Companies never really worried too much about data and the way it was managed and monetized. Those days are long gone. Ivo Weterings sheds light on the crucial role of data in developing new business models and how to manage data the most optimal way. 

‘Data has become the ultimate tool for improving the organization’s internal efficiency and optimizing the customer journey. Resulting in companies yielding more profit,’ explains Ivo Weterings, Senior Manager at Accenture Strategy. ‘Data driven companies make more profit, it’s that simple.’

‘Data management has long been neglected. Companies never really saw the importance and rather focused on other processes. This view has changed drastically. Over the last few years, data has played an increasingly significant role in developing new digital business models and further optimizing the customer journey. Data is the fuel of the new economy and the driving force of the digital era. Moreover, it’s proven to be a very rewarding business model: big data is big money.’

Improving internal efficiency and offering an optimal customer journey

‘In addition to the commercial value of data, its upsurge is also incredibly vital when it comes to companies being more transparent with consumers. Secondly, data helps organizations improve their internal efficiency. Thirdly, optimal data management can help companies offer consumers the best possible product or service. Thus, the companies that don't apply good data management simply can’t do this.

Data Management is no “quick win”

‘This makes it sound like data management is the next logical step, which is true, but it’s not as simple as it may sound. Managing data in the most efficient and effective way possible is by no means a “quick win”: it’s a process that often takes time to perfect. Given the fact that most companies are lacking in that department often means it requires a significant investment in time, resources and effort.’   

How can data yield more profit?

‘As mentioned, the right use of data can improve your organization’s efficiency: to reduce costs; to enable your clients to be more self-serving; to improve and optimize existing processes or to better utilize available data on certain assets – to name a few. Take today’s train systems where everything is monitored. Data reveals exactly which defect might occur on which part, so that a particular piece can conveniently be replaced before it actually breaks. The same can be said about the telecoms industry. Data allows it to act preemptively and avoid resorting to crisis management. Talk about improving your product! Spotify tells you which song you might like to listen to next based on what you’ve listened to the past days, and Amazon suggests which book you might find interesting, seeing “other people like you” do too. It comes as no surprise that virtually all new business models revolve around data.’

The more integration, the better

‘The more integrated your different data sources are, the more improvement you can implement. On a deeper level, integration enables additional flexibility, better insights, and allows you to market new products more easily. Not to mention, it opens possibilities for potential use of your data in broader ecosystems.’

‘The second answer to the question ‘how can data yield more profit’ is sweet and short: through growth. When organizations can offer better services to their customers and have access to data that tells them what the best sales channels are, growth is inevitable. For instance, the sales of pharmaceutical products: instead of sales reps randomly visiting doctors and being unsure of whether or not they will actually sell any products (and only get a good cup of coffee out of it), how efficient would it be if they knew beforehand exactly which doctors to visit? Simply by looking at the medication the doctors have prescribed over the past weeks, reps can have a good idea of what might be needed. While perhaps missing out on a good cup of coffee, your sales rep is far more likely to be successful when visiting that particular doctor. You don’t need to be a rocket scientist to know what this means for salespeople who use data wisely versus those who don’t? One most likely drinks more good coffee, while the other closes a lot more deals.’

"When you no longer need the asset, because data has become your product, the (digital) world becomes your playground."

‘This is the era of platform ecosystems: Airbnb, Uber and the like. One of the things defining their success is the fact that they are data-driven. Data is relatively easy to multiply and thus offers data-driven platforms enormous (and easy) scalability. Once an operational ecosystem is established, you can repackage and re-sell it. When you no longer need the asset, because data has become your product, the (digital) world becomes your playground.’

How are data managed optimally?

‘The logical question arises: how to deal with data? How is data managed optimally in order to benefit consumers and increase profit? Bear in mind, it needs to meet regulatory requirements too (more on that later). To ensure the quality and reliability of your data is guaranteed, the following 5 criteria are vital:

  1. Data Requirements
    Data quality can only be managed when requirements on data are made explicit. It sounds obvious, but it's not as straightforward as you might think. Let's take a mortgage as an example. A requirement could be that a mortgage is always on a house that has a postal code. However, a house that is still being constructed doesn't have a postal code (or house number) yet.
     
  2. Data Governance
    Data is used by several departments within an organization and often with third parties, too. It’s essential that all parties involved are connected and effective communication is executed.
     
  3. Meta Data and Definitions
    Let’s use the word balance for a bank as an example here – did you know that word alone has twenty different possible meanings? Balance at the beginning of the month, at the end of it, balance on average, etc. In order to use data unambiguously, we need unambiguous definitions of what it actually represents.
     
  4. Master Data Management
    Master Data revolves around business entities that define transactions, such as clients, suppliers, products and employers. What’s most challenging here is the ability to identify your clients as unique customers. Let’s take the “forgotten password” example. What happens when you lose your password for a certain account? Sure, you can try to trace down or remember your forgotten password, but most people take the easy way out and simply create a new account using a different email address. Or what about the fact that one IP address is used by an entire family? It’s important to be able to recognize your unique customers – but given the above-mentioned, that proves to be a challenge. Your data must be designed in a way that it recognizes unique users.  
     
  5. Data Lineage
    Effective data management requires knowing exactly how data has been flowing throughout the organization.

How can you use data?

‘Once managed effectively and efficiently, there are various ways to use data, for instance:

  • To look back. How did we perform? A bank looking at last year’s financial statement can draw conclusions and learn lessons for next year.
     
  • For real-time use. What’s happening now? Theme parks monitoring real-time queues and directing people around the park so as to reduce waiting time.
     
  • To predict the future. How can you use data to predict what will happen to this customer in the near future? How (un)likely is it that someone will pay their mortgage this month? Given the number of complaints this customer has filed recently, chances are he might cancel his subscription soon, let’s make sure we prevent that from happening. The more data I have, the better my predictions.
     
  • To prescribe the future. We don’t think this will happen, we know it will. When your data is so reliable and allows you to make predictions that are so accurate you no longer talk about predicting the future, you prescribe it.
     
  • For sale. We know something you don’t, but you want to know. Google is the classic example of a company selling virtually all their data to third parties – it’s the core of their business model.  

Regulatory requirements

‘This brings us to an interesting point in this context: regulatory requirements. One of the things the financial crisis showed us was the devastating impact having limited access to credible data can result in. Companies are, by regulation, required to comply in ensuring their data is correct, and that its quality is sufficient. The level and complexity of requirements differs per industry, yet they are top-of-mind across all sectors.’

"In all fairness, we don’t even know who the data really belongs to."

‘However, in all fairness, we don’t even know who the data really belongs to. Is it the consumer who sells them in exchange for a free app or service? Or is it the company, since they’re the one offering the product? And who has legal ownership if a third party that has purchased the data from the “original” company is involved? These, and other privacy-related issues, are some of the most pertinent questions in today’s debate.’

Digital era is all about data

‘Regardless of the outcome of the discussion, the digital era has left us with this fait accompli: data is the way to go. After having been underestimated for decades, it dominates the business models of the 21st century. The ultimate success formula depends on the industry in which your organization operates, next to a variety of other factors, but opportunities are endless. Reliable, high quality data lies at the core of reliable, high quality decisions. In essence, this is what a successful organization is all about.’