Unlike fine wine, most data do not age well. This is even more stark in the case of financial services companies because old data does not just lose its strategic and business value, it also becomes a liability.
Because of a reluctance to throw anything in face of cheap storage, old data fades into background and becomes dark and difficult to protect. But dark data still contains sensitive financial information about customers whose privacy might be violated if there is a security breach.
Jasmit Sagoo explains why constant pruning of data keeps it safe, relevant, and useful in this article from ITProPortal:
To fight the dark data problem, businesses must stop it at its source. Ultimately, dark data stems directly from a lax data management strategy. This is not a new phenomenon; indeed, it has long been an aspect of development culture in financial services. Historically, mainframe systems were siloed and when a new application was to be built it would be done in a separate environment. Unsurprisingly, the data these companies hold is now spread across many different databases found in the cloud and on-premises.
When data becomes dark, it is not because of negligence but the complexity of keeping it organized in deeply fragmented IT environments. Research shows that employees regularly struggle with an overabundance of data sources and tools, as well as a lack of strategy and backup solutions. According to our research, the majority (81 per cent) of organizations think their visibility and control of data is unsatisfactory and even more (83 per cent) believe it is impacting data security. Not only is this fueling the rise of dark data, it is hurting the ability of employees to find and utilize valuable data, resulting in missed business opportunities and wasted resources.
As data becomes more siloed and fragmented, it is harder to find, manage and protect. This is how dark data turns into a risk. To stop this happening in the first place, financial services companies must create data management strategies that accommodate both recent and obsolete data. At the same time, they have to resist the temptation of a ‘save it all’ strategy. Instead they should take advantage of new tools and platforms that can locate, automatically classify and manage data across multiple environments.
Data management policies should be put in place and enforced from the bottom to the top. This means everyone knows what the data types and formats are and where they should be saved at all times. But it is equally important that these boundaries are not too restrictive. Data is changing all the time, so standards too will need to adapt. Employees should be allowed some freedom of action as long as they stay within the goal posts.
