infoTECH Feature

May 17, 2016

How to Tame Your Big Data So it Works for You

Data about your customers can yield extremely useful insights for your business, but only if you’re able to contend with the challenges involved. By extension, big data can yield big insights, but the challenges are, naturally, bigger.

Raw data is like a natural resource that is easy to find nowadays, but it’s not quite so easy to collect or work with. Refining it into the useful ore that is organized data, from which valuable business intelligence (BI) can be derived and used to grow business success, is the central challenge of making big data work for your business.

Organizations with mature big data practices have been meeting that challenge by simplifying their BI systems, from the number of tools used to the user interface, to deliver analytics directly to users without the help of their over-worked IT departments. When your big data is tamed, people in your company with a wide range of technical abilities can model and analyze it quickly to answer business questions and produce reports demonstrating the right decisions to make.

Big Data is Getting Bigger

Recent research by the Aberdeen Group on the way organizations make the most of big data reveals some consistent best practices, with the general theme that simplicity drives analytical success. The amount of data collected grew over the past year for 93 percent of companies, as the average business intelligence tool user regularly draws data from 30 unique sources.

Both internally generated, structured data and transactional application data are used by more than three-quarters of the organizations surveyed. Some 43 percent use unstructured data generated internally, 31 percent use external unstructured data, and a further 31 percent use data from the internet. In addition, as warranted by the rise of the “internet of things,” 19 percent use data generated by machines and sensors.

Many organizations, in other words, are getting good at capturing data in the wild. Transforming it into business intelligence, however, is still a work in progress for most. Data sets consisting of hundreds of millions of rows are not uncommon, but as Aberdeen (News - Alert) Group research analyst Peter Krensky points out in a recent webinar for BI platform Sisense, they are commonly “a strain on analytical tools, and a strain on user skill sets.” 

Image source: http://www.sisense.com/blog/new-aberdeen-group-research-simple-analytics-are-good-for-business/

The organizations that use data analytics wisely are more than twice as likely as other organizations to use an integrated tool for data preparation, discovery, and visualization. This gives even the less technologically inclined users at best-in-class organizations the ability to analyze complex data at the line-of-business level. People at these organizations report much higher levels of satisfaction with the ease-of-use of their analytical tools. A drag-and-drop interface is not a luxury for these best-in-class business analytics organizations – it is an investment in big data usability.

Single-Stack Approach Simplifies BI

Organizations seeking to adopt business analytics to gain the deep insights possible from big data inevitably end up choosing between three strategies: piece-by-piece or assembly line implementation; legacy deployments; or the single-stack approach.

  • Assembling a complete big data solution requires a business to implement a proprietary tool each for preparing, modeling, querying and visualizing data.
     
  • Legacy deployments typically leverage specialized equipment and expertise in-house to build a full-stack solution consisting of databases, ETL tools, data warehouses and data marts, and all kinds of other things that likely lie well beyond your core business.
     
  • A single, one-stop-shop system takes care of the challenge posed to businesses by the complexity of large-scale data manipulation. The architecture and software are specifically designed to process complex big data for business users, so as to deliver the insights they need to do their job effectively and provide value to an entire organization.

The Path from Data to Decision

Complex data is gathered from diverse sources, integrated, filtered, sorted, and analyzed, to generate a prediction, which then forms the basis of making an informed business decision. The better the analysis, the better informed the business decision is.

Good analysis requires data properly prepared for modelling. Between the various steps necessary in data preparation, and the relationship between those steps, the likelihood of making a mess with an assembly-line approach can be seen, as well as the advantage of a single, simple system.

Image source: http://www.sisense.com/blog/infographic-impact-using-single-stack-bi-tool/

Prepared data is mapped by subject, performance measurements and the order and set of conditions that the dashboard’s users need to see. The prepared data is joined together by the relationship it has to other data, what portion is involved in that relationship, and by key or field. Prepared data is “clean,” which means it’s tested for validity, accuracy, completeness and consistency before it’s used for any BI analysis.

A single system that performs all of these data preparation steps and provides an easy interface provides simplicity. Businesses want simple data modelling, to enable as many users as possible to leverage big data.

Big Data as a Business Growth Driver

Approximately nine out of ten executives using big data consider it very important, and even more are satisfied with their results, according to a survey by Accenture. The results can be applied to practically any department in practically any industry, but only if used well.

Successful BI deployments depend on simplicity. Based on the best practices of mature big data organizations, that simplicity is delivered by a single business intelligence system, which serves the various steps from big data to business growth. The right technology tames big data, so its complexity is converted from a challenge into a powerful tool for growth.




Edited by Stefania Viscusi
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