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Over the past 50 years, BI  has evolved from analysts poring over paper binders, to cloud-based data warehouses that process multiple terabytes of information almost instantly. Data can now be visualized in mobile reports that business leaders use to gain insights and make more informed operational decisions. Until recently, this level of actionable BI required time and the expertise of data scientists.

To maintain a competitive advantage, future business users will face increasing pressure to analyze data and make rapid informed decisions. This means that managers from the top down will need access to streamlined information whether they are behind a desk or out in the world. And as processing power increases, artificial intelligence (AI) will become reality—creating unforeseen business opportunities by calculating enormous amounts of data, to automate insights and align actions with logistical operations.

It isn’t science fiction. Expanded BI is already being used to improve business in real time. So where is business intelligence (BI) headed in 2017? We’ll take a look – but first, let’s examine where we’ve been and how we got here.

 The Rise of Expanded BI

In 2004, Campbell’s Soup Co., was the first to use an algorithm that instinctively monitors weather in specific markets for temperature fluctuations, and then compares the numbers to multiple variables. When an area reaches a certain threshold, Campbell’s will ramp up location-based marketing. For instance, the company increases local radio ads when unusually cold weather hits its second-tier markets. The company has evolved that model and now uses more advanced Big Data to improve sales.

UPS offers another example. Over the past 10 years, the company developed an algorithm dubbed Orion, which helps discover the most efficient route for drivers. This is done by analyzing their average 120 daily stops and the over six-quinsexagintillion (a unit of quantity equal to 10198- 1 followed by 198 zeros) possible options for ordering those stops. It also accounts for variables such as weather conditions and road closures. This is a calculation that the human brain could never scale.

Many technological advances that shape our reality were first met with skepticism. With that in mind, it’s important to consider how arising challenges have fueled new innovations so that we may better understand how BI will advance in the future.

How Critical Evaluation Sparks Technological Innovation

Competitive intelligence has always been a huge factor in getting ahead. The film, The Imitation Game, which details the life and work of Alan Turning, tells the story of how technological need, and perseverance in the face of doubt, can create unmatched competitive advantages.

During World War II, Turing (along with fellow mathematicians and cryptanalysts) worked for British military intelligence to decipher the encryption of the German Enigma machine. The Enigma machine was used by the Germans to encrypt military communications. Once that encryption was broken, the intelligence was used by the Allies to hasten the war’s end.

At the time, Enigma was widely believed to be unbreakable, with its total number of possible encryptions in the quadrillions. But Turing and fellow mathematician Gordon Welchman developed the Bombe, a machine adapted from a Polish predecessor that could analyze and break Enigma codes in less than 20 minutes.

Because of his work during and after the war, Turning is widely considered to be the father of theoretical computer science and artificial intelligence (AI). Turing not only proved how valuable competitive intelligence is, but also that AI would have a huge role to play in how we process and understand data.

This idea was further defined in 1965 by Gordan Moore, then electrical engineering researcher at CalTech and the future co-founder of Intel. Moore published an article that posited the number of transistors on an integrated circuit would double every year. Ten years later he revised his hypothesis, saying that he believed computing power would double every two years. This theory became known as Moore’s Law, and despite critics he was correct.

 Today, thanks to Apple, we have more power in our smartphones than was used to send astronauts to the moon, and it only took about 30 years for this technology to evolve.

 These vignettes add credence to the argument that the proliferation of data-driven technology knows no bounds. Acquiring the right tools to analyze data and make it intelligible, as it exponentially grows, is mission-critical for all businesses. Further evidence of this fact can be found by outlining business intelligence milestones over the past 50 years.

The Rapid Evolution of Business Intelligence

The term business intelligence was coined in 1958 by IBM researcher Hans Peter Luhn, who defined it as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” At the time, BI in practice required teams of analysts to pore over information on paper.

 In the late 1950s and throughout the 1960s, IBM developed new ways to store information, through magnetic hard disks and the development of System/360, one of the earliest commercial databases. Though it wasn’t until the late 70s and early 80s when modern BI took hold.

 During that time, computer-aided decision support systems (DSS) helped employees at various levels solve problems using internal databases and models. This led to more advanced executive information systems (EIS), and group decisions support systems (GDSS), which processed larger amounts of data and helped senior executives make critical decisions.

In 1989, the definition of BI was further refined when Howard Dresner, a Gartner Analyst, described business intelligence as “concepts and methods to improve business decision making by using fact-based support systems.”

In the early 90s, data warehousing helped to streamline data processing by storing lots of information in one location and on-line analytical processing (OLAP) began broadening the realm of DSS. Eventually, extract, transform, load (ETL)—the processes of combining three database functions into one tool—became pervasive. ETL helped streamline data processing as loads on data warehouses increased.

Eventually, the networking of multiple databases, or cloud computing as we call it today, improved processing times by enabling data queries and updates to be handled simultaneously. Growth in technology, along with rising demand to understand the insights that data holds, led us to Big Data. Organizations now have more access to information than ever before, but for most it’s too complicated to understand.

Where Are We Today? Instant Analytics, Current Technology, and the Need for Mobile

Prompted by increasing pressure on data analysts to offer continuously updated reports, today’s advanced BI systems can query and process databases so quickly that metrics are streamlined and organized on user-friendly visualized dashboards. These dashboards solve the problem of time and complexity in data analysis and enable company leaders to make informed decisions within hours or minutes.

The incredible processing power of cloud-networked data warehouses creates connections between multiple BI systems that provide organizations with a holistic view of operational analytics. Until the iPhone was introduced, access to this knowledge was limited to desktop computers.

 Before smartphones, Big Data was historically an enterprise function and many analysts thought enterprise for mobile wouldn’t work. But, the business need for mobile intelligence was too strong to ignore. Retailers needed to put information in the hands of operations, merchandising and regional managers. Pharmaceutical companies needed to provide data to droves of sales representatives. Marketers needed access to metrics when traveling to client meetings and conferences. And the list goes on.

Mobile BI meets these needs by connecting with multiple databases to provide critical information to remote workers through interactive data visualizations. Today, mobile BI gives organizations the freedom to conduct business from anywhere across the globe.

Unfortunately, most BI providers still rely on desktop-powered data analysis and configuration, as well as connections to cloud-based server farms. And porting enterprise solutions to mobile often leads to a poor user experience.

Mobile-first data visualization companies that focus on user experience and in-memory analytics better serve business needs by cutting down on time to analyze. Eventually, companies that embrace mobile processing power and AI will give way to a new era in reporting and decision-making.

Artificial Intelligence and Automated Decisions

Gartner predicts that by 2017, most business users will have access to self-service tools. As this happens, the burden on non-analytical users to understand and make data-driven decisions will increase.

As BI systems and mobile processing power evolve, a company’s competitive advantage could come down to minutes or nanoseconds, based on how quickly data is processed and visualized and how well non-IT users can gain insights from that information. Mobile data visualization will become an even more critical component, as companies will no longer have the luxury of waiting for executives behind desks to make decisions while their competitors beat them in the field. Additionally, AI will automate certain operations that currently require human decision-makers, enabling a level of efficiency we never thought possible.

 Freed from these tasks, and armed with unimaginable insights, the future survival of enterprise organizations and SMBs alike will depend on the tools they have in place and the cleverness of their employees. As Turning taught us, competitive intelligence and creativity has been, and will always be important.

 To ensure success for the next 50 years, business leaders should align their organizations with mobile tools that deliver instant, dynamic analytics today. Because when technology levels the playing field, adaptable organizations will be the ones that live on.

Learn More

Learn more about mobile analytics by reading the other blogs in our mobile series.