Why Solving the Data Puzzle Is Key to Business Success

By Jonathan Westley, Chief Data Officer, Experian UK&I

Growth in data creation and replication soared in 2020, hitting 64.2 zettabytes. Considering that a single zettabyte is enough storage for 30 billion movies in 4K, or 60 billion video games, really puts it into perspective. It’s a lot of data.

The volume, velocity and variety of data is growing each minute; our world is rife with opportunity for data. If companies were not familiar with the potential of data before the pandemic, they are now. With businesses shifting to home working, and consumers seeing examples of how data is used in our pandemic response, we really began to appreciate the range and power of data.

But we’re neglecting the mass of data on our doorsteps. Less than 2% of the data created in 2020 was saved and retained into 2021. And while most businesses now collect data in some form, many are struggling to appreciate the potential of that data or hold it in formats that make it impossible to analyse.

Organisations don’t make the most of data because they’re missing two-thirds of the puzzle pieces. To optimise data processes and extract the insights that will lead to better decision making, businesses need to build a “three-legged stool”. These legs are data, technology applications (like cloud computing), and analytics powered by Artificial Intelligence (AI).

Barriers remain when it comes to putting this structure into practice. Everything from poor quality data and individuals’ distrust in data sharing, to a lack of skills in managing data is holding businesses back from accessing critical data insights.

These barriers are not insurmountable. But leaders do need to make a conscious effort to overcome them if they’re serious about optimising their data use. For some (4) tips that will kickstart your journey towards building that “stool”, read on.

Invest in data leaders and talent

For businesses looking to build strong data foundations, appointing a Chief Digital Officer (CDO) is a good place to start. Researchers have found that businesses with a CDO are twice as likely to have a clear digital strategy as those without. A CDO leads the charge on embedding a culture where data is seen as an asset that helps the business make informed decisions. They can also spearhead business responses to evolving data privacy regulations and policy, as well as lead strategies that will protect sensitive customer and business data.

Furthermore, businesses should put the time into understanding their current state of play for handling, processing, and managing data. Conducting a data audit is one of the most effective ways of identifying problem areas and shedding light on how data assets are currently being used. It highlights levels of data literacy in a business, where money is being wasted, and how data might be better strategically used to increase profits.

Identify opportunities for data use

It’s not easy to know exactly where the next market disruption will come from, but it’s a challenge business must overcome to remain future-ready. Understanding the scope of this disruption is not only good for business performance; it’s vital for staying ahead of competitors and adapting business models to accommodate customer demands. Be prepared to disrupt yourself.

At present, many businesses underestimate the range of applications for machine learning (ML) and AI within their field. Instead, many digital transformation projects focus on enhancing an existing business model. The more productive – and in the long-run, profitable – pathway would be to explore how better engagement with data enables your business to operate, innovate and grow in new and more efficient ways. A customer-centric digital strategy, powered by ML and AI applications, can help businesses to uncover customer trends before competitors.

Get company buy in

Without the support of the wider company, data teams will likely fall at the first hurdle. At times like this, we can fall back on the wise words of the Chinese philosopher, poet and politician Confucius: “Tell me and I will forget; show me and I may remember; involve me and I will understand.” A bit of leg work to demonstrate exactly how data applications such as AI and ML can benefit different company divisions may be required to get everyone on board. These aren’t just buzz words they are technologies that will change outcomes.

A simple way to do this is to look at a particular business function and highlight comparison points that demonstrate the effectiveness of AI and ML technologies compared to the status quo – or, indeed, alternative solutions. For instance, in financial services, it’s straightforward to show that AI does a much better job at determining credit risk than humans. Experian’s own analysis demonstrates that applying ML in credit decisioning can achieve a 25% reduction in bad debt over traditional linear regression models.

While it may be tempting to paint a promising picture of the company’s data-driven future, data teams should be wary of doing so. Setting realistic expectations with teams and leaders is critical for building long-term buy-in on a data strategy. After all, AI and ML applications likely won’t have perfect performance immediately; optimising them is a process of trial and error.

Build trust with your customers

As well as involving internal stakeholders, a data strategy must take external ones – like customers and consumers – into account too. Customers will be some of the first to question how data is being used, stored and protected by a business. A key part of establishing trust with them is enforcing rigorous frameworks around how data is managed and used.

Good data governance starts with being clear about what an organisation is trying to achieve. It should be outcome based, and it should be communicated in a transparent way. If what you’re trying to do is for the good of society or customers – that’s great, and that should be communicated too.

A good relationship with customers is vital if the data your business relies on is sourced from them. The effectiveness of AI and ML solutions is dependent on data quality, accessibility, and management. Rigorous and fair governance frameworks for data can help a business ensure that data engineering is tight and bias and unfair models are avoided. They also reassure customers, who are often sources of that data, that their information is in safe hands.

Putting the stool together

Having data is only one part of the puzzle. Optimising its use in a way that produces valuable insights for a business means investing in technology applications and analytics technology, like AI and ML, too. Eradicating barriers like poor data quality, data skills shortages and customer distrust is vital for helping these investments to take off. Only then will business have a sturdy stool to stand upon, which can be used to support growth and reap the competitive advantages the use of data provides.