With rapidly advancing technology, companies are gaining access to volumes of data that were once unimaginable. Today, businesses are looking for ways to utilize data to drive their strategies and improve performance.

On October 18, 2022, CFA Society Toronto invited three industry experts to discuss data-driven strategy and showcase data analytics practices in corporate finance, banking, and investment management. David Harris, Manager of Treasury Financing at Canadian Tire Corporation, moderated the discussion, which included the following speakers:

• Santanu Pal, Partner, Financial Services Analytics Practice at PwC
• Thomas Swintak, Associate Vice President, Decision Support – CTR and Real Estate at Canadian Tire Corporation
• Ken Sena, Co-Founder and CEO of Aiera

Three elements of a data-driven strategy 

The panelists highlighted three elements of a data-driven strategy:

1. Collecting the right data
2. Using the right tools to derive insights from the data to get business value
3. Delivering the insights to the right people at the right time to drive action

Collecting the right data 

Companies can only build a data-driven strategy by collecting the right data. With advanced technology, the universe of data has vastly expanded. For example, the data companies collect about their customers, such as through loyalty programs and credit card spending, may already provide them with valuable insights into their customers’ shopping behaviour. This, combined with third-party data on demographics, housing, and their competitors, enables companies to design a tailored incentive program to improve sales. 

In the investment management sector, artificial intelligence (AI) technology has granted investment professionals access to unique data sources that might have been impossible to collect in the past. For example, technology can automatically connect to multiple earnings calls simultaneously and provide the transcripts to investment managers. These data sources can give investors an edge whenassessing their portfolios. 

But getting the right data is more than data collection. Companies need to have a robust data strategy, including data use, storage, consumption, and governance, to ensure this valuable resource is protected and put to the best service.

Making data-driven business decisions

If the data is the fuel, then the technology and expertise companies employ to process and understand it are the engines. Many companies are developing their analytical capabilities by investing in advanced AI technology and training their employees. The panellists highlighted a few examples of the benefits of AI technologies in the banking and corporate sectors. 

Improve risk management and process efficiency for banks

Through data collection, banks can more comprehensively assess the risk profile of their credit applicants. This risk profile can help banks identify early warning signals and quickly detect fraud. Furthermore, banks can use customer data to offer customized financial products and the best pricing to enhance customer experience. Finally, banks use data to increase operational efficiency. For example, when choosing a location to open a branch, banks can leverage data on customer demographics, visits to banking centres, and the standard services requested to select the most convenient sites for their customers.

Use data analytics to select a location for a new store

To decide where to open a new store, a retail company uses AI technology to process both internal information collected through the customer loyalty program and third-party data to better understand information such as: 
– Where do most customers reside? 
– What are the housing projections for the neighbourhood? 
– What are the customers buying, and where?  
– Who are the main competitors in the region, and where are their stores located?

With data-based answers to these critical questions, management is better equipped to select the most convenient store location, offer the optimal product mix, and price competitively. 

Enhance research capacities through quantitative and qualitative intelligence

Improved technology can help research analysts collect data and construct basic financial models so they can focus on gaining deeper insights from their models. Additionally, the time saved on data collection helps them increase their coverage of different companies. 

In addition, AI-supported advanced analytics can help analysts process language and identify any uncertain tone from executives during the earnings call. This qualitative intelligence can help alert investors to information and sentiments not necessarily captured in the financial models. 

Action!

Powerful but often overlooked by companies, a data-driven strategy needs to include an efficient process to ensure that insights gained are delivered to the right people at the right time. To achieve this goal, data analytics should be embedded in an organization’s decision-making process. Panellist Thomas Swintak shared that advanced analytics technology and specialized expertise have transformed companies’ traditional financial planning and analysis functions. When evaluating past performance, executives rely on data analytics to better understand the root causes for poor performance and take swift action. Looking forward, data analytics can help them make evidence-based decisions to supplement their business intuition when pursuing new opportunities. 

Data is an asset that most companies already own, with potential waiting to be uncovered. Through examples, the panel showcased how companies can build a robust decision-driven strategy to capture the data, unleash its potential to transform the decision-making process, and ultimately achieve success.