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Analysis / Business

Mastering Data Visualisation And Data Science

Data Visualisation

Many organisations now understand the need to use data to deliver insight-driven competitive advantage. But, how can businesses take this deluge of data, derived from multiple sources, and channel it into a user-friendly format for diverse audiences within the business?

No matter what sector, operational colleagues need pre-filtered data, presented in an easy-to-understand format.  An organisation can have the best Big Data analytics program around, but if the end user is blinded by science, unable to quickly and easily understand the information produced, the investment in data analysis has been wasted.

Don’t proceed without data visualisation

User empathy – presenting the right information, to the right people, in the right format so they get real value from the insights – can be enhanced with Big Data visualisation.

By investing in a process of converting large amounts of analysed data into an easy-to-understand visual format, professionals across an organisation – from marketing to finance and beyond – can begin to put insight into action to drive business performance.

Indeed, research shows that big data visualisation tools are becoming business-critical to organisations looking to deliver value from their analytics programs. 73% of high performers strongly agreed analytics tools are valuable in gaining strategic insights from big data, according to a recent Salesforce study, which might explain why the visualisation market is tipped to grow exponentially, worth $6.4 billion by 2019.

Do it with a dashboard

Real-time dashboards are invaluable in bringing together the functionality that enables users to interact directly with data. For example, the dashboard might process multiple incoming data sources simultaneously, or apply filters to frame the data in ways that relate to specific tasks or job functions – whether that be reviewing up to the minute sales trends, forecasting the ROI of a new service, or benchmarking costs by region.

Don’t confuse data visualisation with data science

Data visualisation serves up rich and relevant insights to end users, supporting the operational, business improvement and customer service aspects of an organisation. Critically, however, this should not be confused with data science, which sits atop of operations to investigate and analysis data and its sources, to see where it can be used to provide strategic insights. Data scientists research what and how data can be gathered to support this; and finally they build the models that will provide those day-to-day operational dashboards.

Investment in data science should be for fine-tuning and future-proofing a business. Its task should be to decide the most productive ways for data to be used, and how best to package that data to optimise use by colleagues in operations. The data scientist’s role is all about exploring possibilities and discovering new ways data can help a company reach its goals.

Taking care of data’s front-end usability, and respecting the findings of data scientists are two fail-safe ways to ensure Big Data isn’t wasted, and instead becomes the lifeblood of an organisation, driving its long-term health.

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John Cooke is MD at Black Pepper Software, a software development specialist.