The dizzying evolution of technology has impacted all areas of our lives, from health, education and entertainment to agriculture, manufacturing and all types of industry.
Practically since the 1980s, the influence of technology has been relentless.
From the commercialization of the first cellular network in Japan in 1979 (1), the spread of the Internet in the 1980s (2) and the launch of the first cell phone with Internet connection in 1996, the Nokia 9000 (3), the foundations on which the current technological trends or technological megatrends were born were laid.
Marketing (5) drove the technological revolution by introducing the “Smart” concept with the first “Smarphone”, the Ericsson R380 (4) to such an extent that it gave rise to a tsunami of the “Internet of Things” (IoT) that triggered the Fourth Industrial Revolution or Industry 4.0, the digital transformation that generated the “Smart” concept, “Smart Factory”, “Smart Things”, “Smart Home”, etc., etc., etc.
And now what has been called “BI” from the English “Business Intelligence”
But isn’t the term “Smart Factory” enough to include “Business Intelligence”, is “Business Intelligence (BI)” just another marketing concept to introduce another class of technology products?
So, what is “Business Intelligence” or BI?
Philip Fisher, late investor, world renowned author of one of the most classic books in the literature on stock market investment “Common Stocks and Uncommon Profits”, mentions the main factors (6) that he distinguished in a company to qualify it as “highly attractive” for investment and they can be summarized as follows:
A company is attractive because it operates with low costs and has the systems in place to identify opportunities to reduce costs or increase profit margins.
A company is attractive because it has a powerful marketing strategy that is totally focused on the customer and has the systems in place to consistently increase sales.
A company is attractive because it knows how to innovate and create new business opportunities.
Could it be said that these three factors define a “Smart Company” equivalent to “Business Intelligence”?
Let’s see how Investopedia, a popular reference for finance and business concepts, defines it:
“Business intelligence (BI) refers to the technical and procedural infrastructure that collects, stores and analyzes data produced by a company’s activities. Business intelligence is a broad term that encompasses data mining, process analysis, performance benchmarking, descriptive analysis, etc. Business intelligence is designed to incorporate all the data generated by an enterprise and present easily digestible performance measures and trends that will inform management decisions.” (7).
Another reference widely quoted today defines:
“It is called business intelligence, the set of strategies, applications, data, products, technologies and technical architecture, which are focused on the management and creation of knowledge about the environment, through the analysis of existing data in an organization or company. The term business intelligence refers to the use of data in a company to facilitate decision making. It encompasses the understanding of the current functioning of the company, as well as the anticipation of future events, with the objective of providing knowledge to support business decisions.” (8).
Quite complete definitions, but in the desire to provoke reflection and the generation of ideas that serve to apply technology with the best possible results, it is pertinent to remember Professor Detlef Rost, German researcher, who defines:
“Intelligence is the ability to solve new problems. Learning from experience and using that experience to solve new problems”. (9)
Now integrating the concepts of Philip Fisher and Prof. Detlef Rost we can ask another type of questions:
Which process is the most costly? And, after implementing an improvement or innovation, did my costs decrease?
What is the average purchase of my customers? And after improving or innovating the service to my customers, did the average purchase of my customers increase?
You can see that the questions are now helping us to learn from experience and from our experiments. The process seems trivial, but in reality it is extremely complicated.
For example, imagine a manufacturing company with hundreds of processes and thousands of inputs. Or a marketing company with hundreds of products that are consumed daily by thousands of people. Worse yet, let’s imagine that the information is scattered in several systems with very different databases. It is impossible to handle and analyze such a large amount of data manually!
Such is the challenge of extracting valuable information for the business (due to the great diversity of specialized systems used in the same company, the digital transformation that many processes are undergoing, the rapidly advancing internet of things, automation and internet phenomena such as social networks), that handling such a large amount of data is in itself a mega technology trend called “Big Data”
The phenomenon of digital data growth is so brutal that we not only hear talk of “Big Data”, but also of “Data Mining”, “Analytics” or “NLP” (Natural Language Processing), “Machine Learning” or “AI”. What finally ends up becoming a mixture of concepts that are mixed, turning BI (“Business Intelligence”) into a nebulous thing where everyone has their own opinion about what BI technology is.
