regulation (GDPR), have served to increase the number of organisations where data is recognised as an asset in this way. Often, this has been a consequence of the data discovery process which reveals the extent of the data which resides across systems in an enterprise. Cataloguing and classifying this data shows up where there are significant gaps in governance and controls.

IT is not the appropriate function to respond to GDPR-driven insights of this sort, but neither is legal or compliance. Only by creating a formal data management function can an organisation approach the state in which data becomes a genuine asset which is capable of being extracted, enhanced, exploited and protected.

Understanding the tech stack for data management

Across every organisation in membership of DataIQ Leaders there is a common theme – that the technology required for data management in support or D&A is not the same as that supported by IT, even where it may look like an extension of incumbent systems. The way in which D&A needs to call off data – typically bulk queries which are not time-sensitive – is operationally different from how operating systems call off data – usually single records in real-time. 

The only arena in which these two activities overlap is digital marketing where the processes of ad bidding, targeting, content personalisation and so on happen within the e-commerce or e-service spaces. But, equally, digital marketing tends to operate within its own eco-system which is also not managed by IT.

Understanding this requirement and specifying it as a solution are central to what data management does as a function. It may well need to operate within the parameters of corporate IT rules, but in setting out clearly what is necessary, data management leaders establish a purpose and role for themselves that is distinct from core technology management. 

This is where the need to maintain an understanding of current tech solutions has a critical part to play. As a rapidly evolving domain, data management has been transformed in the last decade both by the possibilities of cloud services to manage high volumes of data cost-effectively and by the development of new suites of software to support everything from business intelligence to customer analytics. A data management leader who demonstrates a good grasp of these possible solutions and how they can solve challenges in the business as well as the D&A function will have a base that helps to create value, rather than being perceived just as a cost centre.

Putting governance at the centre

Data governance has been a critical activity for at least the last five years in any organisation that recognises data as an asset or has been working towards GDPR compliance. It is central to the role of chief data officer in its v1.0 form.

Regardless of whatever technology solutions are deployed or where data management sits within the organisation, governance needs to be the guiding principle for this function. The core essentials of governance remain what they have always been:

  • completeness – ensuring that data sets contain everything which they are supposed to and have reached an agreed standard of accuracy;
  • integrity – confirming the lineage of data, its provenance and compliance;
  • availability – delivering the right data in the right moment to internal customers, whether that is the data and analytics function or the lines of business it suppports.

This is exactly the task set which IT has historically had (and is directly derived from that function), but the perspective is different. While IT concentrates on information flows to keep systems operating, data management focuses on ensuring data can be used as a resource for insight and analytics, data science, new product/service development, pricing, CRM and so on. This connections with processes, rather than just the solutions they require, is the defining characteristic of this function.

Working with or for IT?

Having understood the connections between asset value, technology and governance, data management can clearly define itself as a function in its own right, just as much as HR stands alone outside of the people management which takes place within each line of business. By concentrating on meeting the data needs of the business, data management can avoid being lost in the noise of the daily processes which need this resource.

So does that mean data management should exist in its own right apart from the IT function? The answer will largely depend on the maturity and organisational preferences of each enterprise. Forcing a structure in which data management operates independently can leave it exposed as a cost centre that lacks political clout since it only connects with value-driving activities via a dotted line running through other functions (eg, D&A or lines of business).

Equally, it is not automatically a negative outcome if data management reports into a chief information officer, especially if the CIO has a set of fresh eyes and an enthusiasm for the domain. This can actually enhance the status of data management function (although much depends on the individuals involved, of course). The only consensus among DataIQ Leaders is that the chief technology officer should not dictate data management strategy, but should operate instead as an enabler and political ally. 

Further reading

Key factors in deciding where to place your data organisation”

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