Client Data Maturity


The power of data is changing everything. Buzzwords such as artificial intelligence, machine learning, blockchain and ‘big data’ are everywhere and are quickly attracting the attention of corporate investment. Data is transforming every industry and Asset Management is no different.

Client data is being leveraged to disrupt traditional distribution models of old and Asset Managers are beginning to realise the true comparative advantage that this data can provide in directing Sales, Marketing and Client Service activity. Whilst some Asset Managers are finding success in this endeavour, many others are quickly realising that their data management practices are immature and the data available to them is less than optimal to produce the quality directional insight desired.


  • Asset Managers are now focusing on distribution enabled by technology as digital platforms are cheaper and more efficient than manual resource-intensive solutions
  • Data analytics, business intelligence and sales effectiveness are fast becoming increasing areas of investment and focus for Investment Managers looking to transform their existing distribution model
  • Data underpinning analysis must be accurate and consistent in order to prove trustworthy in driving business decisions  and providing reliable insight
  • Many Asset Managers are quickly realising that the quality of data being captured across their Distribution teams is not consistent or accurate or complete, which in turn means that the investment they are directing into new technology is not providing the return on investment desired

Why is it important for our clients?

  • Distribution teams must be able to easily capture, store and manage their client data. This is enabled through well embedded business processes that are supported by technology, but ultimately underpinned by a data management framework. It is important that this rich dataset is constructed and constantly developed as the basis for insights to be drawn from and value from investment in CRM and wider distribution systems to be delivered
  • It is also important to understand that quantity of data does not equal quality of data. Data that is captured across the organisation must be well stewarded and effectively managed so that the accuracy of the data can be relied upon
  • Once a well-established data management framework is in place, 3rd party datasets and industry data can be introduced to augment existing client data to help Asset Managers develop a complete view of their clients. This will enable effective and targeted distribution activity such as advanced segmentation based on AUM, flows or engagement
  • For analytics to remain accurate and reliable, data must be constantly monitored. A dedicated data management function or team must be accountable for the continuous data testing to assure the on-going quality of data. This function is also a key point of connection with internal Legal and Compliance teams to ensure that the all regulatory requirements are met and that data quality and supporting data governance practices would pass a regulatory audit

Key challenges for investment managers

Alpha is helping investment managers to assess their current data maturity and supporting organisational data management frameworks, to ensure that the data captured is reliable and can be effectively utilised. Several challenges are frequently seen across our client base:

  • Ownership of data and ensuring that business teams are accountable for the completeness and on-going stewardship of their own data
  • Defining key components of an appropriate Data Management framework for their organisation and operationalising this alongside existing business processes
  • Understanding their own data architecture and landscape including the mapping of data flows across distribution systems and technologies.
  • The on-going management of data testing and quality assurance from any data migrations and 3rd party datasets that have been integrated into the existing data model
  • Embedding a data governance structure that ensures data is monitored, maintained and accurate in a constantly changing client landscape
  • Understanding and preparing for regulation, such as GDPR which often throws additional complexity into data management policies and the way in which teams capture, store and retain both new and existing data
  • Instilling best practice ways of working between the business teams and supporting technology and application teams, to ensure that correct mechanisms are in place to aid the capture of new data and remediation of existing data inaccuracies

How can Alpha help?

  • Data Maturity Assessment: Review of current data model, including mapping of current systems, integrations and supporting architecture and evaluation of the quality of the underlying data within each
  • Data Management Framework: Development and embedding of a best practice data management framework that underpins all ways of working across Business and Technology teams and provides a rigorous governance structure to ensure core principles are adhered to
  • Regulatory Subject Matter Expertise: Advise on existing and future regulation to help guide decisions and operational responses to key industry regulation
  • Data Remediation Reports: Creation of tailored analytic reports to support data review, audit and remediation activity to help ensure the on-going quality of data
  • Strategic Distribution Review: Analysis of data sources available help build broader and richer datasets that will a provide more comprehensive basis to drive decisions from
  • Future State Operating Model Definition: Review and optimisation of distribution processes throughout the annual cycle and definition of a future state operating model to achieve value capture through the data available