Generating effective presentations for Data Analysts

Keeping the technical skills aside, analytics field requires one to have soft skills including communication and presentation. The final stage of analytics process is presenting your findings and conclusions to the client. Although a presentation, data analytics findings require a certain workflow to make the presentation a good business story. In this article we will look a few tips that will help to put up a good presentation.

Start with Business problem and keep that in context

All the findings and the corresponding conclusions come with any business problem connected to it. Without that, the whole sense of doing analytics is meaningless. Same goes with presenting the findings. When starting the presentation, the first thing to start with is what problem have to handled in this task, what did you try to solve? Throughout the presentation, all the statements and findings should stick to this business problem.

Go with a destination in mind, generating bunch of outputs is not the key, but solving the needs of a client is.

Less is more

The main aim of presentation is to highlight the findings and its corresponding proof — Like visualization. So, remember not to overcrowd each slide with ton of text. This could potentially confuse and divert the audience’s attention. The preferred option is to present the findings with not more than 3–4 lines of text and a visualization if present in a simple manner. The text should highlight the findings from that specific graph / chart / analysis specifically but keeping the business problem in context.

Simplicity sometimes overshoots style.

Approach first, result second

Analysis procedure often has many approaches, either with certain disadvantages for each or with difference in ease of application or both. In such scenarios, presentations should start with the approach taken to perform the analysis, it’s justification and then the result obtained. The justification here is that mentioning the approach can be used by the audience to validate the procedure and result while presenting the result only will not help the audience to put forward questions regarding the validity of that approach.

Result will come anyways, but correct results require correct approach.

Start with general and then dig in

When starting with any presentation, first go with basic and low level findings before digging in. Taking too big steps at and skipping general findings can put the audience out of track. The best practice is to start with general findings, giving an overview of the data and then going into it. Audience should first have an overview of the available data and business problem and then present other findings.

Well started is half done. A good introduction and understanding goes a long way.

Be a storyteller

The term “Storytelling with Data” might be familiar to any data enthusiasts. To put up a good presentation, be a good storyteller. Organize the slides in a meaningful order starting with basics, in-depth findings and the conclusions along with approach and justification. A logical flow makes the presentation easy to understand for the audience.

Explaining data is like a story, you require an intro, a core part and a conclusion.

Keep the visualization simple and labelled

Make the maximum of use of the potential of visualization — Ease of understanding. Keep visualizations simple, labelled and self explanatory. Remember to use the correct chart type to highlight the distribution of data. Avoid technical terms when labeling. If any visualization is not relevant to solving the business problem, remove them from the presentation itself. Check out my guide on generation effective visualizations.

Visual aids has a bigger advantage on understanding data. This is the potential of visualization.

Highlight the business metrics

Certain tasks require you to choose a certain business metric and to perform analysis using that. It is required to highlight them in such cases and it’s justification on why that and not other similar metrics — Is it suitability of this metric? Ease of performing analysis? Accuracy?.

Everything should have a what and why with it.

Thank you for reading!

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Guide to generation effective visualization: https://medium.datadriveninvestor.com/a-guide-to-building-effective-dashboards-bd93362a0bf0