Business intelligence history pdf

Over million emails are sent per minute. Visualization tools began to evolve to include the end-user even more. More platforms empowered users to complete self-service access, meaning that they could explore and utilize their data on their own, without training.

As more companies offered these capabilities, unique, cutting-edge attributes became the only way to stay ahead of the curve.

One way to achieve both was through cloud BI, which hosts the software on the Internet, reducing storage costs and making access to organizational data and insights faster and more convenient. Tangential to the cloud is the rise of mobile-empowered platforms, which allows users to work with BI on-the-go on smartphones, tablets, and other devices.

As tools are perfected and improved, they are also being made simpler and more convenient, encouraging wider adaptation. Thanks to technology, especially modern computing time, BI now means using real hard and fast data, not just your own insights, to see the whole picture. I am hoping to view the same high-grade content from you later onn as well.

Thanks for a great article. GA standard is free and widely used, but compared to data standards and functionality in a BI-software, it is basically crap. About Categories Contact. Step 1 of 2. Privacy Guarantee - Your information will remain secure. You'll only be connected with vendors who are the right match. View our privacy policy. Step 2 of 2. Generic filters Hidden label. Hidden label.

Justin Heinze May 27, Business intelligence existed before technology. Origins and Development until Technology did not advance to the point where it could be considered an agent of business intelligence until well into the 20th century.

But it was a new and clumsy technology. Most importantly, it was very difficult to use. In later years, this phase of development became known as business intelligence 1. Business Intelligence 1. Business Intelligence 2. BI was no longer an added utility, or a mere advantage. It was becoming a requirement for businesses looking to stay competitive, and even to remain afloat, in an entirely new, data-driven environment.

Empowering End Users into the Modern Day The agility and speed of the mids business intelligence platform has undergone an intense refining process. Self-service tools and visualization features rely on one another for their growth.

Companies required even more visualization tools to actionably make sense of it. Cloud BI and Mobile BI As more companies offered these capabilities, unique, cutting-edge attributes became the only way to stay ahead of the curve. Vendors experimented with faster and cheaper tools. Share this:. Filed Under: BI Tagged With: big data , business intelligence , hans peter luhn , history , howard dresner , richard millar devens , sir henry furnese.

Our exclusive report will walk you through the process and help you select the perfect solution. Download Now. Comments Pat Hennel says. Due to being very one-dimensional, the flexibility of their use was very limited. Yet, more and more successful cases of profitable use of data came to be. One of the most famous at the time came from Nielsen. Used for audience measurements, the marketing tool known as the Nielsen rating was used to gauge how many people were watching a particular TV show at any time, using a device called the Audimeter , which was hooked to up to a television set and recorded which channel was being watched.

Nielsen ratings were considered the most looked-at national rating report in the TV industry. National ratings would be available every day of the week, every week of the year. Near the end of the 70s, Larry Ellison and two friends released the first commercial version of the Oracle database. It was the first true relational database management system in the market, replacing the ideas used up until then of hierarchical databases and network databases for a more robust structure, which allowed much more flexible searches.

Lower prices for storage space and better databases allowed for the next generation of business intelligence solutions. Ralph Kimball and Bill Inmon proposed two different but similar strategies to the problem of having all the data of the business in the same place to be able to analyze it.

These were data warehouses DW. Inmon is recognized by many as the father of the data warehouse. Data warehouses are databases designed to aggregate lots of data from other sources of data mostly other databases , allowing a much deeper analysis with the ability to cross-reference these different sources. It was still, however, too technical and expensive. Reports needed to be run and maintained by a host of expensive IT technical staff. Top management at the time would live by the outputs of BI solutions like Crystal Reports and Microstrategy.

And, of course, there was Microsoft Excel released in Business intelligence was now an integral part of the tools available for the decision-making process. In the 90s, data warehouse costs declined as more competitors entered the market and more IT professionals got acquainted with the technology.

Data was now commonly accessible to the corporate staff in general, not just top management. However, the problem at this point was that asking new questions was still very expensive. This was also the period where enterprise resource planning ERP systems became popular.

These are huge management software platforms that integrate applications to manage and automate aspects of a business. They also provided structured data for the data warehouses and in the following years would become the heart of every major company in the world. This would have a profound impact on how people produced and consumed data for the following decades. It was now widely considered a requirement to stay competitive. From the solution providers perspective, the abundance of solutions started to coalesce in the hands of a few large competitors, like IBM, Microsoft, SAP, and Oracle.

A few new concepts emerged during this period. It was easier said than done, providing lots of technical challenges. The concept, however, was so useful that in the following years the available solutions in the market would adapt to employ this strategy. As data became more and more abundant, and BI tools proved their usefulness, the development effort was directed toward increasing the speed at which the information would become available, and to reduce the complexity of accessing it.

Tools became easier to use, and non-technical people could by now gather data and gain insights by themselves, without the help from technical support. By , the increasing interconnectivity of the business world meant that companies needed real-time information where data from events could be incorporated in the data warehouses as they happened in real time.

This is the year Google Analytics was introduced, providing a free way for users to analyze their website data. This is also the year the term big data was first used. To cope with the additional storage space and computing power required to manage this exponentially increasing amount of data, companies began to search for other solutions.

Building larger and faster computers was out of the question, so using several machines at once became a better option. This was the seeds of cloud computing. In the past 10 years, big data, cloud computing, and data science became words known to pretty much anyone. It is hard at this time to acknowledge which new advancements were most impactful in these last years. However, there are a few interesting cases that have shown the growing power of modern analytic tools.

In , The New York Times published an article describing how Target accidentally discovered the pregnancy of a high school teenager before their parents. Through analytics, they identified 25 products that when purchased together indicate a woman is likely pregnant.

An enraged father walked into a Target outside Minneapolis and demanded to see the manager. He complained about her daughter receiving coupons for baby clothes, even though she was still in high school. I owe you an apology. In the best case scenario, prescriptive analytics will predict what will happen, why it happens, and provide recommendations.

Larger companies have used prescriptive analytics to successfully optimize scheduling, revenue streams, and inventory, in turn, improving the customer experience. As a new tool, it has improved significantly the flow of useful information to decision makers. Data for Streaming Analytics can come from a variety of sources, including mobile phones, the Internet of Things IoT , market data, transactions, and mobile devices tablets, laptops.

It connects management to external data sources, allowing applications to combine and merge data into an application flow, or update external databases with processed information, quickly and efficiently. Streaming Analytics supports:. Staff can make quicker, more informed decisions and allocate resources more efficiently. You must be logged in to post a comment. OLAP Online analytical processing OLAP is a system that allows users to analyze data, from a variety of sources, while offering multiple paradigms, or perspectives.

OLAP supports three basic operations: consolidation drill-down slicing and dicing Consolidation involves combining data that can be stored and processed in multiple ways. Data Warehouses Data Warehouses started becoming popular in the s, as businesses began using in-house Data Analysis solutions on a regular basis. Business Intelligence vs Analytics Currently, the two terms are used interchangeably.

Descriptive Analytics Descriptive analytics describes, or summarizes data, and is focused primarily on historical information. Predictive Analytics Predictive analytics anticipate the future.

Prescriptive Analytics Prescriptive analytics is a relatively new field, and is still a little hard to work with. Streaming Analytics supports: Minimizing damage caused by social media meltdowns, security breaches, airplane crashes, manufacturing defects, stock exchange meltdowns, customer churn, etc. Analyzing routine business operations in real time. Finding missed opportunities with Big Data. The option to create new business models, revenue streams, and product innovations.

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