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Choose your own data strategy - why data trends are not always the right thing to follow

In today’s increasingly data-driven world, organisations are facing growing pressure to adopt strategies around data, machine learning and AI, keep abreast of latest technology and demonstrate that their data is contributing to growth and profitability.

Ivan Todorovic, data scientist, Capita Employee Solutions, and I have put together our thoughts on why your data strategy should start with the ‘why’ rather than the ‘how’.

The majority of data-driven projects are unsuccessful – as an example, Gartner predicted a 60% failure rate for big data projects for 2017[1], and the estimate was subsequently deemed “too conservative” and readjusted to 85%[2].

Technologies in the data world are developing at unprecedented speed and in a seemingly infinite number of directions. For an average organisation, even one with a dedicated data team, simply navigating through the field of vendors and their product suites is far from straightforward. Selecting the right approach, deploying the right products and actually obtaining useful results is an altogether different level of challenge.

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Simply following the latest hype is a perilous path to take. This approach can easily end up in an awkward set of tools that inflate cost and bring little actual value. Moreover, such an outcome would likely be a major setback for the organisation in its attempt to keep up with data trends and lead to even greater cost to get back on the right track.

A better approach would be for organisations to embrace the possibilities offered by data technology with a view to how data can improve their core business proposition while bringing value to end customers.

A key set of questions to ask could be: “What data do we have as an organisation, what do we want to learn from this, and how do we link the two?”. Once this has been defined, forming a working group including employees and management at various levels of the organisation can help define a robust process to collect; process; and output the desired data insights.

Organisations can then tailor the data process to fit their needs, while taking advantage of the multitude of solutions to pick and choose the right tools, based on criteria such as fitness for purpose, complexity, and cost.

By taking this approach, organisations with modest data capability can discover that starting with a small and manageable data process is an effective first-stage solution, which can progressively evolve into a sophisticated capability.

For large organisations with a multitude of business lines, the challenge is overcoming internal barriers to create a unified approach. By pooling data effectively from across the organisation, feeding results back to the business in a format that is timely, easily interpretable, provides insight that easily translates to action points. Here again the key to success is defining the appropriate processes and data architecture, combined with strong backing from senior leadership teams to cut through internal barriers. With robust processes in place and teams that are well versed in using the data capabilities available to them, even complex organisations are well positioned to adopt and implement new trends and developments in the data space that further advance their cause.

Although the path through today’s world of data technologies is a difficult one to navigate, organisations of any size can adopt a robust data strategy that avoids the trap of following the latest trend.

By choosing their path with core needs in mind, organisations can be set up for success in this dynamic field.

[1] Gartner press release (15th September) Gartner Says Business Intelligence and Analytics Leaders Must Focus on Mindsets and Culture to Kick Start Advanced Analytics

[2] TechRepublic (10th November) 85% of big data projects fail, but your developers can help yours succeed

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