The wealth of insights that one is able to gain from a treasure trove of data depends largely on making sense of the data. Data science is the discipline which uses various methods, processes, formulae, algorithms and systems in order to bring data alive. In order to do so however, it is crucial that this data is presented in a manner which can be easily understood and applied. The rationale of this is to ensure that the target audience (who are often not trained to understand data) receives and understands the message. Before a data scientist embarks on a project of analyzing data, the first question that is asked is the objective of the project. In other words, the business case that is to be made. Hence, clarity should be first imposed on this general level as to what is it that the data scientist is seeking to achieve to help the business.
On a more specific level, we concentrate on the mediums that data scientists use in order to process the data. This usually takes the form of graphs, charts, tables, and formulae. These help to highlight patterns and trends and in more complex cases, can show proportionality between sets of data for the purposes of comparison. Again, as the emphasis of this article goes, the data scientist must not be lost in these statistical tools which would make sense to them, but not a novice in this area.
The step forward in presenting data is where the secret of clarity lies. The first of which is visualization. The human brain connects best with pictures and diagrams (which are known to speak a thousand words, or in this case presumably numbers). When pictures are used for the purposes of presentation, they have the effect of conveying a complex message to the uninitiated. For example, an essay on the origins of data science would leave many readers disconnected as compared to a comic strip mapping out the same evolution. The emphasis on visualization is on the story which brings to the next point on relatability.
In order for the significance of the data to be emphasized, it must be presented in a manner which the audience can relate to. When data is broken down into a story, it makes the message more understandable, valuable and most importantly memorable. The enormity of the data can lead to a tendency for the message to be vague and general. But again, as we tie this to the objective of the data, it is worth noting that being concise and specific is better that covering everything but saying nothing.
To the person receiving the data, the value is not in the data itself but the conclusions that are to be drawn from it. In this vein, the value of the data is further enhanced when data is not only presented clearly, but also incorporates practical takeaways. This would reaffirm the value of the data and would help the recipient immediately find utility in applying it to certain business tasks at hand.
The value of presenting data in a manner which is clear and effective cannot be emphasized more. The obvious benefits of doing so, as set out above, would differentiate the ideal data scientist from the rest of the group. If the data scientist not only makes sense of the data for himself, but also does so for others, his contributions will be remembered and valued in any organization.
