Data: A Strategic Asset Beyond Data
What can I do now that I couldn’t do before, or do better than I could before?
Today, most firms are busy building platforms and capabilities to process and manage massive amounts of data. If we call this era Data 1.0, then the question in my intro marks the beginning of Data 2.0.
To better understand this proposition, let’s think of Web 1.0 as an example, which focused on accumulating basic utilities. Then came Web 2.0, in which businesses started leveraging the web’s ubiquity and interactive nature. By the time all systems will develop data processing capabilities and adaptiveness, the golden era of Data 2.0 will emerge. Here, firms will begin to focus on leveraging data as a strategic asset not only in products and sales but also across the entire organization’s business units. Although, today few firms are already running in an era of Data 2.0.
Data-Driven Decision-Making
Data-driven decision-making (DDD) refers to basing your decisions on the analysis of data rather than on intuition. For example, a product designer can pick up a product design based on her eye for good work, or on how consumers have been reacting to similar designs. The degree of reliance on DDD can be more or less, or it can be combined with intuition. However, a number of studies show that the more an organization operates from a data-driven perspective, the more productively it runs. They also show that applying DDD also has links to high ROIs (return on investment).
DDD can be classified into 2 types:
- New Discoveries within data
- Repetitive decision on massive data
New Discoveries within Data
What if an e-commerce organization could predict the number of expectant mothers from existing data, even if the data doesn’t show this specific detail? And what if this data discovery was accurate, or nearly accurate? Now, imagine how this business could excel over its competitors by making offers to such people before their competitors could do. Pregnant women often change their diets, wardrobes, medications, etc., – data patterns that can be extracted from existing data, run predictive modeling, and then marketing campaigns. This is an example of discovering within data to derive DDD.
Repetitive Decision on Massive Data
Let’s say you’re a telecom services provider that loses customers at the end of a contract period. Attracting new customers is usually much more expensive and difficult (because of customer churn) compared to the effort required to retain existing customers.
How might you prevent this churn? With data you can improve your ability to estimate the likelihood of defection and the extent of focus required for each customer or a group of customers to decrease churn and drive more profitability. This is an example of repetitive decision-making which can reap benefits.
Data-Analytic Thinking
Making data-driven decisions pushes us to think logically and approach problems data-analytically. Data analysis is now a very critical piece in the business strategy. The ability to understand data, derive meaningful results, grasp its underlying concepts, and have frameworks for organizing data-analytic thinking will help in solving business problems, discovering more opportunities, creating more opportunities, and improving data-driven decision-making.
Data and capability to extract useful data from given data sets both complement each other. A data team can yield a little value without appropriate data. Similarly, appropriate data can’t be leveraged to make non-trivial business decisions without suitable data science skills.
I hope this post helps you to begin thinking about using data beyond its standard scope. Let me know if you have any questions in the comments, and don’t forget to sign up for the next post.
