Why reporting is one of the main skills of a data scientist


Big Data


19 MAY 2021


5 Minutes

In 2021, the importance of understanding and reporting data has moved from the introduction to the fundamentals.

We are now when people expect quality data and want to find it instantly. Data scientists needs to have a deep understanding of the data to deliver insights to businesses.

A study by the Chartered Institute of Professional Analysts showed that 61% of senior executives believed data was the next breakthrough skill needed to compete.

The need to compete will become more pronounced as the world becomes more digital and the number of people using mobile devices to gather data and analyze it increases.

For every minute of data you acquire, you earn an average of two minutes and that could be reduced to half if you don’t know the data and know-how to prepare it for analysis.

Data Science is also important when understanding data and its implications to improve operations and increase the value of the data


The more data scientists can explain how data can change the way we do (and not do) business, the more impact it can have.

Because data projects collaborate across functions and data science results often feed into a larger final project, the true impact of a data scientist’s work depends on how well others can understand their insights to take further action.

We need to start a process to make data scientists understand the relationship between data and business and the implications that this has on the businesses operations. If data can be used in a way that has no business value and is purely speculative, it is wasted, wasted effort and wasted time. In the next five years, we will see that this will become less and less of an issue.


Data science has emerged as a crucial competency in technology-driven organizations. While some organizations like Google, IBM, Amazon and Apple are already running with it, other groups are just starting out. To meet the needs of today’s companies, these organizations are relying on data science professionals with strong skills and credentials in mathematics, statistics, and computing.

According to a 2018 report “An AI for every industry: AI in an era of technology and business transformation” published by PricewaterhouseCoopers (PWC), “Data science, and its more specific technical skills such as analytics, machine learning, machine learning, and natural language processing (NLP), will be increasingly important for organizations over the next three to five years”.

Now let’s take a closer look at what data science skills and core technology are required to become, today and in the future, the leading data science talent.

      • Analytic thinking—the ability to extract key information from data and develop workable solutions.
      • Data literacy—the ability to use data in analytical ways to produce knowledge
      • Data science—the ability to determine the structure, relationships, and patterns of data, and then make predictions based on those structures and patterns
      • Machine learning—the ability to build a model from data by using automated tools such as neural networks, supervised learning, or unsupervised learning
      • Statistics and data modeling—knowledge of the statistical theory, techniques and their applications to a wide variety of problems, and the ability to apply them using



You are not the only one interested in using the right data in a better way. The same is true for everybody you contact about your organization’s data. When it comes to the data for which your organization needs to make decisions, and the decisions that have to be made using those data, you will probably have to involve other people. You will need to reach out to them and ask them what their opinions are on your initiative, and whether they can be persuaded. For the record, this is not necessarily a bad thing. The fact that even those that are unlikely to be particularly convinced by what you are proposing will still help you achieve your goal through data.

However, it is important to remember that the people you are trying to influence are usually not people that you know, or know well. So, you might end up going “off the rails” in the process. You will have to get the right people on your side. For example, you can’t convince a CEO of a company you haven’t worked for 20 years.

In order to do this well, and more importantly, to do this effectively, you have to get better information about the people you need to inform. Not only do you need to be better informed yourself, but you also need a better understanding of the people you intend to influence, and what they find most helpful when you engage them in their process.

The reason this matters, for what is called the “key indicators” of success of your enterprise data initiatives, is that they will make the difference between “good enough” and “better than good enough” – that is, between what is successful and what is not.

To sum up, whatever tools you use, remember these basic data science communication tips and you’ll be more likely to deliver the following presentation:

  • Start with a problem. Is this a problem your audience already knows? If not, you should start by clearly establishing the problem. Investigation and preparation are key.
  • Show empathy for your audience and present them with the information they want in a format and language they understand.
  • Use data visualizations to illustrate your conclusions, but let your own explanations—not graphs—drive your presentation.
  • Keep it simple, and leave the unnecessary details in each of your explanations and diagrams.
  • Finally, keep practising! You got this!

So, do you feel ready to engage in better data analysis and reporting?