The majority of organisations know that data exploration and analysis can offer up many real benefits. Sadly however, it is a common experience that big data projects in large organisations become entangled in internal differences of opinion about technology and data management, destroying the chance for a speedy return on investment.
It is important to recognise that successful outcomes in this field rely on the same combination that delivers results in other areas of business. That means combining internal expertise and inputs with the specialist skills that only a multi-disciplined professional services team can bring, based on wide industry experience.
Instead of spending big money on technology tools and expecting IT to generate value from them, specialist teams work consultatively to grasp both the wider industry and more specific challenges and opportunities that the business faces. Only then will they deploy their tried-and-tested methodology which can be applied to each project in order to guarantee a successful outcome.
In the early stages, conversations will anatomise the nature of the business problem and how it can be addressed, rather than jumping in to think about the technical demands of the project. This initial phase includes a statement of the business problem, followed by identification of the analytics required, the data that is a prerequisite and the action that is required to deliver results.
With experience of working on a wide range of industry and enterprise-specific challenges and opportunities, professional services partners of any standing know that, while data visualisation and analysis are fascinating in their own right, an organisation will not see any return on its investment unless it can take action on the information.
Using expertise to assess the impact
Defining the areas of greatest potential value to the business is an important foundation for any worthwhile big data project. With this in mind, the first step should involve a business-focused workshop of between one and three days. This should involve specialists from appropriate industry sectors and professionals from both IT and business within the client organisation.
These are key sessions that need to tease out problems and challenges and set agreed business priorities. For this reason, part of the workshop (perhaps as much as a day) could be devoted to examining data and resource constraints at a more technical level.
Where, for example, an online retailer has a significant problem with abandoned baskets, the team would focus on the data that is required and available to help the organisation identify the primary reasons for such customer behaviour and enabling an appropriate solution to be created. In this case, the value of abandoned baskets themselves would be established, before working out the value to the business of improving the percentage moving to the checkout.
For ease of reference, this information can be collated and presented in a business improvement matrix, which establishes the challenge addressed and the effects that will result from enacting a solution to a specific problem. As well as setting out the challenge and solution, a clear ROI statement would explicitly quantify the value delivered from improving checkouts by a set percentage. This is a useful device for focusing minds on the objective.
It is not a question of using technology for technology’s sake. The exploration of challenges and opportunities with a clear and quantifiable emphasis on value and ROI makes organisations far better equipped to prioritise both the strategy and focus of big data projects and thereby achieve true business value.
An agile, fail-fast methodology
After data engineers have analysed and structured the data for analytics through a series of business-focused projects, the next logical phase of the ROI-driven big data project involves testing the idea against the data.
In a fail-fast approach, this is likely to include a hackathon or datathon in which staff will test out ideas over three-to-five days and will typically involve business analysts who are familiar with the tools, killing off ideas that fail to deliver or where the data is unreliable.
In terms of technology, the deployment of a powerful, ready-to-run platform can pay dividends at this stage in the project by allowing data to be stored in its raw form, ready for use with analytical tools. Ideas can then be tested in an agile way, using the fail-fast approach to both problem-solving and seizing of potential opportunities.
When proving these various concepts, big data appliances and analytical platforms which permit analytics to be conducted at fast speeds also provide insightful visualisations and contribute to important conclusions. As the project moves towards completion, the analytics phase will accelerate and be conducted in agile, bite-sized chunks known as sprints, using potent implementation methodologies so that the solutions are brought to market in the best and fastest possible way.
The smart approach delivers
Smart organisations increasingly understand that pursuing this course of action, rather than deciding which IT boxes and tools to install, is how they obtain fast ROI on big data projects. It is not about simply buying-in to the latest technologies or giving specialists free rein, but rather, having a clear focus on solving business problems.
An organisation using a multi-skilled team will find the experts bring their understanding of common industry challenges to bear while listening to business-specific opportunities. They will help steer the project towards the right data and analytics, use developed methodologies and nurture an interactive, agile approach.
When there are so many new technologies available – not all of which are mature or user-friendly – capitalising on the skills and experience of big data service partners and industry experts is what really helps to deliver value.
Organisations that adopt this approach can quickly home in on the best method of delivering value fast, reacting to insight and using their new-found agility to alter direction swiftly in accordance with business objectives and the requirements of their bottom line.
Vic Winch, Director, Big Data Centre of Excellence, Teradata UK
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