Data modernisation for AI

Prashant Dahalkar at Hexaware Technologies proposes a five-step plan for data modernisation to meet enterprises’ strategic AI priorities

Data modernisation has been high on the agenda for many enterprises in recent years. In fact, Deloitte research indicates that four-fifths of businesses have already started this journey. While at first glance this indicates businesses are moving in the right direction, on taking a closer look, the same research found that less than half are following specific, formal initiatives.

This means there is a risk that data modernisation could take time to complete and fail to support overall business objectives once it is finalised. 

Considering the important role data has to play in their plans to adopt AI, businesses cannot afford to leave data modernisation to chance. According to ISG, for enterprises, the top three strategic priorities for AI are creating a long-term strategy or roadmap, identifying use cases that build new revenue streams, and improving customer experience.

Achieving these priorities will require businesses to have access to large volumes of high-quality, relevant data. It is, therefore, crucial businesses follow a formal data modernisation programme, or they risk falling behind in AI adoption.

The five-step programme

There are many different parts to the data modernisation puzzle. To succeed, IT leaders need to step back, ensure all the pieces are in place, and then put them together in the right order. While many have already begun this process, IT leaders need to take the most strategic approach possible. By doing so, they can be confident their efforts will leave them with data that has integrity, is easy to access, and will enable them to be agile and responsive.

This approach will improve their decision-making capabilities and provide a competitive edge – in turn, playing a crucial role in helping businesses capitalise on the benefits AI can offer.

Here are five steps businesses can follow for a strategic approach to data modernisation. 

1. Define business objectives. The journey has to start with clearly defined objectives and goals. It sounds obvious, but as the Deloitte survey indicates, some businesses may have started without laying the proper groundwork. To give themselves the best chance of success, IT leaders need to set clear targets from the beginning. For instance, they must decide if the goal is to enhance analytics, facilitate better decisions, break down silos to improve context, or to ensure data security.
2. Assess the current state of data. Next, businesses need to thoroughly evaluate their current infrastructure, processes, and capabilities.  First, they must map existing data sources, types, and storage systems and assess quality, accuracy, and completeness. Also, then they need to review data governance practices to ensure compliance and evaluate their data integration, warehousing, and analytics tools.
3. Use business drivers to inform a strategy. The next step is for IT leaders to ensure their planned modernisation journey aligns with key business drivers — such as enhancing customer experience or unlocking operational efficiencies. Then, they should create a strategy that includes a clear vision for the future state of the data architecture and key performance indicators (KPIs) to measure success, policies and procedures for data governance, security, and compliance, and steps for data integration and management.
4. Identify technologies to put the plan into action. It’s crucial businesses use the right technologies and tools while modernising data, or there’s a risk the outcome won’t align with longer-term business objectives. Every business is unique, so there is no one-size-fits-all approach. As such, IT leaders will need to consider a mix of data integration platforms, cloud platforms for data lakes and warehouses, data quality tools, and advanced AI analytics solutions, and select those best suited to their needs.
5. Set a strong governance framework. Finally, businesses need to set a framework to underpin data quality, security, compliance, and accountability. This should include clearly defined roles, responsibilities, and ownership, as well as processes for data stewardship and data governance policies. This framework needs to serve a dual purpose: mitigating risk while also supporting the future expansion of the organisation’s data-driven transformation strategy. 

Achieving AI priorities

By following these five steps, businesses will have the data foundation they need to achieve their strategic AI priorities. They will have the raw ingredients to create innovative products, services, and business models to stay ahead of the competition. This will also enable them to fully understand customer behaviour and preferences and be more personalised and timely in their communications while protecting sensitive data to maintain customer trust.

Looking ahead, data modernisation will enable businesses to create long-term AI strategies and roadmaps. This means they can confidently move forward with properly trained AI models powered by numerous connected data sources, without the risk of compromising on compliance and security. 

Overall, getting data modernisation right will provide a crucial platform for businesses to become more agile and capitalise on the new opportunities AI presents. As these technologies continue to evolve rapidly, modernised data will give businesses the confidence to harness future innovations and remain at the forefront of their industries. 

Prashant Dahalkar is Vice President – Data & AI at Hexaware Technologies

Main image courtesy of iStockPhoto.com and Khanchit Khirisutchalual