The History of the Evolution of Business Analytics

The History of the Evolution of Business Analytics

Imagine what measuring business analytics used to be like before online dashboards or even Excel spreadsheets.

No, we’re not talking about the Dark Ages, but a mere few decades ago. With cross-departmental data collated on handwritten ledgers — with heaps of room for human error and missteps — the evolution of business analytics have had quite a (not so) technological journey until the last twenty years or so.

Business analytics and technology have improved at exponential rates and will likely continue to do so as we look to the future. It’s important to see how far this technology has come to place just how meaningful it has been on business growth throughout time. There are clear and direct correlations between the evolution of business analytics platforms and the booming success of industry expansion.

Business Analytics Defined

Wikipedia borrows authors Michael J. Beller and Alan Barnett’s definition of business analytics from their book, Next Generation Business Analytics

“Business analytics refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.”

Covering all areas of business transactions, industry, and verticals, business analytics measure daily insights in the areas of…

  • Finance
  • Sales
  • Marketing
  • Social media
  • Search engine optimization (SEO)
  • Consumer data
  • Target audience data
  • eCommerce
  • Human resources
  • and much, much more…

When viewing and interpreting analytics on a powerful online dashboard, businesses may track the success (or failure) of particular campaigns or overall performance with regard to total revenue. By studying data over segmented time frames business managers are able to extract key performance indicators to leverage smarter decision-making for forward growth.

Of course business leaders didn’t always have heaps of data directly at their fingertips. The evolution of business analytics has humble beginnings perhaps as early as before any kind of official economy existed.

Business Analytics in a Barter Economy

Let’s take a moment and go waaaaaay back if only for the sake of giving ourselves a baseline example. Imagine how one might have tracked the first barter system without access to pencils and paper, let alone computers. Numerical markings on cave-dwelling walls or with wood and stones are our first glimpse at the evolution of business analytics. A tracking system of sorts would have been needed to trace who had what and when.

This is an obvious oversimplification, but from this example, we can better understand how and why business analytics evolved as industry expanded.

Business Analytics in the Industrial Era

The industrial revolution which began in the mid- to late-1700s brought with it new manufacturing processes with water and steam followed soon after by railroads, steel, and oil. These are complex industries that quickly grew out of their local storefronts into nation-wide companies.

During the late-1800s Frederick W. Taylor introduced the first formalized system of business analytics in the United States. Taylor’s System of Scientific Management began with time studies that analyzed production techniques and laborer’s body movements to find greater efficiencies that ultimately boosted industrial production. Taylor acted as a consultant to Henry Ford and directly influenced Ford’s car assembly line time measurements.

In the early 1900s, Ford measured the time each component of his Ford Model T took to develop through completion on his assembly line. Perhaps a seemingly simple task, but Ford singlehandedly revolutionized not only the automobile industry, but manufacturing world-wide.

It’s safe to say that the earlier days in the evolution business analytics focused mostly on improving production; its efficiencies, quantities, and cost-effectiveness.

Operational Reporting

Operational reporting still functions in most of today’s businesses as a day-to-day summary of what’s happening now. But leading up to the digital and informational ages in the late 1900s, operational reporting rued the day as highly segmented workflow analytics.

This means information was gathered and saved, but typically housed in informational silos that weren’t easily shared company-wide. It’s not that this was anyone’s specific intention, but it was a tremendous challenge to update and share, for example, a handwritten ledger that analyzed the company’s daily reports. Operational reporting resulted in very little integration and low to zero historical data. Organizationally speaking, the challenge of sharing information was great. The bigger the business, the more challenging the data collection process.

Business Analytics in the Digital & Information Ages

Behold the 1970s when computers began to be in regular use at larger corporations. Business analytics in this era were headed by Decision Support Systems (DSS). DSSs grew in popularity as they helped to sort and filter larger quantities of data that assisted executives in data-driven business decision-making. DSS systems helped collate data from various areas of business, for example production and sales, to give key decision-makers a bird’s eye perspective of business in a way that hadn’t really existed before. Examining different slices of data through filtering processes was a game-changing experience in the world of business innovation.

DSS analytics tools are typically driven by the following process:

Automated Inputs →  User Inputs →  Outputs → Results

Computers continued to boom throughout the 1980s and into the 1990s (and certainly beyond) in what’s commonly called the Information Age. A big part of this information was initially historical information. With the technology boom of the Information Age comes a tremendous increase in information storage capacity.

Suddenly, data warehouses could save historical computer data (market trends, growth, pricing) gathered over time and prepped for data analysis. Earnings and operations reports became a regularly accepted way to understand businesses and began to fuel business dealings, investments, and decision-making.

Meet Microsoft Excel

Microsoft Excel built upon this DDS-type platform and introduced its still increasingly popular software in 1985. Excel enables its users to not only sort and filter data, but to program formulas that splice and display the data as specifically instructed. Long gone were the days of handwritten ledgers once Microsoft Excel spreadsheets entered the scene.

Meet Google Analytics

Google Analytics were introduced in 2005. The world was already being overrun by data available since the dawn of the Information Age and finally Google provided a free way for its users to begin to analyze at least some of it. While hardly perfect and arguably not so user-friendly, when considering the evolution of business analytics, Google Analytics is a far cry from the early days of Taylor and Ford’s time and efficiency studies.

Google Analytics allows its users to dive deeper into very specific metrics Taylor and Ford might only have dreamed of knowing. Digitally focused, Google Analytics enables website owners who install a specific line of code into their sites to discover metrics like…

  • Audience demographics
  • New vs. returning users
  • Device type
  • Time spent on site
  • Bounce rate
  • Digital advertising data
  • Total visits, views, click-throughs, and more

Google Analytics is hardly a catch-all platform, but its role in the evolution of business analytics is tremendous as it was one of the first of its kind to deliver immediate accessibility in every household and business that has a computer and website.

The Future of Business Analytics – Predictive and Behavioral Analytics

With the introduction of full-service digital analytics programs like Cyfe, the world of business analytics has taken yet another ramped up turn toward innovation. The future of the evolution of business analytics is bright as we look to these unprecedented features:

Real-time analytics: Real-time analytics are data collected and reported on in-the-moment, or in real-time. An example of this might be that an ecommerce store owner could witness a sale coming through the owner’s website as it happens.

Big data: With huge chunks of historical data available in conjunction with real-time cloud data drawn from a tremendous user base, big data is groundbreaking in its ability to move the evolution of business analytics forward.

Predictive analytics: Based on past trends, predictive analytics looks to big data collected over time to predict future actions.

Automated analytics: Automated analytics are analytics that ultimately require very few to zero manual inputs. Data is automatically analyzed in ways that optimize business systems.

Analytics are still not easy to manage. But dashboards like Cyfe have certainly improved the way we view, digest, and make sense of the information. Either way, analytics are essential, perhaps now more than ever as business and technology continue to grow at an exponential rate.

The reality is that we live in a world today where Data Scientists and Chief Analytics Officers (CAOs) are common and blossoming career paths. Business Analytics is even a degree program at many schools.

The evolution of business analytics will continue to evolve as it has done so throughout the ages. Perhaps what we currently deem the future of business analytics will one day soon be as obsolete as tracking sales with sticks and stones, but in the meantime, let’s agree to appreciate the technology we have and use it to make the best possible business decisions we can.

Check out the latest innovations in business analytics and get started with Cyfe today for free. Discover first-hand how the evolution of business analytics has exponentially improved! No credit card. No obligation. Just insight!



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