What is Visual Analytics?
Visual Analytics is an area that applies analysis and reasoning to information sets using some sort of visual means. It combines the best of a person’s capabilities (decision making, analysis, and reasoning) with the number crunching capabilities of a computer. Visual analytics is a form of inquiry in which data that provides insight into solving a problem is displayed in an interactive, graphical manner.
Just like a picture speaks thousands of words, Visual Analytics can talk thousands of data points. Statistical methods are difficult for an ordinary person or business users to understand and correlate with a business problem in order to make a decision and hence the need is for visualization. In layman’s language, visual analytics is to visually represent the information, with the capability to allow human direct interaction with the tool to produce insights, to draw conclusions, to slice–dice information in real time and to facilitate better decisions.
In the field of Business intelligence or data analytics, the insights in visual form are tremendously helpful. It makes the consumption of insights easy to understand and take decisive action. This creates a need for data visualization tools to process and interpret the complex data sets and models.
How can a Visual Analytics Model help?
Visualization helps to perceive new aspects of the data. The user can explore the data models and achieve new knowledge by incorporating interactivity in visualization.
Basic to any analytics is to get the right set of data. It starts with data cleaning, normalization, grouping, or integration of heterogeneous data sources. Then the analyst needs to evaluate the appropriate modelling technique to cater to the problem. Model visualization can then be used to create evaluate the findings of the generated model. It is a herculean task working with hundreds of variables for creating a model like logistic. Visual analytics approach to modelling for high-dimensional data sets can simplify things. It leverages traditional modelling by providing intuitive visualizations for inspecting statistical indicators. Finally, the knowledge and insights gained from visualization are used to take analytical or outcome driven business decisions.
Visual Analytics: The Need and Applications
Steve, VP of an MNC looking at massive spreadsheets containing sales, revenue, spends, cost heads by regions, product etc wondering, “How great it will be if I get a crisp and concise view of all this data every morning. It will work wonders in optimizing my business and boost the bottom line. And the icing on the cake would be if I could zoom into any region of interest to check what sells and what not…” What Steve didn’t know he needed was Visual Analytics. Visual Analytics is especially indispensable while dealing with a large chunk of complex data, these days typically referred as Big Data.
Visual Analytics in Criminal Investigation
One of a very useful application of visual analytics is in identifying criminal linkages by the investigation agencies. For example, by looking at the movement of funds between suspects involved in activities like drug trafficking, bomb blasts etc the actual kingpin or the mastermind can be identified. This might not be a difficult task by looking at the raw data or summarized statistics.
Visual Analytics in the Retail industry
Retailers are leveraging visual analytics to understand and make sense from large piles of invoices/bills generated at each of their stores. It helps them answer questions like:
Plan for inventories- L size shirts sells them most, why don’t stock them more and less of others?
Decide Product Mix- Should we keep dairy products or only other FMCG products
Stock by season- Should we stock sweaters in summer? In winter should we have same stock levels same in Mumbai and Delhi?
Market- Should we stock rice in same volumes in south and north region?
Compare stores- Which region store is more profitable?
It also helps to look at the larger picture of operations for a sustainable and profitable growth.
Tools for Visual Analytics & BI
1) Tableau. Tableau is a self-service analytics platform that takes data visualization seriously as it looks to get businesses away from PowerPoint presentations.
2) IBM Cognos. IBM Cognos Analytics is a cloud-based, self-service analytics platform that allows users to create compelling visualizations and dashboards. One can quickly find the data sources needed using natural language, not SQL.
3) Qlik. Qlik breaks up its products according to how much support you require. Customers can choose from a self-service solution called Qlik Sense, the original guided analytics QlikView.
4) Microsoft Power BI. It’s pretty simple software to use for building reports and dashboards for business data, collaborating and sharing across devices. It integrates with many data sources, SQL databases and even Spark, stored either on-premise or in the cloud.
5) ZOHO. Zoho Analytics (previously Zoho Reports) is a self-service BI platform that allows users to quickly create data visualisations and dashboards. There is even an intelligent assistant, Zia – incorporating AI, machine learning and NLP technologies – which can answer your queries by pulling up reports and KPI widgets.
Opportunities for deploying visual analytics capabilities continue to evolve and grow with the broader adoption of technologies such as mobile business intelligence and location intelligence software.
