Driving Businesses Forward with Data: Why Operationalising AI is the Key

With vast quantities of data at their disposal, businesses are facing a significant challenge in processing, analysing, and optimising it and doing this in a timely manner

In fact, more than three-quarters of UK businesses take a fortnight to complete this multi-step process, meaning by the time they get to the data it’s outdated. This is a problem that is in dire need of rectification, particularly as in these uncertain times, businesses need quick access to data and insights in order to adapt to rapidly changing situations. Therefore, eliminating lengthy and time-consuming processes to be able to adopt a data-first approach to business is essential.

The following three steps can help businesses in this endeavour, allowing them to put the processes and tools in place to turn data into actionable insights and deliver value back to not only their business, but also their customers.

Implement a data strategy

For many businesses, the focus tends to be on overarching organisational strategies as opposed to their top business priorities. But, to overcome data-related challenges, businesses must redress this approach, beginning by removing siloes and simplifying any issues they are faced with. The most effective approach to creating a data strategy will be to tackle one problem at a time – looking at one business outcome and then determining how they can solve it with data, rather than trying to build a data strategy without looking at how the outcomes are going to be achieved.

In taking this approach, businesses should identify their top three to five priorities, then solve them one at a time. This will ensure organisations are focused and agile, and that the strategy solves the most pressing problems before moving on to lesser issues. With a cohesive data strategy in place, businesses will be able to access and analyse their data quicker, and turn that information into actionable insights.

Remove data siloes

As well as having an effective data strategy, businesses must ensure they have the right tools and solutions to allow them to derive value from their data. Currently, many organisations are being hindered in this by legacy systems and the data siloes they result in. This is slowing down the process of collecting and accessing data and turning it into actionable insights, therefore, removing the siloes created by legacy technology is crucial to be able to adopt a data-first approach.

Make use of AI and ML

While historically analysing data effectively required businesses to move it all into a centralised database, the speed and scale available from data platforms today means that businesses can keep their data where it is, but still access it in real-time. Adopting a data management platform that is capable of this will speed up the decision-making process and enable businesses to derive more value from their data. The insights gained can then be used to reduce risk or to implement cost-saving strategies.

By implementing a data platform that makes use of artificial intelligence (AI) and machine learning (ML) businesses will benefit from being able to analyse and gain insight in real-time. With startups disrupting practically every industry by leveraging AI, it’s vital essential that established businesses follow suit by leveraging and embracing AI to improve processes, become more data-driven and extract insights faster to deliver more value to their customers. This use of AI can offer organisations a multitude of benefits, including the ability to deliver innovative new services, create new revenue streams and streamline business processes – all of which can help to improve the customer experience and help them gain competitive advantages.

Traditionally, businesses have had to spend significant amounts of time analysing data to gain insights, but ML makes this process much quicker. Systems that deploy ML will be able to take a business’ data and find out what it is about it that is interesting and relevant. In turn, subsequent action can be taken much faster than before. This would be valuable for all organisations, but it is even more visible within logistics settings where there can be a variety of optimisation problems, due to needing to move infrastructure around to meet customer demand and keep costs low.

Therefore, organisations in this sector would benefit from the use of ML to determine where the best place is to put products and how to get these products to the customer in an optimal way from the data they have at their disposal. This approach would enable the business to increase efficiency and customer satisfaction, while also potentially cutting costs. Evidence of this can be seen in research from Deloitte which identified that ML return on investment in the first year can range from around 2 to 5 times the cost, depending on the nature of the project and success of implementation.

Moving forward with data

As the amount of data they generate continues to grow, the challenges businesses face in accessing and using this data to the greatest effect are only going to worsen. Consequently, it’s vital organisations act now to get ahead of the problem and futureproof their business. With this in mind, implementing a cohesive data strategy and the right data management tools, such as AI, ML and APIs, must become a priority. This will enable businesses to leave their data where it is, yet still share this information across their organisation and derive insights from it in real-time. It is those businesses that adopt this approach that will be best able to operate adapt to rapidly evolving landscapes, stay ahead of the competition and deliver value back to their customers.

About the Author

Saurav Gupta, technical engineer, InterSystems. InterSystems is the engine behind the world’s most important applications in healthcare, business and government. Everything we build is designed to drive better decisions, actions, and outcomes for the people who stake their lives and livelihoods on our technology. We are guided by the IRIS principle—that software should be interoperable, reliable, intuitive, and scalable.

Featured image: ©Siarhei