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A successful data strategy is built with the future in mind

As the ownership of a data strategy is a collective C-level function, the correct drivers are crucial to its successful execution.
Read time 4min 20sec

We have all heard the saying “data is the new oil” and likely other variations of the phrase. The reality is that many organisations have labelled data as something that has enormous value to them.

Data today is classified as an asset. Assets assist organisations to generate revenue, increase business value and facilitate operations, including the day-to-day running of the business.

However, in order for assets to indeed be (or remain) assets, they need to be effectively managed. The bottom line is that every organisation has a set of strategic objectives it wants to achieve, among them being the following: improving operational efficiency, identifying new revenue streams, improving decision-making, and having an/or keeping a competitive advantage.

The underpinning enabler responsible for the success of the above-mentioned objectives is, in many cases, data. Yet, managing data with no view of future intended use reduces the ability to “realise” and grow the value of data over time.

It is therefore imperative for organisations to have a solid evolution-ready data strategy in place and aggressively execute on it to ensure company strategic objectives can be met.

According to DAMA, a data strategy is a set of choices and decisions that together, chart a high-level course of action to achieve high-level goals”.SAS defines it as a designed plan “to improve all of the ways you acquire, store, manage, share and use data”.

Irrespective of how one defines the actual term, the key objective remains the same: having a long-term plan in place to achieve strategic objectives using data.

I define a data strategy as a well-designed plan that aims to leverage data management principles to enable a business to achieve business objectives through the effective and efficient use of data (the asset). Getting this right requires the business to obtain clarity on a few important aspects.

In the modern-day data-driven world, there is a strong correlation between a business strategy and data strategy.

In the modern-day data-driven world, there is a strong correlation between a business strategy and data strategy. In fact, the data strategy is a determinative factor to the success of a business strategy.

Challenges in defining a data strategy:

A data strategy is not necessarily easy to create. Some of the key components to building a successful data strategy include understanding what you want to do with the data within your organisation, how you plan to manage it and how you plan to monetise it.

It is not uncommon for organisations to lean towards technology when building a data strategy. When used correctly, technology can achieve amazing results but is not a silver bullet to organisational problems. Data governance, data management, data quality, data ownership, data privacy, data access and data monetisation, among other pillars, should be the core focus of a data strategy.

A technology-agnostic data strategy is more likely to be sustainable than a technology-focused data strategy.

Having observed the Gartner Magic Quadrant for many years, it is safe to say that tools and technology are forever changing, some maturing and surpassing others. It is therefore important the enablers of a data strategy are mutable for sustainability.

Data strategy definition and execution pitfalls: The importance of the correct drivers

Twenty years ago, it might have seemed appropriate to attach the responsibility of defining, taking ownership and driving the organisation’s data strategy to the chief information officer.

As companies matured over time, a new role emerged: the chief data officer (CDO), or chief data analytics officer, as of late.

According to a Gartner survey published in 2017, also quoted by Forbes, two-thirds of CDOs indicated they were accountable for all data-related practices ‒ the primary focus being data quality, data governance, master data management, as well as information strategy, data science and business analytics.

Although it is still a belief that there is no single ownership of an organisation’s data strategy, it is important to note the CDO, being accountable for the core pillars that support the data strategy, inherently assumes the accountability for driving the data strategy forward.

Seeing that the ownership of a data strategy is a collective C-level function, the correct drivers are extremely crucial to the successful execution of a data strategy.

In the absence of a CDO, depending on the maturity level of the organisation, data-related practices might be positioned as secondary to other company initiatives. However, delaying action on a data strategy negates the ability to realise the true value of data which is a cooperate asset.

It is undeniable that ever-changing consumer needs are driving organisations to be more prepared for future consumer requirements.

This calls for an evolution-ready data strategy. And a data strategy geared for evolution and innovation is a well-designed technology-agnostic, mutable, adaptive, business value-driven, data-dependent approach to achieving company strategic objectives. Regardless of the change in consumer behaviour.

Windsor Gumede

Principal consultant at PBT Group.

Windsor Gumede is principal consultant at PBT Group.

He is a self-motivated, results-driven principal BI consultant with 10 years’ experience in data and analytics. Gumede has worked on numerous data and analytics projects in Africa and the Middle East.

Throughout his career, he has played different roles, from ETL/ELT development, to data modelling, front-end development, solution architecture and design, to pre-sales consulting. The majority of his experience comes from the telecommunications industry, but he is currently maturing his knowledge in the insurance space using big data technologies to help insurance clients comply with regulatory requirements.

Gumede is a strong believer in the core fundamentals of enterprise data management. “I see a huge gap in South Africa with technical resources that have skills in the big data engineering field but don’t have the proper grounding on enterprise data management principles. Skills on tools and technology without the literature is ineffectual.”

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