Thought Leadership

Four Ways Organizations Can Improve Their Data Management Processes

Data has become the lifeblood of many modern organizations. From predicting trends and identifying new opportunities to gaining a deep understanding of internal operations and customer requirements, it is vital that enterprises become data-driven to make informed decisions and keep pace with the pack.

Companies know this – research shows that 85% of organizations see data as one of their most valuable assets. It’s no longer necessarily a competitive advantage to be data-led but a requirement to ensure that you’re not left behind.

Thus, we’re seeing many entities using data in increasingly progressive and innovative ways. For example, there are data-led practices focused on improving the customer experience in order to improve customer acquisition and retention. Some companies are developing and analyzing databases to refine their marketing strategies, while others are leveraging critical information to optimize their internal cash flow.

Be it process improvement, sales growth, unlocking first-mover advantages, or many other uses, companies actively leveraging data can end up relying heavily upon a series of complex databases. And while these undoubtedly offer benefits, they can equally lead to problems if they’re not managed properly.

Indeed, suboptimal data management can have many adverse consequences, with poor practices posing a significant security risk.

There were a record 623 million cyber-attacks in 2021 – double that of 2020 – as threat actors recognized and aimed to capitalize upon the fact that organizations are capturing rising amounts of highly sensitive data. Failing to manage and protect this data properly can result in catastrophic breaches that can put customers and employees at risk, as well as potentially tarnish a company’s image beyond repair.

Additionally, if your business is not up to date on the latest data management practices, you may be in violation of data protection laws and regulations that could result in substantial fines. In the case of GDPR, financial penalties have been issued in the tens of millions of dollars in some cases, with British Airways (£183 million) and Marriott (£99 million) being prime examples.

Critical data underpinning intellectual property may also be at risk of being stolen and shared. And beyond the security aspects, there are also the issues of operational inefficiencies and data inaccuracies to consider.

If databases aren’t managed properly, they can begin to hamper internal productivity and lead to the use of outdated information. This may, in turn, result in crucial decisions being poorly informed, which result in extensive investments failing to deliver their expected results. The impacts can be substantial between costly expenses, ineffective and inefficient changes, and wasted time.

Here are four data management best practices to consider

With the exponential growth of data usage, data management can make or break an enterprise.

Given the challenges, it is vital that organizations embrace effective practices now as the volumes of data that are created and consumed continue to rise at unprecedented rates.

With the right processes and policies in place, firms can ensure that increasingly critical data functions become both easier and less time-intensive while also reducing risk and improving compliance with data-centric regulations.

So what should organizations focus on? Here are some practical steps to take:

1. Create a data inventory

The critical first step for any organization looking to improve its data management processes should be the establishment of an effective and evergreen,  data inventory. In mapping out data, firms can establish precisely what they have, as well as understand existing processing activities, data subjects, storage locations, and retention obligations. Data-associated risks can only be assessed when you’re looking at the full picture. If you don’t know what you’re collecting, how you’re collecting it, or how it’s being used, then it’s impossible to protect, manage or process data correctly.

2. Develop data retention protocols

With a data inventory in place, organizations can then begin to establish retention policies and consider what kind of data they need to keep and how long they need to keep it. It is important to involve various aspects of the business in this process, with departments such as sales, marketing, IT, and legal all requiring data for different purposes and different lengths of time. For example, some data needs to be kept on record for specific lengths of time for legal reasons. Other data may need to be preserved as evidence in litigation. Once departmental requirements have been outlined, retention policies specifically tailored to the organization’s unique needs can be created and implemented.

3. Dispose of data you don’t need

With all departments and stakeholders reassured and in alignment, firms can set about deleting vast amounts of unnecessary and redundant data that they may have kept and achieve best practices in the form of data and storage minimization. Data minimization stipulates that organizations must identify the minimum amount of personal data needed to fulfill a specific purpose. Storage limitation advocates that personal data should be kept ‘no longer than is necessary for the purposes for which it is processed. At its core, these protocols are about reducing risks. Simply put, data you don’t have can’t be breached.

4. Establish ongoing controls and automate

While this initial disposal process is important, it is just the beginning. Thereafter, organizations should maintain an up-to-date data inventory to ensure that data-associated risks are always limited. Of course, minimizing data manually in real-time would be highly time-consuming, resource-intensive, and costly. Therefore, businesses should look to tap into technologies to automate this process, maintain audit trails, update documentation and policies, monitor programs and undertake annual review procedures. Critically, AI won’t be subject to human errors while also completing tasks at higher speeds and with greater accuracy, freeing up employees to focus on higher-value activities.

Now is the time to enact data management transformation

Given the critical importance of data, rising cyber threats, and the potential impacts of non-compliance facing organizations today, it is vital that firms bolster their data management processes sooner rather than later.

Yes, data is one of the leading assets of many modern organizations, driving innovation and informing key business decisions. But keeping data beyond its regulatory retention period or its usefulness leaves entities vulnerable to harm in several ways. 

Those firms that fail to identify and address issues early on will sooner or later find themselves on the end of hefty fines or highly damaging breaches that can leave them faced with irreparable financial or reputational damages.

All firms find it difficult to delete data; the risks of not having the data you need always seem to outweigh the risks of keeping data you don’t. But the mentality that ‘it might come in handy one day’ has now been judged positively illegal in a growing number of jurisdictions under GDPR.

To avoid these outcomes, firms should seek to establish an effective data inventory. By fully understanding where your data is, what it consists of, and how it’s used, you can begin to confirm what your ideal data landscape will look like.

This isn’t simply about compliance. Indeed, there are several operational benefits that effective data mapping can unlock, including more defined roles and responsibilities to improve reporting practices.

From this base, it then becomes possible to develop logical and relevant protocols and policies that work for all stakeholders before enacting and maintaining them efficiently and accurately using automated technologies.

In following these four logical and necessary steps, organizations will be well-placed to transform their data management processes.


About the Author:

Nick Rich is Head of Corporate Engagement UK&I at Exterro, the leading provider of Legal GRC software solutions, where he is responsible for helping corporations reach their privacy, e-disclosure, and forensics investigation goals. Nick joined Exterro in August 2022 and has helped build out a robust partner network while focussing on key vertical sectors for Exterro’s e-disclosure and privacy products. Prior to joining Exterro, Nick was responsible for significant growth in the data advisory business at Stroz Friedberg EMEA and similarly at Grant Thornton, where he was responsible for data advisory in a fincrime investigative context. Nick has over 20 year’s experience in GRC, from business process re-engineering through records management to litigation support and data privacy. Recently, he presented and participated in panels at the 2022 World Litigation Forum in Amsterdam and the #Risk conference in London.