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Operationalising AI for a data-first approach is key for businesses

(Image credit: Image source: Shutterstock/alexskopje)

Now more than ever, businesses are facing a reoccurring challenge in processing, analyzing and optimizing the vast quantities of data they accrue on a daily basis. This multi-step process is typically taking 76 per cent of organizations in the UK a fortnight to complete, by which time the data is outdated – leaving them unable to make fast decisions to drive their business forward in rapidly evolving landscapes. Eliminating these lengthy and time-consuming processes is critical, however, many businesses are struggling to even know where to begin finding the data they need.

As adopting a data-first approach to businesses becomes increasingly important to be able to respond quickly and effectively to changes, organizations must look to overcome these data challenges. This requires them to implement a cohesive data strategy and the tools that will enable them to access and analyze their data faster and in turn, empower them to implement significant improvements to their operations.

Creating a cohesive data strategy

Currently, businesses tend to focus on more overarching organizational strategies, rather than their top business priorities, which usually over promise and under deliver as resources are overstretched. If businesses want to overcome their data-related challenges, they must begin by removing siloes and simplifying the issues they are faced with. To achieve this, organizations need to tackle one problem at a time by looking at one business outcome and determining how they can solve it with data, as opposed to trying to build the best data strategy without looking at how the outcomes are going to be achieved.

With this in mind, businesses should identify their top three to five priorities, then take and solve one problem at a time. This will benefit businesses by making them more focused and agile, ensuring that the strategy solves the most pressing problems they are facing before moving on to lesser issues. This approach to creating a cohesive data strategy will also go a long way in enabling businesses to access and analyze their data quicker, and turn that information into actionable insights.

Gaining actionable insights

Although historically to analyze data effectively businesses needed to move it all into a centralized database, the speed and scale available from data platforms today means that businesses are able to keep their data where it is, but still access it in real-time. Implementing 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.

Data is extremely valuable to businesses, however, the legacy systems most organizations have in place create a challenge in accessing this data. This leads to a delay between data being collected and the insights gained from it being implemented. If organizations are to adopt a data-first approach, then removing data siloes created by legacy technology is vital.

Deriving value from data with AI and ML

As part of their overarching data strategy, implementing a data platform that makes use of artificial intelligence (AI) and machine learning (ML) will enable businesses to analyze 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 organizations 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.

Previously, businesses had to spend significant amounts of time analyzing data to gain insights but with the help of ML this process will be much quicker. Systems that implement ML will be able to take the data and find out what it is about it that is interesting and relevant to the business. This in turn will enable subsequent action to be taken much faster than before. For instance, in a logistics setting there can be a variety of optimization problems, due to needing to move infrastructure around to meet customer demand and keep costs low. Therefore, organizations 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. In fact, Deloitte has found 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 a range of factors including industry and success of implementation.

Futureproofing business

Data is growing exponentially, with IDC predicting that the Global Datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes in 2025, and businesses aren’t immune to this phenomena. As a result, the challenges in accessing and using this data to the greatest effect are only going to worsen unless businesses take action to implement the right strategies and solutions. Therefore, organizations must prioritize getting their data in order, looking at the most important business cases and determining a data strategy accordingly before anything else.

From there, it’s critical that they put the solutions in place to better manage their data using more intelligent tools, such as AI, ML and APIs. This will enable businesses to leave their data where it is, yet still share this information across their organization and derive insights from it, all in real-time. The democratization of infrastructure with the use of AI, ML and cloud technologies, meanwhile, will allow businesses to deliver real-time outcomes at scale. Organizations that adopt this approach will be able to operate in line with the change of business pace and adapt quickly to new requirements. In turn, they will ultimately be able to deliver value back to their customers and stay ahead of the competition.

Saurav Gupta, technical engineer, InterSystems