Inefficient data storage costs private sector firms large sums each year Reuters

Published by NetApp, an American hybrid cloud data services and data management company, the Data Waste Index has revealed shortcomings in the way UK firms manage and store data.

The costs to the private sector of poor data management amount to £3.7 billion per year, with 41 per of data kept by firms surplus to requirements. Similarly, a recent study by Lenovo and FT Longitude identifies data management as a core factor that contributes to the success of top firms.

Moreover, pressures to ensure effective data management also burden IT leaders alongside the need to deal with an increase in cybercrime. Amidst these pressures, the data management problem is only likely to get worse, with 61 per cent of IT leaders projecting growth in their data estates in the next year.

Why is it a problem when firms store unneeded data? One element of effective data storage is a focus on "carbon-curbing storage." Accordingly, 25 per cent of IT leaders noted that decreasing the carbon footprint of their firm was the greatest factor motivating the streamlining of data estates.

Matt Watts, Chief Technology Evangelist at NetApp, makes the comparison between data and oil, explaining that "when not handled with care, data – much like oil – can have a devastating impact on the environment." In 2023, prioritising data efficiency is a key step in meeting the demands of ESG principles, according to Watts.

Companies also face pressures from consumers to become more environmentally friendly. Watts mentions in the foreword of the report how the impact of business activities on the environment is important to retain the loyalty of customers and partners. Indeed, the protection and maintenance of customer relationships are key factors motivating leaders to ensure sustainable data usage according to the report.

Other factors include reducing costs and improving data visibility. In terms of cost, £300,000 is spent on data storage each year by the average organisation. Consequently, approximately £120,000 of that investment goes to waste every year.

Obstacles to more efficient data management

The NetApp report finds that only 33 per cent of data managers felt sufficiently resourced to identify and sift out unnecessary data. Furthermore, 48 per cent of IT leaders find keeping up with the demands of managing their data estates a "struggle."

The report mentions that a key reason why resource problems were a continual concern could be the broader turbulence of the UK economy, with labour costs, currency fluctuations, and material shortages highlighted as challenges facing firms.

According to a recent study by ManpowerGroup, staff with skills in IT and data are the most needed by employers amidst the wider global talent shortage. Indeed, receiving the support necessary to carry out data waste disposal was identified by 16 per cent of IT leaders as an issue complicating data waste disposal.

However, the policy is also crucial, with concerns over a "customer backlash" motivating 42 per cent of IT departments to avoid data removal. Other factors which complicate the task of streamlining data management include the fear of removing useful data, interdepartmental communication, differentiating between useful and non-useful data, and inconsistencies in data quality.

Access to Tools

The report explains how access to the right tools is one factor that can ensure IT leaders feel capable and motivated to organise data and erase unneeded information. Crucially, manual data sifting is not only "inefficient" but "unfeasible." Firms need smart solutions. However, IT teams are deprived of the digital tools they need to sift through large volumes of data, meaning they lack the confidence to address data management tasks.

The technologies rated as the most in-demand to increase the skills of IT staff include cloud computing, artificial intelligence and edge storage, with 53 per cent, 45 per cent, and 44 per cent of IT leaders respectively identifying these tools as useful. Also mentioned are machine learning and tape-based storage.