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Start harnessing data projects successfully for business value in 2017

Data projects have huge business value potential, which we will continue to see in the short, mid and longer term, but how can they be used to derive this value properly?
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Indeed, this will remain an important piece of the puzzle that most businesses will need to conquer. And it is having the right people for data projects is the key to unlocking business value.

When most people think of data projects, they think of companies like Google and Facebook – digital businesses built around using data to market to people.

Digital marketing and advertising is a small example of what data analytics can achieve and most companies looking to achieve business value and competitive advantage via data and improve their operations need to take a completely different approach.

Traditional companies which don’t exist exclusively online or solely offer digital services can harness the digital revolution, but their challenges are often very different to digital-only companies.

The reason non-digital businesses traditionally struggle with data projects is by taking a “too much too soon” approach. Lots of money is spent on big projects; expensive technology, data acquisition and platforms before a company has the people, skills or infrastructure in place to deliver them.

Often an employee with an interest in data is put in charge rather than bringing in someone with the specific business domain and data experience required.

As a result, projects have a habit of taking too long, costing too much and ending up with no-one knowing what to do with the result or failing to deliver the business case altogether.

Truly successful data projects on the other hand go through iterative, parallel delivery of incremental steps that improve processes. It’s this accumulation of smaller improvements, delivered in parallel with one another, and the quick-wins that drives larger transformational change for the enterprise.

Data initiatives should be brought into day-to-day business and be ongoing, continually iterating and evolving.

They should constantly be identifying the ways that data will improve the business, immediately embedding and operationalising that change so the improvements become business as usual, then looking for and delivering the next opportunity.

As well as this, companies shouldn’t assume that a new graduate with data qualifications (who might just want to work to Facebook) holds all the answers to their analytics problems.

They can’t just let people find interesting patterns in data – they need to start with an understanding of the business decision they need to inform – and that can’t be done by someone who may not understand the business or industry yet. It requires industry experience as well as experience of how data delivers value.

Companies wanting to benefit from data for such scenarios need to bring in data scientists with battle-hardened industry experience. Sending existing staff on a short course or hiring an enthusiastic graduate is not enough to solve these complex problems – they need people who truly understand the industry and what the data is telling them.

When looking for specific insights, people are required who understand the data itself from a first principles perspective, and who understand what the data tells them about the specific industry challenge they are looking to solve.

An interesting pattern might be the start of a new model – but application of a rigorous scientific method is then needed before it can become truly useful in scenarios where costly failures or human safety are at stake.

In summary; through a thoughtful and scientific approach as to how data projects are implemented, as well as bringing in the right people who fully understand how to derive business value from your information, businesses will be able to harness the data revolution.

If they do not take this approach, organisations can spend a lot of money and end up with nothing to show for it, with expensive capital investments in data analytics platforms which are not delivering value or a return on investment.

Failed analytics initiatives that are not delivering the much needed insights to determine the next steps a business needs to take.

Matt Jones is analytics strategist at Tessella

Image: Shutterstock

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