Next-generation data technologies, artificial intelligence, machine learning and robotic process automation will transform a business that has good quality data.
Next-generation data technologies promise a lot. Senior leadership teams are looking at technologies such as artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA) as methods to increase efficiency and innovation within their organisations. As with the adoption of any technology, the true power and value of these tools can only be realised if the right foundations are laid.
Organisations that develop a strong data strategy in preparation for AI, ML and RPA will be well prepared for new data types, emerging use cases and quickly realise business benefits. As a result, they will extract a return on investment (RoI) from their data and these tools.
The CIO of a major retailer says data and technology teams are the internal disruptors of their business and market in: “an era of data-driven insight”. For this particular CIO, data has allowed the organisation to identify its most vulnerable customers and deliver a bespoke service to them during the worst of the Covid-19 pandemic. Being able to respond to major events is going to become even more important for organisations in the near future. The annual survey of business technology leaders, by search firm Harvey Nash in 2020, found that almost all of its respondents stated data was a ‘critical asset’ to their organisations, and the same survey revealed that next-generation data tools were high on the strategies of their CIOs.
Those blazing a trail with AI are already seeing benefits. One international insurance firm reports that AI is improving the organisation’s ability to spot financial crime: “We are developing the data and analytics environment to identify emerging patterns,” says the Group CIO.
“We are seeing the automation of workers, which will mean more jobs are augmented to make them more effective, and this is creating a new set of roles,” says Steve Bates, KPMG Global Leader for its CIO Centre of Excellence. The pandemic is expected to accelerate the adoption of AI, ML and RPA as the appetite for digital transformation continues to increase. “The decreased dependency on a human workforce for routine, digital processes will be more attractive to end-users, not only for cost reduction benefits, but also for insuring their business against future impacts like this pandemic,” says Cathy Tornbohm, distinguished research Vice President at Gartner. The analyst house believes 90% of organisations will adopt some form of RPA by 2022.
Data-rich organisations are keen to embrace the power of AI, ML and RPA, but their benefit can quickly become negative if the data is not prepared and optimised for these technologies; for example data silos prevent accurate analysis, which in turn can lead to the AI developing bias’, a common concern of executive boards and regulators. “Even as an AI strategy is formulated, budget secured, and talent attracted, data remains a significant stumbling block,” finds business advisory group McKinsey in a report on accelerating the use of AI.
Rita Sallam, distinguished VP Analyst at Gartner, states that organisations that fail to get their data strategy and preparation right ahead of the adoption of AI, ML and RPA, quickly find users, and therefore customers, don’t trust the outputs from these technologies.
Organisations need to analyse their data and be sure that the data they have for AI, ML and RPA will meet the needs of the business. Business journal HBR found in its research that just three per cent of organisations have data of the quality required for analytics by AI. “And unlike tools, infrastructure, or talent, a complete set of AI-ready data cannot typically be purchased,” the reason being that every business has unique use cases, which require bespoke data types, so no real market exists.
Typically organisations are sitting on data collected over many years, and they hope that AI, ML and RPA will find the gems hidden in their repositories. But the truth is, the diamonds are only revealed if the data is well prepared ahead of the implementation of these technologies.
“It is inevitable that consumer data will be in different formats and the data will be stored in many different ways,” says the Chief Architect of a major consumer goods manufacturer.
Therefore the data strategy and the architecture that follows will need to be developed with the use of AI, ML and RPA at the core. Only with a data strategy and the correct preparation of the data can organisations successfully adopt next-generation technologies. Together, the strategy and data preparation will, if necessary, work with legacy technologies, if these are still required by the business.
With the right data strategy in place, and having curated the data sets for use in AI, ML and RPA, organisations will be well placed to respond to new data types that are expected to enter the enterprise in the near future. Research by technology analyst house Gartner finds that AI will be expected to meet the rising tide of video, audio and text data as well as new emotional and content data types and even vibration information.
Organisations that prepare their data and strategy for AI, ML and RPA will see business benefits and a return on investment. CIOs that have adopted RPA report that their staff are spending more time with customers – the reason being that RPA is taking care of basic transactions and tasks. Tools like AI and RPA are delivering business efficiencies in enterprise IT teams, finance departments and procurement operations, all of which are vital pillars of the business, but reliant on high levels of process. Research papers from analyst houses and consultancies report profit margin increases of between 12 and 15% following the adoption of AI, ML and RPA tools.
Finance is often a great place for a business to start its use of these technologies. Cliff Justice, an analyst with accounting and business advisory group KPMG says: “AI has the potential to be integrated into every part of your future business operations,” making improvements to every part of the business, such as sales, production and marketing.