Data management is an expensive, time-consuming, and distracting challenge for buy-side firms, and their technology executives are struggling to deliver cost-efficiency, compliance, and customer-centricity. Currently, many are evaluating outsourced or managed data services to help transform their ability to deliver. In this article, we summarize recent Adox Research survey data, which shows there is a compelling business case for automation based on a data-as-a-service model.
For many asset managers, data management is an in-house, on-premise function. But sourcing, validating and distributing market data is expensive and time-consuming. Instead IT teams are making the business case for managed data or data-as-a-service. The business case presented in the Data-as-a-survey 2019 survey report rests on three separate pillars describing the benefits in store from implementing a data-as-a-service model:
- significant direct cost savings of up to 20%
- redeployment of expensive human experts to more value-added tasks
- future-proofing of operating models and IT capabilities.
While the benefits outlined by these three pillars are not disputed, firms have historically found it hard to find budgets and to build a compelling business case for change. A shift towards customer-facing use cases and high-value data sets is however changing firms’ priorities and outlook, and promises to accelerate adoption of data-as-a-service.
A shift towards customer-facing use cases and high-value data sets is however changing firms’ priorities and outlook, and promises to accelerate adoption of data-as-a-service. Gert Raeves, Research Director and Founder at Adox Research
Digital and regulation are changing the game
For IT executives in financial services, there has always been a healthy tension between the conflicting need to avoid risk and maximize opportunity at the same time. In the past decade that particular dilemma is best captured in two secular shifts. First, the increased regulatory burden since the 2008 financial crisis has been relentless for banks and investment managers, which is confirmed by our data that shows that regulatory compliance to this day outranks all other business drivers when it comes to overall impact on IT strategy. The second shift is toward digital customer-centricity. Initially, digital transformation meant two separate things: changing existing legacy back-end technology to enable digital channels, and secondly, the use of digital channels such as web, mobile, and chatbots to service new and existing customer segments. Those two strands are now converging: increasingly, all customers are digital customers, and all processes and data are part of a customer-centric operating model.
Quantifying the benefits
In our survey, we wanted to focus on capturing hard data on the direct cost savings: what level of cost reduction (if any) do firms expect to realize from adopting data-as-a-service solutions? The results show that adopting a managed service model will yield very significant returns: close to two thirds of firms expect to reduce costs between 5 and 20%.
Those are very impressive numbers, so why are firms not moving forward with adoption of a data as-a-service model faster than they are?
Fighting the fear of change with facts
Doing nothing or delaying change projects has for a long time been the default setting for many firms when it comes to data management. We provided survey participants with a menu of potential risks and concerns, factors in their de facto decision to date to do nothing, or to delay investment.
Doing nothing or delaying change projects has for a long time been the default setting for many firms when it comes to data management.Gert Raeves, Research Director and Founder at Adox Research
The cost of change is still the dominant factor. Data management solutions, however imperfect, are typically deeply embedded in firms’ operating and technology model. Change of any kind therefore implies ‘big everything’: big costs, big projects, big integration headaches, etc. So direct dollar cost is a worry, but the bigger challenge lies in building the case for change. Quantifying the benefits of data-as-a-service adoption is where firms need the most help.
Vanilla data sets are not enough to build the business case
All data is not created equal. Some data is, if not quite public domain, widely shared, non-differentiating and of mostly operational relevance. Data management efforts have typically focused on this kind of data in core reference data projects: security master databases, customer and counterparty IDs, and related data sets such as classifiers, identifiers, and security pricing. These data sets are mission critical as they are used throughout every part of the investment and trading lifecycle, and without them none of the core systems and applications in any bank or asset management firm can operate.
The wide and deep distribution of these non-differentiated yet mission-critical data sets has had a strong influence on firms’ perceptions of the data sets that are most important to them. Their focus on bottom-of-the-pyramid security master or pricing solutions has often been at the expense of paying attention to more business-relevant and higher-value data sets.
In order to find out if the perceived value of specific data and data set has changed, we asked firms which data sets were most impacted by low data quality, and therefore would benefit most from improved data management. The results show that data quality is no longer perceived as just a ‘vanilla’ data problem as survey participants are reporting that achieving high-value data for risk, liquidity, and performance is where the biggest benefits can be gained.
These survey results are good news for executives who are trying to build stronger business cases for data management, and show that the impact zone for investments is no longer only the predictable and unexciting world of operations, IT, and security master projects. Risk and trading desks are now key stakeholders, and they are focused on high-value and differentiated data sets. Implementing a data-as-a-service model appears unquestionably to be a strong answer to their concerns.