Scattered Information? Try These Techniques To Rope In Wandering Data

by Anna Johansson
Published 05/01/2018
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Big Data @Scattered Information? Try These Techniques To Rope In Wandering Data

Manually gathering data from multiple sources takes time, yet it’s a necessary task when data analysis drives your decision-making. With data coming from multiple sources and programs that don’t automatically talk to each other, processing data can be an arduous task.

The promise of big data is insight into customer experiences to improve products and services. To gain those insights, you need an air-tight organizational system for capturing and managing your data. The promise of big data can only be realized when disparate data sources are integrated.

The first step is to de-clutter; stop collecting data you don’t plan to make available to decision-makers. The second step is to make sure the data you collect is accessible. The final step is to implement company-wide policies to maintain the integrity of your data.

Create an organized foundation: be selective with the data you collect

The amount of data you could collect is infinite, but that doesn’t mean you should collect it all. Unused data accounts for about 99% of all collected data. That unused data is taking up precious space on servers and hard drives belonging to businesses across the world.

A 2012 Digital Universe study reported that in 2012, 2.8 trillion gigabytes of data had been collected, but only 3% was tagged and ready for use and just 0.5% was being analyzed. The study noted the small percentage of analyzed data has been shrinking as more data is collected.

Collecting data is the easy part. The problem is that most data isn’t being made available to decision-makers, and that’s why so little data gets used.

Although the Digital Universe study was performed in 2012, those numbers have remained accurate in further studies. For example, in 2015, McKinsey & Company wanted to know if data from oil rig sensors was being used for decision-making in the energy industry. They discovered less than 1% of data obtained from approximately 30,000 data points was available to energy industry decision-makers. Even if they wanted to make decisions based on all data collected, they couldn’t.

If you’re not going to make collected data available to the decision-makers, it won’t be of any use. Before diving into data collection, you need a plan outlining what you’re going to collect, and how you’re going to make it available to the decision-makers who need it. This is founded on an internal strategy that manages how and when team members access, collect, tag, and distribute data.

Create a written strategy for data management

To prevent different interpretations of the same data, you need a written policy dictating how data should be accessed and interpreted. You want everyone to use the same programs to crunch numbers or department heads will end up using different data points to make important decisions.

It’s important to have team members use tools that present data visually in some kind of dashboard. This helps them access and interpret multiple sources of data more easily. If they rely on that data to make any decisions, a visual display of data is imperative. For example, a business intelligence dashboard that displays graphs and charts makes it easy for a marketing team to identify conversion trends at a glance on a consolidated, single screen.

Any dashboard you create should only report information relevant to the audience using it. A dashboard is strongest when you define your target audience first. In other words, you don’t want to clutter up a sales team dashboard with financials relevant only to the accounting team.

In this Business Intelligence Best Practices guide, datapine confirms that dashboards achieve their highest potential when designed to eliminate clutter. Design is a form of communication, and a visually appealing dashboard communicates data effectively to its user. A lack of clutter makes that communication quick. Presenting too much data or a cluttered, complex interface will fail to serve the intended purpose.

Datapine’s guide also outlines the Gestalt Principles of Visual Perception that outline basic human interaction within the context of visual stimulation. The six principles include proximity, similarity, closure, enclosure, continuity, and connection. These principles should be understood by all.

Zero in on data accuracy and consistency

Thoroughly exploring the concept of data accuracy, published this meaty analysis sharing their investigation into the accuracy of click through rates for branded vs. non-branded keywords. The article shows that the deeper you dive into verifying data accuracy, the more you learn about the programs you use and discover their limitations.

Although no data analysis will ever be perfect, strive to maintain consistent strategies between all who collect, analyze, and distribute data. You want all your gears moving in the same direction so if the way you manage data evolves, everyone will adapt together.