How Clean, Quality Data Unlocks the Power of Digital Transformation

Piece written by the SVP, EMEA North, South and Emerging Markets at Syniti, Chris Gorton

Global 2000 companies are looking to become more mobile and agile in how they work as the ways they serve their customers change rapidly. When it comes to digital transformation, companies are generally focused on four primary business objectives: reducing cost, increasing revenue, mitigating risk and sustainability. For all of these corporate goals, data quality projects can help speed and move these processes by layering over analytics and building in different models to support management and efficiencies.

Reducing costs and opening new markets

Obviously, getting things right the first time – whether it be with project delivery, removing waste or removing duplication of efforts – can reduce costs. But if you have bad data underpinning your projects, you’re not going to be able to get things right – and you’ll essentially be throwing money out the window.  Whether a company is experiencing an inventory problem, a supply chain program or some other business issue, 90% of the time it’s caused by a data problem. Ensuring you have good data quality is critical to helping pull off projects without a hitch the first time.

Data quality — and the processes that ensure it — is also at the heart of increasing revenue – whether it’s through new market opportunities, going to market with products faster, or performing a merger, acquisition or divestiture faster.

Data quality and risk mitigation

In terms of risk, many of these large organisations are moving to the cloud and looking to be more agile. This is to protect against the new wave of disruptors coming to the market that are stealing market share, which large enterprises must respond to. They are asking themselves, “How do we mitigate risks that impact our employees and customers?”

Data quality fits in with risk mitigation by helping ensure compliance, data security in transit and maintaining business operations without disruption. For compliance, in the EU, one of the biggest compliance requirements is GDPR, which is complex and can be a roadblock for a lot of companies that are either operating in EMEA or looking to come here. It’s important for major enterprises to be aware which data falls under personally identifiable information (PII) protections and to understand that not all data is equal.

Meeting sustainability goals

Becoming more sustainable is quickly growing in importance for many companies. Increasingly, they’re being incentivized to make their businesses and supply chains more sustainable.

A recent report by McKinsey Research, for instance, found that supplying the goods and services to enable the transition to net-zero emissions could be worth £1 trillion to UK businesses by 2030.

​​For example, imagine that an energy company is looking to go mostly green by 2025. It might be an internal corporate goal, or it might be mandated by the country they’re operating in. They must think about how to become a more sustainable business and provide sustainable products.

To meet these goals, companies can collect and analyse quality data to help make decisions about how to operate more efficiently and operate in a greener way. This includes data ranging from energy and resource use, to greenhouse emissions and supply chain performance.

Another area where data quality can play a major role here is in terms of technology investments made to support sustainability goals. Every piece of technology needs data; if you’re pushing poor-quality data into the investments you’ve made, you’re just creating new problems in more places.

Ensuring successful data transformation through clean data

Ensuring you have clean data to make your data migration a success requires a certain amount of preparation that goes beyond profiling and cleansing data. You need to look at the broader aspects of your data footprint, including rightsizing your legacy environment (which entails archiving and deleting redundant data.) Decommissioning data is another area that must be tackled. These are all aspects of ultimately achieving clean data, and yet they’re too often overlooked or even actively avoided because they can seem too hard.

Once you’ve tackled these areas, though, it’s time to perform a data quality assessment on what remains. It’s better to go for a platform approach instead of a silo approach. A silo creates issues; what you need is everyone looking through a single pane of glass, the same set of issues and the same set of data in their own context – from the developer to the data leader.

It’s important to consider the total cost of ownership. Look at what can be reused in terms of assets, knowledge and experience. Pool those resources into a longer-term approach, not looking at only the immediate cost but also the benefits the company is getting over time. Think about what you are building for the long term; look at the total cost of ownership of your data strategy over time.

Building data’s better tomorrow

Business today requires a whole new level of agility, and that requires a whole new level of data quality. As digital transformation continues, companies must keep business goals front and center: saving money while making more money, reducing risk and increasing sustainability. A sound data quality program can assist with all these objectives and more. Use the best practices noted above to build or improve yours. If done well, it will serve your organisation for the long haul.

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