Therefore, it is necessary to try to identify, order or classify the technological tools that we can use in our companies. In this effort, we propose a simple example of giving a verbal command to a Smartphone to “Play U2!” and although it seems trivial, we can distinguish 3 major challenges:
The challenge of communication. In this example, verbal language and air were used as the means to give the command to the device (cell phone).
The challenge of finding the information. In this example, a “command-service” type interaction was established, where the device detected the command as valid (command) and related it to a piece of data (a song or an author). Then, the “smartphone” program started searching for the song or author among all the albums (which we will call “database”) until it found the song or author equal to the one that was ordered.
The challenge of analyzing the information. In this example, the smartphone performed a basic analysis of searching for a song called U2 but did not find it. What it did find is the group called U2, then it selected all the songs of that group and then started playing them.
So we can distinguish up to 4 kinds of mega technological trends that play a preponderant role in “Business Intelligence”:
- Language Technologies: includes all natural language processing software (human verbal language), “NLP” (Natural Language Processing), but also technology for human facial and emotional recognition.
- Big Data Technologies: Comprises all the software to handle large amounts of data, coming from different unorganized sources (e.g. social networks) (10) and generated at high speed (e.g. all those that are beginning to be generated in real time with the internet of things).
- Analytics Technologies: Comprises all software to extract meaningful information from data (“knowledge”). Data mining or “Data Mining” can be classified under this heading, and of course all Artificial Intelligence software (and subsets of this such as “Machine Learning” or “Deep Learning”).
- Integral Business Intelligence Technologies: In fact, companies in the industry are integrating Big Data, Analytics and Artificial Intelligence technologies to develop more specialized Business Intelligence solutions.
So what is Business Intelligence Technology?
Finally, we can define Business Intelligence (BI for short) as a set of technologies that provide meaningful information, in real time and in the “palm of your hand”, to learn and make better business management decisions.
Examples of Language Technologies:
Stanford’s Core NLP Suite, Natural Language Toolkit, Apache Lucene and Solr, Apache OpenNLP, GATE, Apache UIMA, TiMBL.
Technology for human facial and emotional recognition is included:
Microsoft Azure, DeepFace, Noldus, NVISO, Affectiva, Kairos, SightCorp, SkyBiometry, FaceCognitiveServices.
Examples of Big Data technologies:
Apache Hadoop, CrowdEmotion, Text Emotions, Synesketch, Adoreboard, Domo, TeraData, Tibco, Panoply.
Examples of Analytics technologies:
IBM Watson Analytics, Apache Spark, Microsoft Azure, Oracle Analytics.
BI technologies (integrating the above technologies):
SISENSE, Microsoft Power BI, Tableau, Qlik, Tibco, SalesForce, ThoughtSpot, SAP BI, MicroStrategy, Looker.
- Wikipedia: https://en.wikipedia.org/wiki/1G
- Wikipedia: https://en.wikipedia.org/wiki/History_of_the_Internet
- Wikipedia: https://en.wikipedia.org/wiki/Nokia_9000_Communicator
- Wikipedia: https://en.wikipedia.org/wiki/Smartphone
- Artículo en blog de Linnet: https://linnetware.com/2019/01/01/iot-inteligencia-de-negocios/
- Fisher, Philip A.. Common Stocks and Uncommon Profits and Other Writings (Wiley Investment Classics) (p. 279). Kindle Edition.
- Investopedia: https://www.investopedia.com/terms/b/business-intelligence-bi.asp
- Wikipedia: https://es.wikipedia.org/wiki/Inteligencia_empresarial
- Britannica: https://www.britannica.com/science/human-intelligence-psychology
- Oracle: https://www.oracle.com/mx/big-data/guide/what-is-big-data.html