The Pathway to a Future Free of Data Distress
Combat data distress with modern tools and connected governance. Discover the path to sustainable data management in complex environments.
Explore the challenges faced by data leaders in managing exponential data growth and discover solutions offered by Mary Anne Bullock, VP of Strategy at Solidatus, to alleviate data distress.
Want to read a familiar claim? The rapid growth of data in today’s digital age has reached staggering heights, causing data sets to skyrocket beyond imagination.
The problem with familiarity is that it breeds contempt. So familiar are we with the exponential growth of data and our reliance on it that we’ve come to accept it and the often-suboptimal technology we adopt to deal with it.
But we’re in danger of neglecting an all-too-important consideration, one that goes beyond data warehouses, superfast connections, and Big Data itself: the human cost.
This is the reality: the unprecedented challenges that data leaders are grappling with and the overwhelming pressure – whether from complying with burdensome regulations, demonstrating compliance, or ‘just’ making sure that the right people know where to find the right data and are confident that it is up-to-date so they can make the right decisions – is beating employees down.
Highly regulated and comprising large and complex organizations, financial services is one of the worst-affected sectors, and there are stats to back this up.
According to research by Solidatus, 71% of senior data leaders in the sector are on the brink of quitting their jobs, 87% say the stress impacts their mental health and wellbeing, and 80% say it affects their ability to do their job correctly.
Solidatus CEO and Founder Philip Dutton cites too many disparate and siloed data sources as one of the leading causes of data distress and warns that urgent action is needed to protect the mental health of senior data leaders.
So, what should be done for financial services firms and other large companies whose practitioners are struggling to stay on top of their data?
In short, organizations need to urgently deploy a better operating model supported by modern data management tools. This will enable them to cope with complex data environments and increase the simplicity, transparency, and understanding of data models, reducing the stress and burden on data leaders.
Quantifying the Data Stress Problem
But before we go into more detail, let’s quantify the problem. Why is data management anything but simple for the 300 senior data leaders in banking and financial services in the US and UK surveyed in this research? And how might their views explain the headline figures above?
The findings highlighted four chief causes of data distress. 82% of respondents chose reasons that we’ve categorized within the bracket of ‘data ambiguity and certainty,’ with ‘too many disparate and siloed sources of data, reducing confidence in its integrity and trust in it’ having the highest individual score of 33%. This was followed by ‘human factors’ at 49% and ‘business considerations’ at 41%. Almost a third (31%) chose the specific reason for ‘Risk of fines relating to data governance and regulatory compliance.’
Regarding why managing data for financial regulation is so time-consuming and stressful, 93% of respondents cited tech deficiencies, with ‘lack of data management tools’ being the single most significant option, chosen by 47%. Over half (51%) fell into a category we call ‘daunted by data.’ 27% cited human factors.
Finally, we’d draw attention to the resources identified by practitioners that could make their lives easier. We can’t do anything about needing more staff, which 25% chose. However, 75% also identified the broad automation category, with 74% citing tools for governance, provenance, and visualization.
See More: 3 Steps to Ensure Data Governance of Your People Data
Solving the Stress
We’re champions of connected governance, a way of operating that deploys a lineage-first approach. This starts with building a lineage model. This allows you to develop a data catalog, prioritizing areas of focus and the critical data elements (CDEs) within it rather than starting with your catalog. You can then identify data controls, develop a shared understanding, and set up a regime for monitoring changes before they impact outputs.
This proactive data management promotes rapid gains in knowledge and shared understanding, providing immediate clarity and focus on the critical data you need, as well as benefits in change management and a better basis for designing data for the future.
Much of the content and ideas will be familiar to experts in this area, but this is a fast-moving field, and good practices are evolving. Which brings us to the technology that’s fuelling this evolution…
No one-size-fits-all piece of software will alleviate all the stresses and strains of a data practitioner’s working day; if there were, everyone else in the tech space would have to go home. Instead, it’s about selecting the right combination of tools for the makeup of your team, your data estate, and your organization’s requirements.
This research demonstrates that, among other things, 75% of practitioners are clamoring for governance, provenance, and visualization tools.
Solutions that help with this, allowing you to assess the lineage of data in your existing setup, represent a big part of identifying and implementing enhanced elements of your tech stack.
This approach is a methodology in its own right; it also promotes better methods in your broader activities once you have the optimum systems and datasets in place.
Your organization’s trust in your data – the lack of which is the source of so much anxiety – can be regained, leading to better, quicker, and more reliable decisions and a hugely reduced risk of fines.
See More: Achieving Digital Agility Through Change Management
A More Relaxed, Confident Future
A lack of data trust decreases efficiencies, increases risk, and ultimately creates data distress. With such a seismic shift in organizations’ data and regulatory environments, a new operating model needs to be deployed, supported by modern data management tools designed to cope with infinitely connected and complex environments within financial organizations.
Organizations’ data management foundations and supporting structures must be replaced to enable simplicity, transparency, and understanding, thereby promoting implicit data trust. Only then will data leaders operate in a sustainable environment free from data distress.
How can organizations simplify data management and reduce stress among data leaders? Let us know on Facebook, X, and LinkedIn. We’d love to hear from you!
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