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Wednesday, 03 February 2016 18:08

The Wolf of Data Street: ROI from big data projects

Posted By  Vic Winch

Top tips on what it takes to obtain quick results from big data projects from Teradata’s Big Data expert Vic Winch

Despite the many benefits that data exploration and analysis have to offer, big data projects at large organisations are frequently being pushed back by petty internal arguments over technology and data management. The result: stalled projects and a slim chance for a speedy return on investment.

As in many areas of business, success in big data projects requires both skills and contributions from within an organisation, alongside the expertise of specialists. Thankfully, these specialists are only ever a call away for organisations that are big data dreaming.

Using a consultative approach, such specialist teams don’t simply buy technology tools and expect IT to start printing money. They collaborate with organisations to ensure the wider industry challenges and specific problems and opportunities are fully understood from every angle. They are fully focused on the right objectives for the business before they deploy a tried-and-tested methodology which can be marched out with each project to achieve victory.

Early discussions will focus on the heart of the issue – the business’ problem and the best way to address it – leaving the technical demands for later. This will produce a statement of the problem, followed by identification of the analytics required, the data needed for it and the subsequent action required to deliver results.

With experience in a wide range of industry and enterprise-specific challenges and opportunities, established professional services partners are wholly aware that, while data visualisation and analysis provide the flash and bang to projects, they won’t provide a return on investment until the organisation has the information necessary to call them into action.

Knowing your priorities

Armed with an understanding of the organisation’s challenges and context, the team can then draw up a battle plan, starting with defining the areas of greatest potential value. Like any great strategists, the team will make planning a collaborative effort, beginning with bringing together the best-of-the-best, including specialists from relevant industry sectors and company staff from within IT and business for one to three day workshops.

With an A-team assembled, they can begin to tease out problems and challenges and establish the top business priorities. Part of this – perhaps a day – could be allocated to conducting a more technically-oriented and detailed examination to understand the data and resource constraints.

If, for example, an online retailer was facing a problem with abandoned baskets, the team would focus on data that could zero in on the cause. With this information, they can draw a plan of attack. In this case, the key could be establishing the value of abandoned baskets before working out the value to the business of improving the percentage of customers progressing to checkout.

For reference, this information can be pulled together and presented in a business improvement matrix, which describes the challenge addressed and the positive effects of solving a problem. In addition to setting out the challenge and solution, a clear ROI statement would focus minds on the joint aim by defining the value that improving checkouts by a set percentage would be worth.

This emphasises to all involved that the purpose of the entire project is to deliver quantifiable ROI.

Shifting gears with a solution

Once data engineers have analysed and structured the data for analytics in potential projects, the next step will be to take these ideas for a test drive.

This is likely to include running a hackathon in which the idea can be submitted to rigorous testing over three-to-five days and will typically involve business analysts who are familiar with the tools, discarding unreliable or inefficient ideas.

In terms of technology, a powerful, ready-to-run platform can play an important role in this phase by allowing data to be stored in its raw form ready for use with analytical tools. Ideas can then be trialled quickly, using a fail-fast approach to solving problems and exploiting opportunities.

When testing these various concepts, big data appliances and analytical platforms which allow businesses to carry out quick analyses can also provide further insights through visualisations, contributing to crucial conclusions.

Finally, as the project nears the finish line, the analytics will gather pace and be conducted in agile bite-sized chunks using proven implementation methodologies to take the solutions to market quickly and effectively.

The right stuff

To get the most from big data projects, it is not a matter of deciding which tools to install. Indeed, smart organisations are increasingly realising that obtaining fast ROI on big data projects does not involve acquiring the latest technologies or giving specialists an open-ended brief – it is about maintaining a clear focus on solving business problems.

Since they understand common industry challenges and are committed to identifying business-specific opportunities, a multi-skilled team will help steer the project towards the optimum data and analytics, use developed methodologies and work with an interactive, agile approach.

When there are so many new technologies – not all of which are mature or intuitive – making the most of the skills and experience of big data service partners and industry experts is how an organisation can back winning horses rather than ideas that fall at the first hurdle. Ahead of their competitors, such businesses can quickly spot the best way of delivering value fast, using their new-found agility to change direction swiftly and achieve the right business objectives.

About the author

Vic Winch is Director at Teradata’s Big Data Centre of Excellence. Vic has over 30 years’ experience as a database consultant, designer and vendor system professional. Over the years he has worked with the leading UK banks and retailers and is a frequent presenter at big data conferences.

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