Of all the stages of big data consulting, visualisation seems to be the most underrated. Companies sometimes fail to dig into this topic, take it for granted and do not realise how much these charts and graphs contribute to their success. But this approach is wrong. Visualisation is extremely important. Without it, business users could not grasp data that is, by definition, enormous and growing. Without visualisation, there would be no business insight, no answers to ad hoc questions and no data quality control.
Why companies should bother visualising massive data
John Tukey, a celebrated mathematician and researcher, once said: “The greatest value of a picture is when it forces us to notice what we never expected to see.” Visualisation makes the results of analysis obvious and clear. Business users can easily spot the dependencies and hidden correlations within large data sets. We have identified 3 main advantages that will be relevant to any company:
- Gaining insight.
- Identifying trends, opportunities and threats.
- Making data-driven decisions, which is the ultimate goal.
Big Data visualisation makes the difference
Here are examples of how some big data analysis results can look with and without data visualisation.
Example 1: industrial data analysis
It is great when the maintenance team receives a notification that a part in a machine is likely to break down. They can check and repair it before it goes out of service and disrupts the usual flow of operations. In this case, the team does not need to look at the millions of values the sensors supply every day: they leave the processing work to the analytics system, which checks the values against a failure pattern.
However, the team is unlikely to be satisfied with instant alerts alone. They also need to know trends, dependencies and connections, and they can only do that with big data visualisation. If they are interested in comparing the working time and efficiency of the available machines, they will get this information instantly just by looking at the line chart.
Example 2: social feedback analysis
Imagine a retailer with nationwide coverage and a customer base of more than 20 million. It would be impossible to trawl the internet for every comment and review, and it would be madness to try to gain insight simply by reading them all. To perform these tasks automatically, companies turn to sentiment analysis. And to get instant insight into the results, they apply big data visualisation techniques. For example, word clouds show the frequency of the words used: the higher the frequency, the larger the word. If the most prominent words are “hate”, “horrible”, “terrible” or “failed”, it is time to react.
Example 3: customer behaviour analysis
Companies use a similar scenario to analyse customer behaviour. They strive to implement big data solutions that can collect detailed data on purchases in physical stores and online, browsing history, GPS data, mobile app data, calls to the support centre and more. When logging billions of events a day, a company cannot identify trends from just a handful of records. With data visualisation, e-commerce retailers can easily notice the change in demand for a product based on page visits, understand peak shopping hours or see the share of coupon redemptions.
At Digital Fox Data we turn massive data into clear dashboards with Qlik, Power BI and Tableau. Discover our Business Intelligence consulting or tell us about your challenge.