How Businesses can Leverage Data More Effectively in 2024

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, WNS Triange‘s EVP & Global Business Unit Head Akhilesh Ayer offers commentary on how organizations can leverage data more effectively in 2024.

Business leaders have never had access to so much data and they continue struggle to use it effectively. In order to leverage data more effectively, various elements of the data ecosystem such as data quality, governance, privacy and security, data modernization, data democratization among other elements need to align and converge. According to the WNS Triange-Corinium research report ‘The Future of Enterprise Data & AI’, nearly half (47 percent) of those surveyed admitted that their data democratization is only ‘Moderately Effective.’

It is therefore essential that business leaders build a data strategy that supports the goals of the organization. However, the challenge faced by many organizations today is that much of the data they hold is in an unstructured format. Take the example of a leading insurer which had large amounts of data on underwriting, which was unstructured and therefore was not easily consumable. Cognitive intelligence platform was used to extract and contextualize the data and to summarize it. This helped to make it appropriate for decision making and insights generation as it was embedded into the company’s workflows.

Leaders need to adopt a data driven culture that puts data at the heart of decision making. But to enable this change, leaders and heads of department need to ensure that not only do they have accurate, timely data. In addition, they should be analyzing the data sufficiency and ensure that it is available in an easily accessible format to all relevant teams to be able to derive timely actionable insights from it.

Building an Enterprise-Wide Data Function

An enterprise-wide data function can provide a robust framework and structured controls, ensure integrity and quality. At the same time provides accessibility, discoverability and metrics for effective data usage while keeping security and privacy at its center. Data democratization can empower teams to take faster business decisions. Organizations can consider Solutions such as Data Mesh and Data Fabric as well as hybrid and multi-cloud-based systems tailored as per organization’s data strategy to become more agile and cost effective.

Using Data for Better Decision-Making

To make most of a critical asset such as data, Businesses must keep it at center of decision-making. Identifying the relevant AI interventions needed to do this enhances downstream analytics. It’s important to establish that the data ownership lies with the business user and is thus driven by the business as a whole, rather than just the IT function. This means engaging the business users at an early stage to identify and own the data they need for their own decision-making. Breaking down data silos and simplifying consumption of data to give teams access to intuitive, self-serve business intelligence (BI) as well as educating them on the effective use of data is also essential. What lies at the heart of better decision-making is having consistent definition of the KPIs across the organization. They also need to implement robust data governance and data ethics policies to ensure data integrity and success of their analytics and AI programs.

Businesses should also leverage advanced technologies to improve interactions with BI. Enabling business users with self-serve BI tools will help expedite decision-making. Gen AI can now be leveraged to provide user friendly features such as human-like querying capability and responses across text, images and speech.

Experimenting with data and rethinking its application can help to uncover innovative ways of solving business problems. Predictive analytics can enable businesses to run a number of potential scenarios to measure possible outcomes on everything from customer experience to ESG targets.  They should also be ready to scale up their use of data analytics. This involves testing hypotheses using proofs of concept and adopting a cloud platform-based approach for both the company’s products and services and its use of technology.

Business leaders should identify and implement predictive analytics and Machine Learning (ML) to understand historic business data and use it to identify future trends including new opportunities for functions such as sales, marketing, finance etc.

Identify Data Monetization Opportunities

Depending on market landscape, core competency of the area of business, nature of business data and quality of available data, companies can identify ways to monetize their data. This can be a new revenue opportunity for the business. To drive RoI businesses need to constantly identify such new use cases for analytics while ensuring that they align with the overall data strategy.

The data available to business leaders will continue its explosive growth – but this increase won’t necessarily bring in the benefits. It will only be those leaders who establish a data driven culture within their organization, who put data at the heart of decision-making and who create robust frameworks that allow them to experiment with data to develop new products and revenue streams will reap the transformational rewards.