5 Insights to Secure your Management’s Attention to Data Governance

If you are a manager dealing daily with less-than-optimal processes due to poor data quality, you’ll be looking for an efficient way to put data and its governance on the agenda. Here is how you make data a management topic.

Faster time-to-market, growing e-commerce revenue and personalizing the customer experience are some of the current topics being discussed in most boardrooms. Going digital is the main objective. As a digital driver, the COVID-19 crisis has accelerated the pace by which businesses deploy their digital initiatives. According to a McKinsey survey of 1,500 business executives, the frequency of digital activities moved to monthly and even weekly. With every initiative launched, strategic questions arise such as: how can we do this? What do we need? And, sooner or later, the word data is part of the answer. With the following five key messages, you can assure data as a subject gets all the timely attention it needs from your management.

1. Data is a Valuable Asset

In 2017, the Economist put data ahead of oil as the most valuable resource in the world. Based on the profits of the five Big Tech companies alone – Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft – the Economist declared data the most valuable resource. But unlike oil, data cannot be “used up”, in fact, it’s replicable, reusable and can grow in value through its usage.

Still, companies find it hard to put a specific value to their data. That’s because the value of data is not in selling it but in using it for the business to optimize processes and create a better customer relationship. Data lets you understand what’s really going on in your market, analyze and predict customer needs and provide customers with a personalized customer experience across all channels. The right valuation and usage of data is what makes all that possible.

2. Data Provides the Foundations for Business

Underneath powerhouses like Netflix, Uber and Disney lies a solid foundation of data. This foundation drives the actions and decisions that make or break the business. The importance of having a trustworthy data foundation only becomes apparent when you consider how often your people work with data. Every task, meeting and project includes some data going back and forth, covering products, customers, suppliers and product components. Without data exchange, nothing happens.

In practice, mistakes caused by incorrect or incomplete data are rarely addressed correctly. The subsequent costs are written off as department costs or labeled as inefficiency, while there is a clear underlying explanation: poor data quality. It’s only when leading projects such as digital transformation are not delivering their promised benefits that it becomes obvious that a foundation of quality data is missing. That’s why you can ask for every agenda item: which data is needed for this strategy? And what will happen to the project if there is no solid data foundation?

3. Data Drives Successful E-Commerce

One of the main business goals today is to move the revenue streams online, either partly or even completely. Companies starting with e-commerce find, however, that it’s not as straightforward as putting their product catalog online. Customers expect clear product descriptions, specifics by which they can compare and several product pictures. Without a product picture, a product wouldn’t even be looked at, let alone be considered. Ideally, your product information includes rich media such as complementary products, videos, stories and illustrations. But it’s not only product data that enables e-commerce: product recommendation models using customer data can help boost online sales even further.

4. Data Quality indicates Process Quality

Incomplete product descriptions impact sales negatively. Incorrect descriptions can lead to product returns and claims. Duplicate products will affect availability and logistics. An overall poor customer experience will damage the brand. And that’s just for the process of selling products online. From manufacturing to retail, finance to customer support, every process depends on data being trustworthy. Data needs to be accurate, complete, consistent, reliable and up to date for your employees and customers to do business. By setting the bar for data quality too low you end up wasting time, losing revenue and having poor performance. According to Experian’s 2022 Global Data Management Research Report, 75% of businesses that improved data quality in 2021 exceeded their annual objectives.

5. Data for Understanding the Customer

The better you understand your customers, the better you can meet their needs. This goes beyond providing personalization as part of the customer journey. By analyzing your customer data, you can segment your customers and take the right action to improve your customer relationships. The RFM model, for example, puts the recency of the last purchase, the frequency by which customers buy and the monetary value together to identify new customers, loyal customers, VIP customers and the loyal VIP customers you are currently losing. Approaching your customers with the right message in the right moment will draw them back in and increase their loyalty.

Don't Talk Data, Talk Business

Data governance and data quality won’t get much attention by themselves. It’s better to start talking about the current challenges that management addresses, like improving the time-to-market, setting up an online shop, creating a better customer experience or lowering costs. Then, pinpoint the strategic role of data and position data quality as a key enabler for the digital initiative. The business case for data is different every time. Sometimes, there is a burning platform or a legal basis such as the GDPR that forces your organization to implement data governance. The crux for every case lies in showing the connection between data quality and the goals set. Show your management what happens when the data quality is poor. And when that message lands, you can then establish the minimal data quality needed and discuss how your organization will get there.

This is the first blog of our article series on Data Governance. Click here for the next article.

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