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Data Analysis as a Means to Solve Problems
05 de April de 2022
Just like we are made up of a countless number of molecules, we’re surrounded by thousands of millions of data. Our behaviours say more about ourselves than we ourselves know about ourselves. Yes, exactly. Big Data and the Internet of Things have forever changed marketing and businesses, offering possibilities that were, up to now, unthinkable in the relationship we have with clients and the commercial environment.
It’s as important to have the data as it is to know how to read and interpret it. Big Data is like a Ferrari; and just like one, you have to know how to drive it. Not even Ferraris are impervious to accidents. If used well, it can take us to the finish line in the blink of an eye or find new roads and shortcuts. Data Analysis is not an end in itself; it’s an exceptionally powerful medium to maximise business and catch opportunities.
Data Doesn’t Work Like a Crystal Ball
Data doesn’t work like a crystal ball… even though, in view of its enormous possibilities and derivatives, it can enchant us to think so. It’s important to understand what can be asked and what can be expected from data analysis. As it’s the case in so many other aspects of life, questions are as important (or more) than the answers. Data itself doesn’t have value, it doesn’t reference anything if it’s not it’s not confronted with previous content, ideas and hypotheses that can add value to it and be tested in practical applications.
Experts know that incorrectly defining a problem affects everything else That’s why data requires good analysts and trained minds in order to understand the processes and find opportunities. Also, it’s not all about results; one must focus on knowing which are the goals. Inaccuracy is an expensive mistake.
The Opportunity: Making Data Operational
It’s important to be able to find a differential value in all that data. Drawing from it the opportunities that can help companies with their operations. Companies are increasingly integrating data implementation in their business goals. That means that those goals and the previous hypotheses must come before the reading of parameters.
Experts talk about “operationalising data”: critical evaluations that allow you take steps and make decisions. That’s why it’s crucial to have clear goals and a deep understanding of data. These offer different layers of understanding and are open to interpretations that can sometimes be wrong.Integrating various perspectives datawork will help you generate a more accurate opinion and to have a more effective and real application in your company’s operations.
These are some of the benefits you’ll get:
- Cost reduction.
- Creation of new services.
- Faster decision making.
- More reliable adaptation to the market trends.
Data Culture Must Permeate the Whole Team.
Data analysis brings big changes to companies, executives and workers. It’s necessary to build a strong corporate culture around it, integrating every division and having an unwavering commitment. It’s not enough to have data scientists and expect them to lead and execute the whole process. You have to combine internal and external teams, technology and business.
The roles of analysts and data scientists are quite new. There are many types of professionals that have emerged thanks to this tool, but not all of them master all the necessary layers, from maths and statistics to corporate vision. The features that are in highest demand in analysts are their abstraction and communication abilities. The first one allows them to work with data and draw conclusions based on goals or purposes- to correctly “read” statistics. But, without the second one, you can’t operationalise data and find practical applications.
Companies are increasingly choosing to have multi-disciplinary teams that co-create around data from different perspectives. In this process, it’s interesting to pay attention to the way in which we implement the analysis tools. There are many and very accessible ones in the market, but the choice you make can determine the variables you’re going to have.
Data Driven: Constant Experimentation
Nowadays, digital transformation is not complete without analytics. This is no easy journey — it’s not exempted from false expectations and bad decision-making, but companies are betting on it and will continue to do so more and more. There’s an experimental component in data analysis that presents us with a challenge and stimulates us. The possibility to test the users’ response to the different methodologies is exciting.
Data allows us to greatly broaden the offer of online services, but our covenant with consumers must be honest, respecting the security and trust they need when it comes to commercialising their data. The learning process is constant: there will always be parameters to study and from which we can draw conclusions. The world of data is being developed as we go. That’s why the data driven concept is gaining more and more importance — it’s the capacity of companies to integrate technologies that are related to data analysis into their processes and strategies.
Data analysis requires professionals who are capable of drawing conclusions and knowing how to communicate them in an effective way in order to find the differential value in the services and products that a company has to offer.
You can watch the complete EAE On Session on “Data analysis for the solution of business problems” here.