Data Visualization - Impact on Data

It was big data analytics that gave rise to machine learning and artificial intelligences, concepts that have come out of sci-fi and into the real world. Innovators realized that if a machine can crunch data, it can learn the data as well and develop near human intelligence.

Big data analytics is advancing and data visualization is advancing with it, opening new avenues for stakeholders and decision makers to interact with data and derive actionable insights out of it.

Every product launch, every new service or business launch and even research, is powered by big data analytics, so it is imperative that the final output of this analysis gets presented in a visually cognitive format, helping decision makers to take note of important trends and insights.

Industry KPIs-driven custom dashboards are key when it comes to data visualization. Data visualization has moved beyond static pie charts and maps and has adopted a more interactive approach. End users are now able to interact with the data, adjusting data points across the board to arrive at the insights they are looking for. It is more apparent in predictive analytics, where end users interact with data projecting future trends and patterns.

Apart from the obvious, data visualization has impacted data management in a much subtler way:

1. More focus on data quality - Data visualization has made data discovery easy. Now imagine that a stakeholder, using the insight derived from analysis and visualization of a certain data, takes a business-critical decision. It later turns out that the data was of poor quality and a potentially damaging decision was taken. Data visualization assumes that the data is of high quality, so the onus is on analysts to verify the data before churning it through the analytics engine.
2. Easy identification of data errors - Data visualization has enabled analysts to zero-in on data errors quickly. Imagine going through tens and thousands of rows of data just to find a single error. With visualization, all the errors are put out in the open and analysts can correct the error before making the final report that will be consumed by the end user.
3. Data ownership - Data visualization is not a walk in the park. Neither is identifying patterns, trends, and insights from the data. Expert analysts and resources are needed both to make sense of the data and maintain it, in terms of quality and security. Organizations that assign dedicated process ownership of data, whether to an internal team or an external resource like Thinklayer, a leader in data visualization, that has worked some of the biggest names across all business verticals, to provide data visualization support by creating dashboards to fit their business requirements.