5 Data visualizations trends to follow
Why do we need data visualizations?
Doubtlessly, if implementing a change to the process in the company is necessary, metrics to track are to be found.
Certainly, the easiest way to track a metric is to visualize it. Tools created for calculations and data aggregation do not necessarily enable visualization. For example, Snowflake dashboards added this functionality because the market needed it. Admittedly, visualization started from Excel sheets and formulas. However, currently there is a complex SQL background under the graphs. Therefore companies are forced to purchase new solutions.
The market is divided between major leaders : Google DataStudio by Google users, Power BI by Microsoft and Tableau by Salesforce.
New features
- Visualization becomes easier. On the one hand, complex visualizations created a new job of BI developer who specialized in visualizations. On the other hand, new products have emerged. They feature drag & drop functionality. Indeed, a well-prepared database is required and with it users can build certain aggregations and data visualizations independently using well-prepared databases. It is possible even with a little knowledge of SQL and without programming expertise.
- No pre-installed software needed. Browsers enable building interactive dashboards. Given that the connection is established directly with the sources of information, data can be instantly blended and visualized.
- Ready-made solutions available. Visualization products build the community where members share case studies gathered in the gallery.
- Interrogating data directly. Google Insights Center suggests you the questions to be addressed towards your data. While the initial purpose of the ready-made solutions gallery was more about sharing experience within the community, now it is even more about teaching machines to ask questions. Subsequently, users are to be educated about benefiting from the product at fullest.
- Build-in visualization functionality as a part of the analytical product. Simple dashboards with complicated queries underneath become part of solutions pretending to be analytical. In fact, under the simple graphs, difficult queries and logic are often hidden. Data preparation can be covered on the database side and aggregated for visualization tools.
Successful visualization
Years ago, preparing analytical data for visualization required involving developers. Business analyst created the requirements and checked the implementation. Import from external data sources required manual coding. Developers become dependent on stability of the external API which makes their work a never-ending story.
Subsequently, the role of dedicated BI developers emerged. BI developer builds the data architecture and takes care of sustainability of the system. The quality and speed of the data processing increased rapidly with the appearance of this role. When BI developer acquires decent analytical skills, they become an Analytical Engineer. Manual data extraction from the source starts being replaced by dedicated tools which take care of the stability in terms of data flow pipeline. Having set the pipeline once, you benefit from the direct import in the database. Finally, machines took the people’s jobs.
The raising requirements towards the data transformation triggered the creation of the special data transformation tools, such as dbt.
Main rules for a good visualization:
- Visualize your user flow as a funnel and let your data tell a story. The process which is visualized step-by-step is capable of showing the funnel’s bottlenecks. Digging deeper into the data may bring quick wins.
- Choose 3 ± 2 metrics as KPIs. Keep the focus on the important things, which are the metrics your stakeholders can impact. Data essentialism suggests not to overload the stakeholders with the content.
- Ask the “So what?” question in order to make your data actionable.
Summary
Visualizations have gone a long way from Excel tables to full BI engineering support. Recently data visualization has started including even instruments which were created for other purposes. It displays the demand for a certain kind of functionality on the market.
The threshold became lower: all you need to do is just connect the sources between the tools and start benefiting. The amount of already existing solutions suggest you the best practices with no strings attached in terms of installing additional software.
The biggest challenge is to give your data an opportunity to tell a story. For this purpose, it is crucial to cherrypick the KPIs capable of showing a big picture to stakeholders. Data becoming convertible to actions are by far more beneficial.
