Data Science Strategy: Memilah Konsep Data-driven Organization

Sorting Out the Concept of a Data-driven Organization Data is the new black! Or the new oil! Or the new gold! Whatever you compare data to, it’s probably true from a conceptual value perspective. As a society, we have now entered a new era of data and intelligent machines. And it isn’t a passing trend or something that you can or should avoid. Instead, you should embrace it and ask yourself whether you understand enough about it to leverage it in your CHAPTER 1 Framing Data Science Strategy 19business. Be open-minded and curious! Dare to ask yourself whether you truly understand what being data-driven means. The concept of being data-driven is a cornerstone that you need to understand in order to correctly carry out any strategic work in data science, and it’s addressed in several parts of this book. In this chapter, I try to give you a big-picture view of how to think and reason around the idea of being data-driven. If you start by putting the ongoing changes happening in society into a wider con- text, it’s a common understanding that we humans are now experiencing a fourth industrial revolution, driven by access to data and advanced technology. It’s also referred to as the digital revolution. But be aware! Digitizing or digitalizing your business isn’t the same as being data-driven. Digitization is a widely-used concept that basically refers to transitioning from analog to digital, like the conversion of data to a digital format. In relation to that, digitalization refers to making the digitized information work in your business. The concept of digitalizing a business is sometimes mixed up with being data- driven. However, it’s vital to remember that digitalizing the data isn’t just a good thing to do — it’s the foundation for enabling a data-driven enterprise. Without digitalization, you simply cannot become data-driven. Approaching data-driven In a data-driven organization, the starting point is data. It’s truly the foundation of everything. But what does that actually mean? Well, being data-driven means that you need to be ready to take data seriously. And what does that mean? Well, in practice, it means that data is the starting point and you use data to analyze and understand what type of business you should be doing. You must take the outcome of the analysis seriously enough to be prepared to change your business models accordingly. You must be ready to trust and use the data to drive your business forward. It should be your main concern in the company. You need to become “data-obsessed.” Before I explain what it means to be data obsessed, consider how you’re doing things today in your company. Is it somewhat data-driven? Or perhaps not at all? Where is the starting point in different business areas? Figure 1-5 shows a model (with examples) for comparing a more traditional approach to a data-driven approach related to approaching different business aspects. 20 PART 1 Optimizing Your Data Science InvestmentFIGURE 1-5: The difference between a traditional business and a data-driven business. Comparing the approaches in a traditional business versus a data-driven organization is worthwhile. Many companies’ leaders actually think that their companies are data-driven just because they collect and analyze data. But it’s all about how data drives (or doesn’t drive) the business priorities, decisions, and execution that tells you how data-driven your business really is. Understanding what the starting point is will help you define your ground zero and identify which areas need more attention in order to change. Being data obsessed So, what does the term data-obsessed actually mean? It’s really quite simple: It means that you should always assume that the access and usage of data can improve your business – in all aspects. Use the following list of questions to determine how data-obsessed your organization actually is: » » Which data do you need to use as a company, based on your strategic objectives? Do you collect that data already? If not, how do you get it? » » Do you own all the data you need? If not, how can you secure legal rights to use it for your needs (internal efficiency or business opportunities)? » » Is the data geographically distributed across countries? If yes, what needs to happen to your infrastructure in order to enable you to use it efficiently? CHAPTER 1 Framing Data Science Strategy 21» » Is the data sensitive? That is, does it contain personal information? If yes, what are the applicable laws and regulations related to the data? (Be sure to note whether those laws and regulations change, depending on which country houses a specific data storage facility.) How do you intend to use sensitive data? » » Do you need access to the data in real-time to analyze and realize your use cases? If yes, what type of data architecture do you need? » » What data retention periods do you need to establish for the different types of data used by your organization? What will you use the selected data types for? Are you in control when it comes to expected data volumes and data storage costs per data type? » » Can you automate most of the data acquisition and data management activities? If yes, what is the best data architectural solution for that? » » Do you need to account for an exploratory development environment as well as an efficient and highly automated production environment in the same architecture? If yes, how will you realize that? » » Are employees ready to become data-driven? Have the potential, value, and scope of the change been clearly stated and communicated? If so, are employees ready for that change? » » Are managers and leaders on board with what it means to become data- driven? Do they fully understand what needs to change fundamentally? If so, are managers and leaders ready to start taking vital decisions based on data? The questions I post here don’t comprise an exhaustive list, but they cover some of the main areas to address from a data-driven perspective. Notice that these questions don’t cover anything related to using machine learning or artificial intelligence techniques. The reason that isn’t covered is because, in practice, a company can be data-driven based only on data, analytics, and automation. How- ever, companies that also effectively integrate the use of technologies like machine learning and artificial intelligence have a better foundation for responding to the machine-driven evolution in society.