The AI race worldwide is on, but businesses are neglecting the first piece of the puzzle – getting their data in order.
With worldwide spend on cognitive and AI systems projected to reach $77.6 (£61.4) billion in 2022, businesses across the globe have started to integrate AI into their systems in order to streamline efficiencies. However, it seems many are neglecting the first piece of the puzzle – getting their data in order.
In order for businesses to future-proof themselves by investing in new technologies, they first must ensure the data they are processing is of the highest quality. Without this, businesses will never be sure whether they are making sound, strategic decisions, especially if there was doubt whether the data they are processing was up to par.
And just as water is the foundational building block that supports all life on Earth, data is the key element in today’s business world and the basic building blocks of all products, services and customer interactions.
The challenge becomes much more sophisticated – knowing the underlying data is of pristine quality keeps companies out of court and reduces compliance failures, as well as
optimising processes, reducing costs and driving innovation. While data could appear as crystal-clear on the surface, it is what lies beneath that is truly important. In order to avoid making biased decisions based on superficial data, businesses have to take a deep dive in order to truly understand how their data works.
As a result of murky data quality, businesses can be left with dissatisfied customers, regulatory disclosure risk, and an inability to forecast earnings. In order to unlock further potential, drive innovation and allow for the flow of more revenue, businesses must address these issues in greater depth.
Take the recent high-profile public sector example of the Windrush scandal. Whereby the Home Office held incorrect data on individuals’ immigration status, who had actually legally moved to the UK years ago, starting sharing this data with other parts of the government, resulting in people wrongly being denied services or being deported. The National Audit Office argued that the Windrush scandal showed the consequences of relying on poor data, showing why departments must improve their handling of information in its recent 'Challenges in using data across government' report.
Data quality also impacts many private sector industries. Take, for example, asset management, according to research from State Street Corporation, the majority (88 per cent) of asset managers see data requirements as a challenge to their distribution strategies while almost half (36 per cent) agree MiFID II will prove a barrier to their cross-border activity.
So, what should businesses do to begin their data purification?
> The AI revolution is here: is your business ready?
> Create a plan: In order to ensure that the meandering flow of data stays on course, businesses must outline a series of structured steps that would be undertaken to keep their data of a high standard. A step-by-step data cleansing plan can create more transparency around an organisation’s data, such as where it originated from, how often it occurred, whether all of it is needed and who needs to access it. As a result, upon implementing such plans, businesses can cut costs and vastly improve efficiency.
> Use technology to do the heavy lifting: In the old days, businesses had to go through a lengthy manual process of intervention in order to improve data quality. But thanks to the emergence of analytics and machine intelligence tools, they can do the bulk of the purification process for us. Research what tools on the market would best suit your needs and budget and use them to make your and your team’s life easier.
> Carrying out an audit: The next course of action would be to carry out an audit to assess how relevant the existing data is to the business. For example, data that is required to boost sales will differ greatly from that which is needed for offering personalised services to customers or decreasing fraud. Equally, consider which customers and vendors you must run your business and which are no longer active customers – which you can leave behind. It is critical that you only focus on purifying data that your business actually requires when moving to a new ERP platform. Similar to the filtration process in water, whereby the goal is to remove impurities so that only clean water remains. The same principles apply in the transition to a new ERP solution – separating relevant data from what is irrelevant.
> Don’t cherry pick data: This is undoubtedly one of the easiest and most common mistakes companies can make. Of course, it’s natural to tend to look for the results you want to see and cut down on more unfavourable data. However, what this does is obscures the whole picture that is the data report. Objectivity breeds success and companies must include all relevant data — good and bad — in their findings in order to ensure that they’re due on the right course.
> Don’t forget the culture: If you want to maintain high-quality data, as an organisation you’ve first got to accept change. And then the real challenge comes in on maintaining this level of data quality. There’s no doubt that your business will continue to create new data. This is why attitude is extremely important in this venture as most employees and employers alike underestimate the importance of data.
> Assigning data advocates: One way of effectively reinforcing and shifting mindsets is to delegate willing data advocates across different teams. Education is equally necessary so that employees understand the importance of data quality as a driver of organisational success, else all will be sure to evaporate as swiftly as raindrops on a window pane.
> Businesses could be losing millions due to poor data management
> Bearing all these in mind, it is important to note that setting the bar too high can prove detrimental. Drawing from established best practices, comparable companies and industry standards would be the best way to proceed. Then comes the issue of compliance laws. While maintaining awareness of interface issues that could arise, companies must ensure that data is able to flow seamlessly between systems and businesses as well as with suppliers and customers.
> Data is the essential component of every business, and by following these steps, you can ensure that yours is efficient, relevant, and channels that all-important competitive advantage. And just like our water ecosystem, data ecosystems are precious – they must be free-flowing and not polluted with unnecessary rubbish. Then and only then, can you start to reaping the rewards of new emerging technologies.
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