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The world of work is radically changing. People have embraced remote and hybrid work, job vacancies and salaries are approaching all-time highs, and resignations are at record levels. Navigating this volatility can be a challenge for businesses of any size. We all know that high staff turnover can negatively affect the continuity of service to customers and slow down business operations. However, staff departures increasingly carry with them an even greater danger – data loss.
Data loss can mean physically losing vital information: for example, if it has been improperly stored on a departed staff member’s personal device; losing knowledge on how to access, collect or use data; or losing awareness that the data actually exists. It can cover everything from passwords to customer information and marketing databases, through to source codes, developer documentation and other critical pieces of business intelligence. The portability and abundance of data coupled with the decentralization of teams via remote working mean that the risk of catastrophic data loss grows with each team member that leaves.
Damage may not just be limited to a loss of information; there’s scope for both business functionality and brand reputation to take a serious hit. Not to mention, with data protection laws strengthening across the world, the very real risk of legal trouble.
Protecting your business against these problems really comes down to a business reassessing its relationship with data. The first step is recognizing that vulnerabilities do exist.
Legacy architecture and data management platforms
Many readers may be shocked to hear that many of the world’s largest companies use extraordinarily outdated or rudimentary data management solutions. We have often encountered organizations that use little more than an Excel spreadsheet to collect and manage some of their most sensitive data. These issues aren’t confined to big corporations; a significant proportion of startups also put data management low on the agenda. It is usually seen as something to sort out later — “when we’re a bit bigger.” However, the reality is that your data management infrastructure is the rock on which your business is built and loss is a very real risk. Complex analysis and information sharing are also underpinned by having a flexible, robust and open data architecture. It needs to continue to evolve in line with the scale and needs of your company and the advancements of data science techniques.
Taking a piecemeal or “one and done” approach to planning and investing in data infrastructure is where businesses become unstuck. It will ultimately be the main reason your business can be exposed to so much risk if and when staff members leave. As such, it is critical to continually audit your data infrastructure. The priority should be ensuring that the management and upkeep of your infrastructure is a responsibility shared across the company. This means having robust procedures covering how information is documented, cleaned, protected and analyzed. By sharing responsibilities across departments and adopting an ‘always in review’ mentality, you can quickly identify where there are gaps in your infrastructure or where systems or policies need to be reviewed. After all, what may work for your development team may be completely inappropriate for your marketing team. This brings me neatly to a big problem to avoid: silos.
Data silos create single points of failure
Information flow can be one of the most profound issues a company — big or small — can face. Insights, data and actions trapped in the brains of laptops or systems of one department can be invaluable if shared with another, but a lot of different factors can get in the way: culture, policies, tech infrastructure and a lack of skills or education (more on that later).
The Great Resignation magnifies the current problems because siloed data and insights are more acutely at risk of loss. All it can take is one key team member leaving and failing to accurately document or share crucial information.
An immediate way to tackle this problem is tasking your managers, IT and HR departments to work together to create a “data exit interview.” To do this you will need to first:
- Get existing team members to self-audit all the information, access, documentation and other insight that they are aware of, have access to or are in charge of.
- Combine this information with a company-wide audit conducted by your IT team to identify gaps either where responsibility of information is unknown or information exists that was previously undocumented.
- Task your HR and relevant managers with the creation of bespoke questionnaires for each team member to cover basic questions to be answered ahead of their departure on the location, status and access to the data they may have been responsible for or have stored on their devices.
This questionnaire should be conducted well ahead of a staff member’s final day at the company to allow for any unforeseen complications to be tackled and for your IT team to take an incremental approach to transferring controls and access. On that note, it is vitally important that access to all data and systems that carry company data are fully removed.
It’s worth remembering that no data exit interview will be completely foolproof simply because there are instances where people don’t know what they know, or, at least, may not consider it worth sharing. Think about it in the same way you would seek to preserve institutional knowledge — questions need to be probing and investigatory, and all the information needs to be captured and documented in such a way that it can be easily captured and shared throughout your teams. After all, an insight that may not mean much to your HR rep may mean something significant to one of your developers.
Tear down those (data) walls
Data exit interviews are a quick short-term fix, but long-term improvement and risk mitigation can only be achieved by tearing down all the data silos in your business.
As mentioned above, culture, technology and procedures all play a hand in erecting data walls within your company and all three areas need to be tackled. Going into a full de-siloing strategy would take a separate lengthy article — so I’ll quickly summarise some of the key steps that apply to most businesses:
- Audit your tech to determine the best tech stack — as we discussed regarding data management — and expand your audit to focus on all your systems and how they interact. API-driven, flexible, cloud-based solutions that can scale with your needs and speak to each other are often the best choice. Do not immediately go for a mainstream monolithic stack that may lock you into more than you need or won’t flex with your needs. Do also be very cautious about building your own solution or attempting to bend existing systems.
- Audit your data — either with the help of outside experts or your own data team begin tracking down where all your information is, the systems that are used to collect it, the people who are in charge of managing and analysing it, how it is updated and cleaned and crucially, what it is used (or not used) for.
- Review your policies — how appropriate are your data governance, ethics and utilisation procedures? The key is to ask how you can make data a part of your entire company’s decision making policy. Everything from major strategy decisions to the day-to-day actions of every team member.
The final step comes hand in hand with arguably one of the most important changes you can make to both protect and enhance your data knowledge — education.
Upskilling, education and training are the answers to the data question
In many companies, using and understanding data is the responsibility of only a handful of people. Not only does this exacerbate the risk of data loss when people leave, it’s also the single biggest impediment to a company becoming truly data-driven. Insights have lower value, staff members are not empowered to make their own decisions and innovation is limited to a select few “power users.” It can create bottlenecks and, if senior leaders are not properly skilled, can lead to bad company-wide decision making.
Approaching this issue doesn’t mean creating a whole team of data scientists. Training and upskilling is a broad spectrum and should be tailored to individual team members and departments. The first step is to make everyone understand the basics of data analysis and statistics so they can interrogate their own data and properly scrutinize insights. Next, it’s about giving your team the tools they need to maximize the role of data in their careers. Technology, managers and procedures should all enable and support this process. Simply training people once, then expecting them to become immediately data-driven isn’t going to work. It requires a mindset change where every action or decision should, if possible, be underpinned by data. Making it a habitual part of your business by, for example, including data skills in how you assess pay and promotion, helps to engrain it into your company culture.
Take it slowly but recognize the need to act
If you’ve got this far you’ve probably realized that the risk of data loss is a symptom of much bigger issues that companies need to grapple with. The more exposed your company is towards losing vital information from departures, the more likely it is that your approach to data is not fit for purpose. But do not despair. Recognizing and committing to tackling the risks is a great first step and the rewards you will reap will go far beyond protecting information. The key is to plan your approach and pilot your projects. Don’t run in and tear out all your data infrastructure or send your entire team on an intensive data training course. Start with what you can fix now and then experiment with your approach and carefully assess the ROI. It is much easier to take on bigger challenges if you know what does and doesn’t work for your company and you’ve already seen gains from smaller initiatives. Becoming data-driven is not so much a journey with a fixed goal but rather a mindset shift underpinned by constant evolution and innovation.
Natalie Cramp is the CEO of Profusion.
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