Meet the Golden Master for asset managers
14 February 2018
In September, professional services firm Accenture brought onboard a team of research and benchmarking experts from consultancy Investit to beef up the business analytics skills of its asset management industry practice.
Its aim is to focus on helping asset management firms better benchmark, analyse and improve the performance of their business processes and technology and convert the general increase in assets under management to healthier revenue margins.
“Using our peer group benchmarks enables asset managers to make informed decisions for their businesses based on clear patterns and trends. We help them create stronger investment, product and marketing strategies,” states Matt Long, head of capital markets UK at Accenture.
Part of this new focus will be on unleashing the full potential of big data.
“Asset managers have always had an amount of personal data they could use to make investment decisions, but we are now, thanks to the growth of FinTech able to capture and aggregate quantitative meta data,” he says.
“That means alternative data sets which are based on consumer behaviour. So you can now have a mix of product types and structure data, financial data, credit and debit card data, search engine activity, GPS information, age and even weather. You couple all this together and you can use this information to devise more personalised products for investors. Big data can lead to a better informed human relationship based on no longer placing customers in pigeon holes.”
"Big data can lead to a better informed human relationship"
Anders Kirkeby, Vice President, Enterprise Architecture, at SimCorp, says “Market and reference data are cleaned up to form a a highly prized ‘Golden Master’ for asset managers. You can use it together with detailed transactional data for just about anything else in the organisation, for example optimising your future trades. it’s a piece of data that you have a high level of trust in. One area big data can also help to boost, in an increasingly regulatory environment, is compliance.
“By leveraging a rich audit trail and assessing what has changed and what hasn’t, firms can better handle reputational risk. You can also look for rogue trading patterns,” he states. “In an industry that is heavily regulated and with such thin margins, differentiating by improving customer experience is key. Behavioural patterns can be analysed which could, for example, help an asset manager identify which of their clients are likely to leave before they do. It can also help attract more digitally literate clients, such as millennials.”
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Long says the use of data from a wider array of sources will mean all asset management firms will need to create data capture platforms as part of their in-house data programmes.
“It’s starting to happen already with some companies investing in their information and data architectures. The machine learning technology which will really power big data analysis is emerging and will be on us quite quickly,” he states. “It will disrupt the traditional research function and the asset managers who are more technically advanced in consuming and interpreting this data – in essence robust data architects – will perform best. This will fundamentally force asset managers to take their data science abilities to another level.”
That is quite a shift as Long says at present asset managers are not renowned for their data strategy prowess. “They only really capture traditional sources of data such as public published information. In addition, at company level their digital strategies are based more around customer service and distribution than how to improve investment performance,” he states.
Kirkeby calls for the asset management sector to stop seeing big data as an IT project. “You need to democratise data access across the organisation to enable employees to create value from it,” he says. “Machine learning and artificial intelligence are important but without sufficient quality data you risk over interpreting a pattern as a causal relationship. To avoid that you need automation through a consolidated investment technology platform and treat data as a core asset and not just the by-product of a process.”