data is hard to do well
Building a data platform, managing data as an asset and delivering value through analytics, visualisation and democratisation. These things are all part of the nirvana that is the data-driven organisation. Still, they are also really difficult, especially in combination. And they’re big. They take ages. If you get them wrong, you can be set back months or years of progress. It only takes a quick google to see headlines like “85% of big data projects fail” or “Why most big data analytics projects fail”.
From experience at BJSS, delivering a range of data programmes into large companies, we’ve compiled five key recommendations that will drive successful outcomes. Implemented, they will bring about repeatable, efficient delivery of data, with reduced risk, on a solid technology foundation, designed for users and the business.
Focus on delivering value throughout
Organisations invest time and attention in data because they want to generate value from it. The great potential benefit promised often prompts teams to embark on large, slow initiatives aimed at producing perfect results. Strong foundations are critical, but you don’t have to wait for them to be completed to start realising a return on investment.
We believe you can and should support the undertaking of analytics projects in parallel with an in-development platform or managed data initiative. This is especially important in the early stages of your data journey. The idea that you need to spend many months getting data into a fit state before you can reap the rewards of data science is a fallacy.
Instead, support your analytics teams to deliver alongside the platform build initially, with a roadmap to bring their solutions into the fold as you develop. Select early use cases that don’t require sensitive data, that work from data sources you can already access or require reduced effort to onboard.
A mechanism to ensure that value is delivered early is steel threading. More common in agile software development, this approach identifies a minimum effective thread of functionality, data, governance and exploitation, end-to-end, and delivers this into production use first. You build foundational aspects of the technology, onboard key initial data sources, provide a use case which generates business value and have a platform upon which to deliver further use case iteratively – each time building out parts of another thread.
Successful data journeys combine aspects including technology, governance, privacy, security, quality and exploitation. We see success when these are considered with a human-centred, service-oriented lens.
