How to do Data Management at Scale

As organisations mature, they acquire massive volumes of data. This, in turn, provides huge opportunities to optimise an organisation’s processes and drive further growth. 

But many organisations struggle to manage their data so that they can get the most out of it. When data sets, lakes and tools become extensive and wide-ranging, companies are faced with difficult choices. The good news is that through intelligent data management, large-scale financial services organisations can get the most out of their data while providing a better experience for users. 

Nurture your data

Data management is difficult and time-consuming. Processes tend to rely on staff “doing all the doing” and without the right support, the output is suboptimal. As organisations grow and departments mature, this approach doesn’t scale very well. To make matters worse, allocating personnel to data management diverts resources away from higher-value activities such as sales, strategy and management.

Clearly, data is valuable. But rather than think of it as an asset like oil or gold, it’s helpful to reframe it as a resource, like wind, or land. The resource should be nurtured in order to realise its true value, rather than have a static upfront value attached to it. Intelligent data management does precisely this – it nurtures your data so that people in your organisation are empowered to use it to infer quicker insights and make better decisions. This is the hallmark of a successful data management process. 

A wasted opportunity

Data management tools offered by vendors promise solutions, but they tend to be focused on capabilities, rather than users. Yes, they can fix specific problems when it comes to data management, but they’re not domain-specific, which means they fail to address specific problems and they hardly embed within business-as-usual activities.

Integrations are another problem. Different tools are no good at talking to each other, meaning that users have to switch between multiple platforms, repeating brainless tasks. An assortment of different data structures and technologies deny users a holistic view across end-to-end data management process. 

The net result of these failings is that users are failing to derive insights from their data that can drive positive outcomes for the organisation. Not only are staff frustrated with the fragmented and inefficient approach to managing data across multiple repositories – they’re prevented from realising their true potential.

The future of data management

Organisations are often unsure where to begin when it comes to rethinking their approach to data management. Inertia sets in, and as they continue to scale and data becomes increasingly fragmented and disordered, the problem intensifies.

Truth be told, solving the formidable challenges we have outlined when it comes to data management requires more than sheer determination and a willingness to adopt new tools and technologies. It requires a multi-faceted approach that enables managers to deploy solutions to address several needs, depending on operational imperatives. These may include one or several of the following:

• The issues you’re trying to address;
• The value you are trying to realise;
• The tools you are using; 
• The architectural requirements you have.

In order to stop seeing data management as a burden and start seeing it as an opportunity, you need to think carefully about who you work with. Yes, data management expertise in the market is important. But the technology landscape is evolving, fast. It makes sense to seek out partners that can offer full-stack competencies bringing together machine learning, information design and UX to help you realise the true potential of your data.

Our solution to this is Intelligent Data Management, delivered via a portal that offers a single access point to the data management tooling landscape. It’s designed to serve each role in the data community through streamlined journeys enriched with machine intelligence, facilitating data management that’s more efficient and user-friendly.

The right data leads to the right insights

Effective management of data is just the beginning. The key is inferring actionable insights from data. As we have explored in this blog post, one of the fundamental challenges when it comes to with data is its sheer magnitude, and how to scale across a business. In order to interact with the data, driving value and decisions from it, the right data needs to be presented with the right insights, at the right time, to the right people. This can’t happen if all your data is situated within a colossal data lake or siloed by different departments.

Only by focusing on end-users – understanding what they want to achieve within the constraints of unique business environments – can we challenge the perception that data management at scale is messy and complicated. That’s why we’re building the next generation of data management, one that is fundamentally user-centric.

By adopting intelligent data management principles, processes and tools, large organisations can become more self-aware, more scalable, and more profitable.