Data analysis & data visualization best practices for Business Intelligence (P1)

Data analysis. What’s the point of it? Your first reaction might be to scoff at, or dismiss this question as rhetorical, perhaps pointless. But answer it anyway.

We analyze data to gain a clearer picture. To understand what raw quantitative masses of information mean – how it relates to, and impacts upon, real-world activities. In the enterprise, we use data analysis to uncover trends and manage processes, to gain an up-to-date ‘360-degree view’ of critical business functions. We then apply that knowledge to underpin prudent, timely, accurate and vital decision-making.

But how do we convey the results of this valuable data analysis? How do you unlock its potential power? We use Business Intelligence (BI). We then consume and share this analysis through data visualization.

If the point of collecting, collating and ultimately analyzing data is to help make better decisions, it can be successfully and definitively argued that the success of any business analytics initiative resides in the quality and aptness of the visual representation of organizational data assets.

So what happens when data visualization fails to communicate the right information in the right way?

Data visualizations: When only the most appropriate, not the ‘best’, will do

In a recent blog post – Business Intelligence: Intuitive vs cool data visualization and infographics – we discussed data visualizations in the context of appearance and appropriateness. The conclusion was that, due to a plethora of contributing factors, including vendor hype and increased business-user contribution in the purchase decision, many BI or data analysis tools are now purchased for their aesthetic appeal (pretty shapes and colors), rather than their ability to most effectively deliver the best business insights.

Not only can this modern addiction to the sleek and sometimes superficial affect the initial purchase decision, this mindset can place corporate data analysis in a long-term straitjacket. Users of all types will be tempted to dazzle colleagues and clients with impressive looking 3D multi-pie charts and animated graphs to the detriment of the data analysis – The chart, rather than the data, becomes the star of the show. For best practice data analysis, the visualization of that data should only support and facilitate understanding, never distract or detract from it.

As noted in the aforementioned blog post, the 2002 publication, Information Visualization in Data Mining and Knowledge Discovery, laments that: “Data visualization has lagged its sister disciplines of data capture, data storage, data analysis, and knowledge discovery… there is still a huge gap between our ability to extract answers and out ability to present the information in meaningful ways.”

The book goes on to define the principle function of data visualization in stark, uncompromising terms:

“Visualization, well done, harnesses the perceptual capabilities of humans to provide visual insight into data… (it is) fundamentally about data reduction… Finding a view or projection of the data that reduces complexity while capturing important information.

“A successful visualization is one that emphasizes the information of interest and presents it as a resolution sufficient to perform the task.”

We asked whether most visualizations of corporate data lived up to this unwavering, heady definition. Unfortunately, the answer was a fairly resounding no.

Yellowfin 5.2: Helping you have your pretty cake and successfully eat it too

At Yellowfin, we’re about to launch the new release of our BI solutionYellowfin 5.2: Making Business Intelligence even easier – in a celebratory series of webinars, Tuesday 7 June. Register for a webinar time that suits you here.

This release will further our position as the world’s easiest-to-use, deploy, integrate and embed BI solution. Amongst other additions and enhancements, a major focus of the upcoming release has been on making data analysis easier, to give users of all types access to better business insights, via a range of new highly intuitive analytical visualizations.

These new chart types hold notable visual appeal, but this was not our first priority. Our primary aim is to make data analysis easier and more insightful.

Making data analysis easier: Delivering better insights to everyone with new intuitive analytical visualizations

Yellowfin 5.2’s new chart types, including Box and Whisker charts, make the implications of data analysis easier for everyone to understand and act on. New HTML 5 integration also provides interactive rollovers for Yellowfin’s range of data visualizations and includes enhanced chart tool tips. Improved tool tips enables users to quickly, accurately and effortlessly interpret particular trends or aspects of a data set, to transform analysis into understanding and then steadfast action (see image).

The above Box and Whisker chart clearly demonstrates how this insightful chart type, in combination with Yellowfin’s new interactive tool tips rollovers, allows you to effectively manage and monitor your sales process. Suddenly it’s easy to track and identify trends in the mean, median and range of costs and sales to determine your yearly average profit margin, and compare sales and costs on a year-to-year, and quarter-to-quarter basis.

Yellowfin 5.2 is making data analysis easier by empowering everyone with deeper insights via new highly intuitive analytical visualizations.

Stay tuned for part two of this blog entry for a shortlist of effective data visualization rules.

Thanks for trying Yellowfin

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