The true value of data
Previously published on networkcomputing.co.uk.
Joining the likes of agile and innovative, the term data-centric has become part of the modern business lexicon. Whilst the most cynical might consider data-centricity merely a buzz term, the facts are undeniable: businesses of all sizes are sitting on swelling data stores, with valuable business insights hidden within. More data doesn’t necessarily mean better data, and teasing out the genuine value can be a problem.
As well as the volume of data, variety is also important and includes customer, financial, machine, social, structured and unstructured forms. The types and categories of data that are generated, in ever increasing quantity, can be overwhelming, and determining what data is most useful for any aspect of business decision-making can be confusing. Of course, financial data is critical for the finance department, but to assume its value is limited to that one department would be myopic.
Invest in data-centricity
The inherent value locked away within data stores is not lost on businesses. Some research we recently conducted amongst IT decision makers made it clear that there’s already significant budget dedicated to extracting value from data, with a roadmap for further investment.
Currently, organizations have allocated on average $797,537 to operationalizing data. This is expected to grow to $1,132,013 by 2019, and $1,725,309 by 2023.
It’s no bad thing that businesses are prepared to put substantial investment behind becoming more data-driven, but it is important to make certain that these investments are appropriately targeted. In our research, over a third said that their company lacks a coordinated data strategy, which doesn’t present an ideal vision that this money is being put to best use.
Investment balance – Tech and people
Rather than risk throwing good money after bad, businesses wanting to get the most value from their data need to think strategically about how to allocate and apportion their investments. Technology is of course key here, and one of the main struggles for companies with large data stores is that it often exists in siloes across business units, with no reliable central store to inform cross-enterprise business decisions.
The data silo is not uncommon and is in many ways a symptom of how software use within the modern business has changed recently. As IT departments relaxed their rules on software use across departments, rightly empowering business units to acquire best-of-breed cloud apps to suit their specific needs, Software-as-a-Service (SaaS) applications have proliferated and are now commonplace.
A 2017 McAfee report found that, on average, enterprises are using 1,427 distinct cloud services, with the typical employee using 36 different cloud applications daily. With so many of these cloud apps being used, often in isolation, it’s unsurprising that data siloes result. One key area of investment to tackle this would be to ensure data integration tools are employed to connect and contain this SaaS sprawl and provide clear, consistent, quality data for the business to use.
Technology is just one aspect of data operationalization that businesses must contend with. In many businesses there exists a culture of data hoarding, with various departments mistrustful of sharing the data they generate with the wider business. If businesses want to organize their business model around data, this is something that will have to be eradicated and replaced with a culture of open data sharing.
Fostering an understanding and culture amongst the workforce around the importance of the free flow of data, coupled with technology to support better integration at the application and systems level, are good first steps. They are also essential for businesses that want to take advantage of their data and make better use of it when making strategic decisions.