Data Driven Investment Manager – Unlock the value proposition through Data strategy: By Vishal Rodage

DATA – A KEY DIFFERENTIATOR IN BUSINESS VALUE GENERATION

In today’s fiercely competitive market conditions, investment managers are looking at various ways to differentiate themselves from the competition. Having effective Data Strategy is critical to unlock the power of data that the organisation can harness
to its competitive advantage, be it to generate deeper insight into customer behaviour or to react in timely manner to the dynamic market conditions.

Investment Managers are turning to Data for competitive advantage and efficient business

Modern Investment Managers rely on reliable, optimised capabilities of data, analytics and AI to deliver business value to end customers and shareholders.  

Typical business drivers to build solid data strategy are 

• Accuracy of the data used in investments, risk management, product control and regulatory reporting
• Quick turn-around time on responding to business data needs, launching new products and rebalancing of existing products
• Cost optimisation by reducing reconciliation breaks, errors across fragmented data
• Generate Alpha on the investments while reducing the cost of management
• Improve client engagement by personalisation, digitisation and real-time reporting
• Shrinking shelf life of investment products and increasing investor demands leading to new ways for marketing, sales and distribution.
• Providing accurate and timely data to the front office to make investment decisions

 Data Headwinds for the investment managers

However, achieving these business objectives is not straight forward. There are bumps in the road.

54% of the investment managers are challenged by the errors in the data which is now one of the top reasons for the firms to invest in data strategy intitiatives¹

Some of the top data challenges faced by Investment Managers are:

• Improving accuracy and efficiency of investment risk management

  • Incorporating current data, handling large volume of data from disparate systems and linking data for accurate investment risk management is difficult

• Getting unified view of cross asset portfolios

  • Increased adoption of non-traditional assets and legacy siloed architecture making it difficult for portfolio managers to get consolidated, real-time view of the portfolio

• Improve customer engagement

  • Getting 360-degree view of customer is challenging due to lack of handing unstructured data

• Regulatory compliance and reporting

  • Evolving regulatory requirements on transparency, security and privacy demands accurate, timely data.

• High cost of processing data

  • About 50% of IT staff spend time servicing data request from investment managers rather than generating insights from the data¹

To cruise through these headwinds and achieve business objectives, investment managers need to become data driven.

However, what it means to be a data driven Investment Manager?

Investment Manager make effective use of Data as strategic tool in the full life cycle of the investment management process. Some of the key themes use by such modern Investment Managers are 

Process diverse data:  Systematically Integrate Diverse Data sources such as Traditional, Market, Alternative, Non-structured data into their investment workflows

Use of advanced analytics: Leverages advanced data analytics technologies to get meaningful insights to augment decision making

Domain Data Product: Transforms organisational culture i.e. moving from data as information report to data as product giving data ownership in the hands of business

Harness power of AI:  Generative AI, ML, NLP, Agentic AI in every step of investment management for efficiency, net new revue and customer engagement

Key to become a successful Data Driven Investment Manager

One of the key elements of the good data strategy is to have Singel Vision cross the organisation i.e. CTO, CIO and CEO are aligned on the vision, build data culture across organisation and embark on the transformation journey utilising disruptive technologies. 

Business stakeholders should have clear understanding of the business value to be delivered by data strategy which should be specific and measurable.

Technology, People and Process are three pillars on which data driven organisation is built. These pillars need to be built very carefully. 

Adopting data-driven strategy often requires not just technological enhancements but also re-evaluating existing operating model to facilitate better data integration and collaboration across functional boundaries. This necessitates need of robust Target
Operating Model (TOM). Following image shows key elements of TOM.

Data Governance

• Data Ownership
• Data Stewardship
• Data Protection and integrity

Data Quality

• Clean, Valid and Trusted Data
• Timely and Consistent Data
• Accurate and Complete Data 

Organisation Structure

• Focused Org. level team to manage data strategy
• Avoid siloed nature of data solution implementation
• Data driven culture and team with right skills

Technology

• Right tools for right data solution
• Adoption of Cloud computing
• Integration with legacy systems

Data operations

• Data Monitoring
• Data flow process management
• Access control management

Data driven approach across Investment management lifecycle

Effectiveness of the data-driven investment strategy hinges on the breadth, depth and quality of the data utilised. There are three main categories of the data

1. Traditional Data – Trade data, Financial Statements, Macroeconomic data, customer data, investment data
2. Market Data – Pricing data, Benchmarks, Trading volumes etc.
3. Alternative Data – Sentiment data, Satellite imagery, Supply chain/ logistic data 

Data Driven approach will ensure consistency, accuracy and efficiency across the entire investment lifecycle. Some of the usecases across the Front, Middle and Back-office functions are – 

Data-enhanced research and alpha generation 

• Predicting company fundamentals using alternative data e.g. web traffic on the e-commerce company
• Alpha generation – unique portfolio construction and optimisation strategies to generate alpha over benchmarks

Advance Risk Management through data analytics

• Risk Modelling using ML / AI using historical data and trained models to forecast market volatility
• Real-time monitoring of market risks exposures, counterparty credit risks, liquidity etc. using continuous analysis of real-time data feeds.

Optimising Trading and execution

• Transaction cost analysis (TCA) by analysing execution data to measure cost of trading.
• Smart Order Routing (SOR) using real-time market data to ensure best execution for the customer.

Transforming Client Reporting and Engagement

• Personalised, timely and insightful reporting with interactive, self-serve client reporting portals
• AI powered reporting in story telling style using NLP technologies

Ride the wave of AI and ML

Effective implementation of the data strategy ensures good quality; timely and reliable data is available to the organisation which opens door to the adoption of rapidly evolving AI technologies. 

At high level Investment Management industry is ready to take advantage of three core AI technologies – ML Models, Generative AI and Agentic AI.

Some of the business functions where these technologies can be used

ML Model 

• Product Management: AI powered Investment coach
• Application support: Predictive monitoring of the applications and FinOps
• Trading: Trading efficiency improvements

Generative AI

• Investment Research: Earning Summarisation, 
• Client Reporting: Portfolio Reporting in story telling format, Customer Service 
• Risk and Compliance: Summarisation of regulatory changes 

Agentic AI

• Investment Operations: AI powered automated trade reconciliations, trade allocation 
• Customer Services: Automated customer query and complaints resolutions
• Client Onboarding: KYC, AML and account opening using AI Agents

Summary

Data is key differentiator for investment managers, enabling product innovation, customer engagement, risk management and competitive advantage. Effective data strategy help drive business outcomes by leveraging data, analytics and AI capabilities.

However, investment managers are facing significant challenges such as data errors, fragmented systems, high processing cost and dynamic market and regulatory environment. To overcome these challenges investment managers should have robust data strategy,
focusing on business drivers, vision, technology and target operating model. This is supported by shift towards data driven organisation culture support from executives.

With the advancements in the AI technologies, there are immense opportunities to add business value across investment management lifecycle. Foundation of these technologies is good quality data and hence having good data strategy across the organisation
is key to the success.