In 2020 there were approximately 44 zettabytes of data in the world, with one zettabyte equal to one sextillion (1021) bytes. Given how much data is created every day, it is predicted there will be 175 zettabytes by 2025. That is a lot of information, and it requires skilled people, machines, systems and software to make sense of it. Lots of advanced technical skills and knowledge are needed in order to sift through often huge datasets for patterns and meaning, which is where data science and applied statistics come in.
You might think that statistics and data science are entirely technical fields and in large part, they are. Analysis and number-crunching is of little inherent value on its own, however — at least to businesses and other organizations. If you were to complete a Master’s in Applied Statistics at Michigan Tech, for example, you would learn advanced statistical and analysis methods such as data mining, predictive modeling, model diagnostics, parametric estimation and forecasting. You would learn about programming and relevant technology with industry-standard software and tools such as R, SAS, S-Plus, and Python. However, there would also be an emphasis on so-called ‘soft’ skills, like leadership and communication.
Communication is particularly important because it is not enough to analyze the data and statistics to draw up insights and conclusions. You also need to be able to communicate the meaning, implications and value of those insights to a wide range of different people — many of whom will have less technical knowledge — in a clear and understandable way. According to one EU paper, major institutions such as Unesco, World Bank and Eurostat have listed several features to be considered in assessing the quality of statistics, which include integrity, methodological soundness, accessibility and serviceability. Comparatively less attention has been given to the importance of the communication of statistics, which, the authors argue, should be considered to be “an integral part of data production and dissemination”.
What do you need to communicate?
You need to be able to communicate the findings of any statistical or data analysis, but you will also often have to adopt a ‘bigger picture’ view. That may mean connecting the dots between findings from different datasets and working out where they connect with the interests of your business or organization.
Communication is always a two-way street and, while presenting your findings to others is important, you will also have to be receptive to communication coming the other way. Firstly, you might need to communicate with other departments, stakeholders or client businesses in order to find out what they hope to gain from any statistical analysis. That certainly does not mean skewing or distorting the results of any analysis, which would be both unethical and usually counter-productive.
It does help, though, to have a clear understanding of what your data analysis can provide. Is an organization looking to improve its decision-making process by embedding data-driven thinking into its business intelligence processes? Is it being used to assess and analyze cyber-security issues, or to sift through mountains of consumer data for marketing purposes? Data science and applied statistics can often provide ‘accidental’ insights that were not initially considered, but business insights can guide the datasets you select, how you select them and the methodologies you apply to them.
It may also be necessary to communicate the value and impact of your projects, ongoing work, data science and statistics as a whole to the wider company or organization. This can help ensure that the ‘pipeline’ remains open, and the organization continues to reap the benefits of data-driven decisions and analysis.
Who are you communicating with?
The ways in which you communicate may in large part depend on who you are communicating with. Two teams of data scientists collaborating on a project, for example, will communicate their findings and processes very differently than an organization communicating key findings of a large and complex study to the general public.
You may find yourself having to communicate with:
- Other data science teams or colleagues within statistics — expect communication to be technical.
- Stakeholders and subject matter experts — less statistical jargon but can communicate complex ideas.
- Upper management and other departments — communicate findings and value in a complete but clear and concise manner.
- Customers, the public, press releases — look for key takeaways and summaries, but with more detailed analysis and statistics available for those who want it.
Types of communication needed in data science and statistics
As future technologies (including those used in data science) continue to evolve, it is more important than ever to be able to communicate clearly and effectively with a wide range of people, including those without technical backgrounds. It may be necessary to communicate the nature of those changes, as well as findings from any particular piece of analysis, to other departments, stakeholders and parties.
Some useful ways of communicating effectively include:
- Data visualization
Data visualization involves the representation of data via graphics of various kinds. This could include charts, graphs, maps and infographics. This can very effectively express complicated ideas and concepts in a way that is easy to understand, as the human brain is adept at processing imagery.
- Personal presentation
This could vary widely, from one-to-one engagement with a single stakeholder, to a small group like a board, to classroom-type environments and finally large audiences. Some more technically oriented people’s palms might start to sweat at the very thought of addressing a large audience armed only with a stack of statistics and a prepared PowerPoint presentation, but the ability to make an effective and persuasive presentation is a valuable and very transferable skill to have.
- Written presentation
The written word is still important though, whether it takes the form of a formal report or white paper, a proposal, business case or email.
No business operation exists in isolation and communication skills are important for everyone in an organization, from the CEO down. That certainly includes people working in largely technical fields such as data science and applied statistics, who often need to communicate their findings and insights effectively to a wide range of people.