Around the world, many businesses fail because they’re not focused on what matters most – their data. Managing and protecting data in a meaningful way requires a slew of solutions, policies and procedures, all of which must form part of a data-centric strategy. Managing and protecting data is not only complex, but expensive in an ever-evolving business and technology landscape, with burgeoning data volumes, and one that has been made even more difficult by the accelerated work from home, cloud and digital transformation initiatives hurried in during the pandemic. One thing is clear. Changes are impossible to keep up with unless your data protection and management initiatives constantly evolve alongside the business.

Gartner’s hype cycle maps out a common pattern of five phases that a new technology goes through - 'the innovation trigger', then the 'peak of inflated expectations', followed by the 'trough of disillusionment', the 'slope of elightenment' and the 'plateau of productivity'. According to Andrew Jackson, Group CISO for Performanta: “We have this with AI and blockchain, and to a degree, data has also undergone a slower version of this cycle. The success of US technology giants in using purchasing data to determine the behaviour and desires of a customer, or locational data to provide time-sensitive data for mobile advertising, and the revenue it has generated, has mostly obscured the facts that data needs context. You often need large amounts of it, and other information, to create a valid supposition about something. The value of data is often immediate and then of little use. In this day and age, a lot of the data we have is in a legacy language and in disparate silos, so the ‘data sherpa’ is a job title nowadays, revolving around finding, getting, transporting and translating data so it’s useful. More often than not, data-led business cases are failures as, aside from aggregating the data, the people who create the business case often vastly overestimate the importance and relevance of the data – the business case often fails the ‘so what’ test. Since not all data is equal in value to a business, you have to think carefully about the provenance and validity of the data that you are using and what you want to achieve with the data.”