From Manufacturing to Mining: Asset Management Data Strategies Reach for ‘Knowledge Alpha’

Olivia Vinden, Emma Haffenden, Biff Sharrock


Research across sectors highlights how banks and asset managers have been slow to recognise data as a source of business growth and sustainability. One study, looking at data science capabilities, shows Finance ranked last out of ten industries in data visualisation, and only sixth in machine learning (World Economic Forum, 2019).

Market uncertainty, fee compression and flows into lower margin products are driving forces behind the need of asset managers to boost their competitiveness and simplify the operating platform. Alpha created a survey to measure asset managers’ specific progress in establishing an effective, advanced data operating model. Our research explored how far asset managers have come in establishing foundations for data management, across both technology infrastructure and governance. To understand data maturity across the business, the survey addressed a broad range of topics through all aspects of investment management, looking at Investments, Distribution, Operations and Compliance. In total, 33 asset managers participated from Europe and the US including 11 firms with headquarters in the UK. This article presents our key findings and the main themes from the research.

Time to Go on the Offensive

Our research found that asset management firms are lagging on the data maturity curve, with most firms describing themselves as ‘Getting Organised’ (60% as shown in our Data Maturity curve below). Whilst the industry has increased levels of investment and focus on data, we have yet to observe any firm describing itself as a ‘Data Innovator’. In fact, a legacy of tactical data initiatives in functional silos has left many asset managers feeling ‘Frustratingly Fragmented’.

According to Alpha’s Data Strategy Index, a proprietary measure of data ambition, large asset managers are ‘on the offensive’ with data, and without significant investment mid-sized firms risk being left behind.

Against a backdrop of market uncertainty, the industry is bracing itself: 64% of asset managers had already adopted a more defensive strategy (State Street Growth Readiness Survey, 2018). However, our research indicates a significant shift is taking place – firms are starting to recognise that an offensive data strategy is needed to capture growth in competitive times. Leading firms will be those who build data intelligence and skills into their culture; going beyond data operations that are based on minimising risk to the business to also invest in client and investments focused analytics and A.I. capabilities.

From Fragmented to Organised

Asset managers want to leverage data to improve their processes, but they lack commitment to define a consistent approach to making this happen. Firms are deploying an ever-growing range of disconnected BI & Analytics point solutions across their technology estate, and smaller and mid-sized managers often struggle with user adoption, training and exploitation of analytics insights as a result.

Another gap our research has found is that 45% of the respondents do not have a defined Enterprise Data Model. A full Enterprise Data Model defines conceptual entities and the relationships between them, adding a valuable layer of detail beyond that available in a domain/attribute model. This better supports standardised, agile creation and consumption of data sources.

Governance and organisation is another pillar that firms are yet to establish consistently. Having a single party responsible for data helps to avoid data silos and can help drive enterprise-wide value from data initiatives. Currently, data is functionally owned across 73% of small asset managers, while only 27% of large asset managers are fragmented in terms of data ownership. Mid-sized firms are possibly the most complex in relation to data responsibility, as none have filled a position of Chief Data Officer and almost half are still siloed by business function.

Bringing it All Together

A common theme our research has identified is the lack of a coordinated, consistent and robust approach to data management and governance. This is needed to underpin systems, tools and processes for companies to be successful in the long term.

Our research brought out positive signals about willingness to innovate and a readiness to accept new methods. Responses do not point to pessimistic outlooks or conservative attitudes – firms now need to unite their teams behind a common goal and agree a comprehensive roadmap on how to get there. The opportunity to capitalise on the value of data has long been ignored by many firms and has become the familiar ‘elephant in the room’.

To hear more about our experiences helping firms to accelerate their data journey and transform the data operating model, or to find out more about the research please contact:


A summary of the Data Operating Models research report can be downloaded here:

About the Authors

Olivia Vinden

Olivia has over ten years experience consulting in the financial services sector, and leads our FinTech & Innovation practice. Olivia has defined and implemented a number of different operating models and solutions covering the front, middle and back office, as well as client reporting and performance measurement, and has led a number of large transformation programs, especially in the outsourcing space.

Emma Haffenden
Senior Manager

Emma has 15 years’ experience delivering business and technology strategy consulting services to C-level Executives of the leading global asset and wealth management firms. She specialises in research, analysis, benchmarking and competitive intelligence.

Biff Sharrock

Biff is a consultant at Alpha FMC with a primary focus on the back and middle office integration following M&A transactions. His experience at Alpha FMC includes over a year’s tenure with the Benchmarking team and financial modelling, and work on a back and middle office integration project.