Want benefit from Big Data? Then ask Right Questions
Ever wondered how to get value out of this big data? We have all heard data science turned data into insights or even actions. But what does that really mean? If following questions have popped up in your mind then, read along.
After going through lot many courses and internships, I can sum up Data science can be thought of as a basis for empirical research where data is used to induce information for observations. These observations are mainly data, in our case, big data, related to a business or scientific case. Insight is a term we use to refer to the data products of data science. It is extracted from a diverse amount of data through a combination of exploratory data analysis and modelling. The questions are sometimes more specific, and sometimes it requires looking at the data and patterns in it to come up with the specific question. Another important point to recognize is that data science is not static. It is not one-time analysis. It involves a process where models generated to lead to insights are constantly improved through further empirical evidence, or simply, data. For example, a book retailer like Amazon.com can constantly improve the model of a customer’s book preferences using the customer demographic, his or her previous purchases and the book reviews of the customer. The book retailer can also use information to predict which customers are likely to like any book and take action to market the book to those customers. This is where we see insights being turned into action. As we have seen in the book marketing example, using data science and analysis of the past and current information, data science generates actions. This is not just an analysis of the past, but rather generation of actionable information for the future. This is what we can call a prediction, like the weather forecast. When you decide what to wear for the day based on the forecast of the day, you are acting based on insight delivered to you. Just like this, business leaders and decision makers act based on the evidence provided by their data science teams.
We set data science teams. This comes from the breadth of information and skill that it takes to make it happen. You have probably seen diagrams like this one that describe data science. Data science happens at the intersection of computer science, mathematics, and business expertise. If we dig deeper into this and see the sets of expertise, we will see a variation ranging from. Every skill requires deeper knowledge and skills in areas like domain expertise, data engineering, statistics, and computing. An even deeper analysis of these skills will lead you to skills like machine learning, statistical modelling, relational algebra, business passion, problem solving and data visualization. That’s a lot of skills to have for a single person. These wide range of skills and definitions of data scientists having them all led to discussions like are data scientists’ unicorns? Meaning they don’t exist. There are data science experts who have expertise in more than one of these skills, for sure. But they’re relatively rare, and still would probably need help from an expert on some of these areas. So, in reality, data scientists are teams of people who act like one. They are passionate about the story and the meaning behind data. They understand they problem they are trying to solve and aim to find the right analytical methods to solve this problem. And they all have an interest in engineering solutions to solve problems. They also have curiosity about each other’s work and have communication skills to interact with the team and present their ideas and results to others.
Asking the Right Questions.
The first step in any process is to define what it is you are trying to tackle. What is the problem that needs to be addressed, or the opportunity that needs to be ascertained. Without this, you won’t have a clear goal in mind, or know when you’ve solved your problem. An example question is, how can sales figures and call center logs be combined to evaluate a new product, or in a manufacturing process, how can data from multiple sensors in an instrument be used to detect instrument failure? How can we understand our customers and market better to achieve effective target marketing?
Next you need to assess the situation with respect to the problem or the opportunity you have defined. This is a step where you need to exercise caution analyzing risks, costs, benefits, contingencies, regulations, resources and requirements of the situation.
What are the requirements of the problem? What are the assumptions and constraints? What resources are available? This is in terms of both personnel and capital, such as computer systems, instruments etc.
What are the main costs associated with this project? What are the potential benefits? What risks are there in pursuing the project? What are the contingencies to potential risks, and so on?
Answers to these questions will help you get a better overview of the situation. And better understanding of what the project involves. Then you need to define your goals and objectives, based on the answers to these questions. Defining success criteria is also very important. What do you hope to achieve by the end of this project? Having clear goals and and success criteria will help you to assess the project throughout its life cycle.
Once you know the problem you want to address and understand the constraints and goals, then you can formulate the plan to come up with the answer, that is the solution to your business problem.
Conclusion
To sum up, a data science team often comes together to analyse situations, business, or scientific cases, which none of the individuals can solve on their own. There are lots of moving parts to the solution. But in the end, all these parts should come together to provide actionable insight based on big data. Being able to use evidence-based insight in business decisions is more important now than ever. Data scientists have a combination of technical, business, and soft skills to make this happen in this Information Age. Defining the questions you’re looking to find answers for is a huge factor contributing to the success of a data science project. By following the explained set of steps, you can formulate better questions to solve using analytical skills and link them to business value.
I hope could be of some help to draw more value from your data and stay tuned for more on Data Science Strategies and Implementations.
