We get it; we must use data! So, what do we do now?

There are few people in the business space that would disagree with the idea that data is one of the most important assets of any organisation.

Accordingly, companies are investing in projects to improve how they use their data. As an indicator, the graph below shows the increase in interest for Google searches associated to key data roles.

Yet, many organisations struggle to capture tangible benefits from their data initiatives. Developing capabilities and implementing changes to harness the data, to improve decision-making and competitiveness is difficult

Where do I start? What teams and governance do I need? What technology do I use? What capabilities should I build, which to buy? Where can I get the best payback? What is the real value of my data anyway? - these are critical questions that do not always have an obvious answer.

Underestimating the importance of addressing these points from the outset and misunderstanding of the meaning and purpose of a data transformation initiative are factors that decrease the likelihood of success.

Below, I have listed some misconceptions that I have observed when planning data transformation initiatives - in no particular order.

Here there is some data, let’s do something with it!

Often it does make sense to start with small and tactical projects, and data exploration is a valid approach. Yet, transformation requires a guiding strategy, even if it must be revised frequently. The strategy should answer the questions I raised above, as well as set the governance, objectives, priority use cases, and milestones.

We are a data-driven organisation!

Despite recognising the strategic importance of data, often senior management find it too difficult (or boring) to get involved beyond the vision statement. Data transformation is a marathon, and it requires sustained and deep engagement and attention from senior management for a long time.

We are ready, our data is now in a cutting-edge Data Lake!

Identifying, cleaning and standardising data, update the architecture and infrastructure, and deploying new tools are a ‘must have’. Yet, technology is not enough. Assuming that these all-important tasks will deliver a transformation is a mistake. Business alignment, new capabilities, changes in culture - to name a few - are equally important.

We have done it; we are using a fantastic data visualisation tool!

Timely and accurately reporting provides better visibility of the business and can improve decision-making. However, that is just the starting point. Processes, products and even business models should also be examined – how can we use data to improve our customers’ experience? What value-added services can we create now?

We have all we need, data and a very good IT team!

Data transformation is a multidisciplinary effort. Data architects, engineers, scientist and technology partners are needed to build the right tools. Yet, analysts, business and domain experts, product managers and business representatives are also required to identify and drive opportunities. These resources are likely to need new skills and a fresh mindset.

We have successfully tested new analytic algorithms!

Too often we place most of the attention to the development of proof-of-concepts. In doing so, we make a number of dangerous assumptions: moving a PoC to production is simple and quick; people will use the solution because it makes sense; once deployed, the algorithm will keep working, forever! Obviously, that is not the case. A product perspective can help driving a more holistic and long-term view of the solution.

This does not pretend to be an exhaustive list, but it shows some of the ‘traps’ that we should be watching out for, when implementing a data transformation programme.

Do you have a different perspective? What else can be considered to increase the success ratio of data projects? I would love to hear your views.

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