Asset Management Series – Data First, Data Second
Asset Management Series – Data First, Data Second

Global asset managers need to compete in an eco-system that is increasingly data-driven. Changing demographics and client expectations require leadership teams to re-think their target operating models to meet these new challenges. A holistic change and digital transformation program should become a priority to transition the asset management enterprise into a lean and agile state at an operational level. To facilitate the change program, it is crucial to think about operational agility and the technologies required to achieve the business objectives, efficiently, and with the least disruption.

Change and transformation programs can be challenging and typically, may face some internal resistance. It is vital that senior stakeholders in the front, middle and back-office buy into the program. Each functional area will have different challenges and priorities, so the overall alignment to the strategic objectives of the business is an essential element for a successful outcome.

“Asset managers are harnessing the tools, expertise, and infrastructure needed to turn data into actionable insights that can drive growth in investments and their investor base.” [Source BNY AM]

Data must be the lynchpin of the exercise. Data is the cornerstone and foundation of everything we do, whether in the front, middle or back-office. One of the biggest challenges faced by asset managers (and across the entire financial services sector) is the use of legacy technology and the disparate data sources used across the different functions of the business.

Data Example: Global demand by investors for ESG continues to increase, with AUM totalling US$40 trillion, up from $US12 trillion in 2012. [Source BAML]. However, one of the main challenges is associated with inconsistencies in the data used to research investments. Different data providers use different methodologies, have different calibration methods and may be subject to ad-hoc manual adjustments. In practice, this means that companies will have different ratings depending on which data is used.

Therefore, analysts and investors typically choose several data providers, extract this data, and then carry out their research based on internal best practices or different investment mandates. The ESG data should be centralised and harmonised from a central point. As it stands, data aggregation and harmonisation processes can typically be considered inefficient, primarily because of data inconsistencies, manual inputs, and sometimes poor data governance. This is just one area where intelligent automation would pay immediate dividends.

Data is an asset, and it is essential to set out the master data management strategy to ensure that the enterprise has access to a 360° view of the information required to run and grow the business. It is essential to know where it sits at a functional level, who can access it, and who can make changes. There are significant and tangible benefits of getting your data strategy right, as this is your starting point.

  • Harnessing data and storage into a data warehouse or lake will allow the enterprise to scale its operations across every business area.
  • Data represents value, and this data means de facto better-informed decisions across the investment process.
  • Management information and insights required to run the business more efficiently can be accessed in real-time.
  • A properly thought-out data strategy will allow asset managers to identify and reduce operational inefficiencies and risk.
  • Data should be used to improve client experience and lead to higher levels of employee satisfaction.

Agility also means speed, and consideration must be given to how you extract data from multiple sources while reducing errors that are often associated with the manual inputting of data. Further, it is operationally inefficient and costly to hire smart people to carry out tedious manual work that can be accomplished through automation.

A strategy for automation should already be an integral part of your digital transformation planning. We must not lose sight of the fact that change management must take process optimisation (from a business and functional perspective) into account when re-thinking the target operating model. To illustrate some of the benefits of automation, I have included some customer KPI’s that clearly show some of the benefits that clients have realised.

  • 95% Process compliance.
  • 100% Real-time reporting to leadership.
  • 75% Reduction in processing time.
  • 80% Reduction in manual tasks.
  • 65% Increase in productivity.
  • 60% Faster processing time.

As well as the obvious advantages of native automation technologies such as RPA, it is also essential to be aware of some of the common mistakes that occur across the sector.

BOT’s have typically been deployed in areas of the business to fix tactical automation needs. In essence, replacing humans who would otherwise be carrying out repetitive manual tasks. The advantages of automation to the enterprise are:

  1. Cost reduction by increasing operational efficiency.
  2. Increased levels of employee satisfaction.
  3. Processing speed, error reduction and consistency.

It is also important to point out that BOT’s do not constitute a significant capital investment.

Be Aware of the Limitations

  1. RPA is limited in future scope and can only take you part of the way on your digital transformation journey.
  2. To extract real value, you will need to think about end-to-end automation and what that looks like.

RPA should not be thought of as a panacea, given that you will probably be tempted to automate areas of the business considered to be the low-hanging fruit. RPA can be perfectly successful, but, in the end, it is likely that as a stand-alone technology, it will most likely fail to align correctly to your digital transformation roadmap. As you think about RPA, it is a great technology but does what it says on the tin, no less and certainly no more. As levels of complexity increase and you try to scale the solution, you will most certainly hit a roadblock.

If RPA is currently being used to automate some of the business processes, you may already be thinking about what next? To properly align the automation projects to the change programs strategic initiatives, you will need to consider the following:

  1. Start by being deliberate about which parts of the business process you want to automate so that the end-to-end process works cohesively.
  2. Building a centre of excellence which will help you to avoid one of the common pitfalls which is the creation of automation silos.

Then think about:

  1. Data orchestration
  2. Business process improvement
  3. Workflow
  4. Handling exceptions & rules
  5. Integration and use cases for AI

Once you start factoring in these elements, you are having to think about data orchestration and intelligent automation as they work together to build the end-to-end automation you need to accomplish your digital transformation goals.

For further details contact
David Landi – Head of Asset Management


VASS integrates the team and business of psKINETIC, the UK-based Intelligent Automation technology consultancy.  VASS continues to grow its international presence. psKINETIC, …

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