I would be surprised if there is an asset manager out there that doesn’t have data as one of its key priorities. Most likely, it is also one of its most expensive budget items. The use of technology in the investment industry has grown exponentially over the past decade. To different degrees, many of us in our industry felt a false sense of comfort that we had prepared ourselves for the ten years to come. Until we realised that a bigger challenge was about to dawn on us: the exponential growth and evolution of data.

For an industry already struggling with information overload, the proliferation of data has, for many, only served to turbo charge the problem while, for others, it presents an opportunity. Whichever side of the argument you fall on, the emerging landscape is leading to an evolution in the way asset managers are sourcing and utilising data.

One thing is for sure, this is a struggle that our industry will continue to face for the foreseeable future. There are a number of factors at play which are prompting a need for more data collection, and the subsequent adoption of tech-enabled solutions to process the data and make sense of it.

Firstly, the changing regulatory backdrop means that we’ve seen an explosion in the volume of data related to our own operations and investments, whether it is D&I and climate data on our operations, or broader ESG data on our investments. Asset managers have been realigning resources to comply with new regulations and disclosure requirements – both at a corporate and a product level. Companies have been acquiring and analysing data, and feeding this information into company-level and investment management systems and processes. More importantly, companies have had to learn to harvest their own data. 

“There are a number of factors at play which are prompting a need for more data collection, and the subsequent adoption of tech-enabled solutions to process the data and make sense of it.”

Secondly, in an increasingly competitive industry, the effective collection and use of data can make a real difference to the soundness of the investment process as well as to providing comprehensive client reporting. The range of questions that we at M&G have been asking ourselves (and we suspect we are not the only ones) has ranged from “how can we best harness this data to provide a richer array of inputs for investment analysis, generate differentiated insights, and ultimately create better investment outcomes for our clients?’’ to ‘’Can we create tech-enabled solutions to improve efficiencies and provide additional flexibility for our investment teams?”

As active managers at M&G, the human element in the investment process will always be critical to our success. However, technology plays a pivotal role in helping us create innovative solutions and scale up existing processes for greater efficiency. In essence, technology exists to serve a particular purpose – helping us translate data into actionable insights, more efficiently.

In our machine learning-based investment strategy, we utilise both the technology and the human element. We have developed algorithms which form the core recommendation engine, and we overlay its conclusions with oversight from our fundamental equity team. The aim is to leverage the best of human and machine intelligence in the stock selection process. And in the same way as human knowledge grows, also the data size and compute power have increased over time – just more exponentially. As an example, when we first started employing this strategy in 2018, the underlying dataset comprised 1 billion datapoints covering 10 years of history. This is now over 10 billion datapoints covering more than 25 years of history and growing every day.

We have also been developing a behavioural tool, using data analysis to reflect on past investment decisions in different market environments with the aim of uncovering potential biases in our decision-making. The goal is to improve the quality of our investment-decision making.

To allow our Private Asset investment colleagues to hone in relevant details when searching for information in stored company filings, a cross-functional team at M&G has integrated various AI-functionality (such as smart search and chatbot) using natural language processing (NLP) to improve efficiencies. We continue to build on this functionality and broaden the capability.

In addition, we can use natural language processing and other machine-learning algorithms to generate insights through topical and sentiment analysis and visualise trends over time.

In one project, we tracked company commitments to achieving net zero against emission changes over time and looked at the public perception of a company’s efforts versus progress in practice. In carrying out this type of analysis, with the help of technology, we were able to uncover inconsistencies and identify outliers from a large pool of company-relevant data.

Tech-enabled solutions can also help us deal with missing or inconsistent data. For instance, in our Private Assets business, we’ve developed a ‘Carbonator’ tool, based on a proprietary machine-learning model, that uses publicly-disclosed emissions data to help us estimate the carbon emissions of companies with similar profiles to listed companies (business model and operating activities), but where published ESG data isn’t readily available.

Importantly, ESG data represents a whole new challenge. This needs more interpretation at the source and, hence, creates the need for more tailored data quality assurance processes.

For example, differences and inconsistencies exist in the output of different ESG data providers. Also, while good progress has been made on company-level disclosures, these remain voluntary and, yet, not subject to any independent verification.   

It’s still early stages for the industry and, collectively, we are all on a (data) journey. Firms need to contend with questions about what they have done, or are doing, to improve the rigour of their own assessments of data validity and consistency. To bridge the gaps, data analysis strategies and tech-enabled solutions are fast becoming key tools in an investment manager’s armoury to meet the growing demands of clients and regulators.

For active investors like M&G, tech-enabled solutions have the ability to create efficiencies, while also allowing greater flexibility to generate differentiated insights.

While data and technology are powerful enablers, it’s what we do with these tools that will set us apart. The human element matters.