“Insanity is doing the same thing over and over again and expecting different results.”
This quote, often mistakenly attributed to Einstein, can be applied with a significant degree of justification to the way in which many financial sector firms go about addressing their Data management strategy.
Broadly speaking, most firms today need timely and seamless access to data that can be rapidly onboarded, managed, and distributed to end user systems efficiently. The model should be proportional to their objectives, and focused on delivering information and insights that allow every part of the organization to be data enabled.
Instead, faced with poorly defined processes and a data landscape that’s constantly shifting, many data management professionals try the same thing over and over again – and unsurprisingly fail to get the results they need.
Continuing to invest heavily in a build strategy with ETL tools, data warehouses and Enterprise Data Management platforms, organizations find themselves unable to realize agile data management capabilities while costs and system complexity rise, headcount grows, and support issues proliferate. The approach is bad news for data consumers, who start to see data as a tax on their operations and change capabilities.
Many firms also employ overly restrictive data policies to meet the needs of their commercial models and internal controls. The approach in effect imprisons data and stops firms from extracting full value from their investments. This is material because data is expensive, and thanks to emerging players in operational data and alternative data the number of datasets that firms need to use are increasing.
Andrew Barnett, Global Head of Product Strategy at RIMES, provides some thoughts: “It’s easy to see why firms fall into this data management Groundhog Day – indeed, it’s a trap I myself fell into in previous roles. In my case, the options were limited, turning off previous attempts at data management was cost prohibitive, and business cases for investment in new models were tied to wider organisational change, typically for investments or mandatory regulatory programs.
“However, I sense that a real change is at hand. Organizations are differentiating via data as the value of insights generated by effective data management is commercialized. And data expertise is becoming more expensive to acquire as it moves from an activity based on processing data to one focused on enablement.
“As a result, many firms are looking to outsource their defensive data management activities, recognizing that operational data management is foundational to wider data enablement but not the core or alpha activity of their investments and distribution teams. Doing so means they can access a dynamic, fluid, and high-quality stream of data that frees them to focus on value.
“One recent survey suggests that 45% of asset managers are considering outsourcing data management within the next two years – more so than for any other operational function. These organizations will enjoy a significant competitive advantage in moving to this operating model.
“If your internal customers see your output as data rather than information, then it’s probably time to change your approach. Pivot away from technology-centric approaches and leverage the flexibility and speed of managed data services to enable data to become a strategic source of insight for your firm.”
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