FOLLOW

FOLLOW

SHARE

The Evolution of Business Intelligence

Achieving success in the third generation of BI

30Sep

Today's organizations are drowning in data. It sounds like a problem, but it actually presents a huge opportunity for businesses to transform their products and services to be more data driven. This part of the story is, of course, nothing new. But what isn’t explored so often is how business intelligence (BI) tools have evolved over the last decade, elevating companies to the best possible position to be able to make the correct decisions.

In the 1990s (in the first generation of BI), it could take weeks of IT work and coding to create a series of highly formatted reports. Back then, companies embedded proprietary BI tools, such as Crystal Reports, into their desktop or client/server applications using proprietary application programming interfaces (APIs). Single discrepancies in the data ultimately impacted final figures, proving to be extremely costly and, as a result, the time spent checking the data wasted a lot of company resource, which could otherwise have been invested elsewhere in the business. During the first generation of BI, businesses simply could not make snapshot reactive decisions like they can today.

This started to change in the 2000s (during the second generation of BI), when the rise of both standardized data warehouses, in-memory engines and Web technologies made possible the access of large amounts of normalized data through intuitive drag-and-drop report- and dashboard-building tools. Analysts and business users could finally self-serve their analysis without the involvement on IT personnel. This new generation also included improved embedding techniques, enabling companies to create and integrate reports and dashboards inside applications using HTML, iFrames and SOAP-based Web services interfaces. This ultimately meant that employees could access more than a static report, and as dashboards grew in popularity, users were able to quickly investigate the data to help make better-informed business decisions across a variety of domains. The problem that remained with this generation of BI: users ultimately had to go looking for the data, rather than the other way around.

This brings us neatly to today (the third generation of BI), a world of increasingly multi-structured data sets that all need to be analyzed. Businesses may be drowning in data, but many are now recognizing the newfound responsibility of using data to create more value, whether it is to keep costs down, drive additional sales, engage customers more fully or improve process efficiency. The good news is that doing so needn’t actually be as difficult as you might first imagine. Achieving success in this BI generation requires two key steps:

  • 1.Putting data into the context of the day. For BI to truly become a key business attribute, it is vital that data is more easily put into the “context of the day.” What I mean by this is applying just the right amount of data, in the right visualized format, and within the proper setting (context) of the application the business user relies on each day. By bringing BI to the user, almost anyone can become more capably analytical within their role. This requires new embedding technologies, so business intelligence becomes a seamless part of the most popular applications, allowing the data to find the user, not the other way around (this is ultimately what the second generation of BI could not achieve). The techniques for integrating visualized data inside applications must be simple to achieve yet intuitive for the end user.
  • 2.Educating employees. Even when data is put into the context of the day, and even when all have access to the right data sets, the employee must possess a degree of analytical skills so that data-driven decision-making starts to become natural. The good news is that there is a greater opportunity than ever before to build upon employee interest and curiosity. Today, organizations can take advantage of free courseware and online content to create a broader base of analytical skills in every function. Ultimately, these skills are needed in order to thrive in this data-driven economy.

Both steps combined enable more employees than ever to gain access to relevant data and possess the analytical ability to put it to use. No more drowning in data, as the business becomes best positioned with speed and confidence to regularly make transformative, data-driven decisions. Welcome to the third generation of BI.

Comments

comments powered byDisqus
Thumbnails pt 2.015

Read next:

Injury Detection and Prevention in Youth Sports

i