„Data is the new oil“ is a well-accepted quote, and many decide to use it as their motto on their quest to become more data-driven. But what does this concretely mean, and how does being data-driven generate revenues?
There is, of course, the scenario where one sells bytes of data to a third-party. But, all legal questions aside, this only represents a small fraction of the many ways one can monetize data. When Clive Humby coined „data is the new oil“ in 2006, he referred to oil’s versatility and value when refined and processed. Indeed, by itself, raw data does nothing much more than taking up space in storage drives. But when cleaned, analyzed, and combined, it can power the largest companies we see today. Let’s find out how you can start monetizing data for your business!
1. What data?
Data can come from many places. Most often, data is an internal asset gathered over time. Think of order history for your products, data about the machines producing your goods, customer reviews, and so on. However, data can also be external, such as information about the weather, market trends, the news, etc.
Do I have the right data? Do I have enough of it? Where do I get more? These are questions that every data-driven organization should ask itself. And the answer to these questions doesn’t necessarily lie within your databases. Knowing what data you need depends on what decisions you plan on making with that data:
- Do you want to forecast orders to better plan your deliveries?
Then you need historical records for your orders. (Find out what we did for Centraal Boekhuis.)
Do you want to find new clients?
It would be great if you had a mix of internal data to understand the clients you currently have and external data about the pool of potential clients.
Do you want to make it easier for your customers to find answers to their questions?
Having data regarding the types of questions they currently have is surely helpful.
Think of data as if you were running a restaurant. It makes much more sense to make your shopping list based on the dishes you are planning to cook than the other way around. And, just like cooking, you might already have enough ingredients in your pantry to make a decent meal!
„You are what you eat! Your decisions are only as good as the data driving them.“
2. Which analytics?
Analytics is often split into three categories: descriptive, predictive, and prescriptive, which all serve a different purpose.
Descriptive analytics allows you to look back and discover patterns. For instance, you might notice that the questions your customers ask are constantly changing.
Predictive analytics allows you to look into what you believe will happen in the future, such as predicting the number of orders you will need to deliver in the upcoming week.
Prescriptive analytics uses your predictions and automatically decides on the best course of action. An example is to schedule maintenance based on the data your machines are sending you.
The most useful insights are uncovered over time. Knowing what your customers are buying today is undoubtedly helpful, but putting it in context over many weeks/months/years can help answer different questions. This is why descriptive analytics should not be seen as a step towards „smarter“ analytics but instead as a crucial building block for decision-making.
3. What technology should I use?
There are hundreds if not thousands of data technologies, and picking the „right“ one may feel like an impossible task. So let’s first remember that what you do with your data matters much more than how. A fancy data warehouse doesn’t always guarantee the best and the most valuable insights. Therefore, first look at what you can do with what you currently have in-house.
When it comes to data, the 4 V’s of Big Data can help you identify the right tool for you:
Volume. The more information, the better, and your solution should make sure that you don’t have to make difficult choices as to what you should record.
Variety. The technology you use should allow you to record and store different formats. Do you need to record text, audio, video, machine data, etc.?
Velocity. Your stack should allow you to ingest and make data available in due time.
Veracity. Your data stack should allow you to maintain a single source of truth that users can trust.
Check out what we did for DSM
Together with DSM, our Data Scientists built interactive dashboards that highlight market trends & discover business opportunities — leading to a multi-million euro increase in sales in just a few weeks!
4. How do I get started?
Being data-driven is a process that will determine how decisions are made within your organization, and it is no easy task. Here are some characteristics to look for in a data-driven initiative:
So what’s the verdict? Data can generate a valuable competitive advantage as long as you understand how to organize it, structure it, and analyze it. The journey to being data-driven is a team effort and our specialists at Mediaan are perfectly equipped to assist you every step of the way. From IT Consultants, Software Engineers, UX/UI Designers to Data Scientists, our experts can bring your data to the next level and help you become a data-driven organization. Get in touch with us and together we make a difference in your business!
This blog is written by Valentin Calomme – AI Engineer and Business Line Manager at Mediaan.