Evolution in Business Data
Evolution in business data is the slow process of change from one form or level to a better or higher one or that brings into being a superior or new order. Evolution does not occur in a straight, steady progression but is marked by false starts and dead ends, random leaps in different directions, and long periods of no fruitful activity.
A few years ago the world of business intelligence has been totally turned upside down. the data have become big, it has adopted cloud computing which you can store your document and use it for other stuff, for example, spreadsheets took a backseat to actionable data to actionable data visualizations and interactive dashboards.
According to Richard Millar Devens 1865, the roots of business Intelligence can be traced all the way back to work. A banker did the best in his competitors by gathering information about market trends and use it to propel his business to success. A computer scientist named Hans Peter Luhn wrote an article in 1958 that would later become the basis for how they understood business intelligence in this day and age. the importance of using technology is for simplifying the process of gathering data better than writing it down with your hand
https://medium.com/datadriveninvestor/the-evolution-of-business-intelligence-
Business analytics and technology has improved in a exponential rates and is more likely to continue improving in 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 Intelligence
Over 150-year technology has been evolved naturally and associated with formation. even though its origins occur the invention of computers, it was only after they were widespread that business Intelligence grew in relevance and its development, from that time became paired with the evolution of computers and databases.
https://www.toptal.com/project-managers/it/history-of-business-intelligence
Sometimes they use business Intelligence as the umbrella for all analytics but originally was used as the term for what is now call Descriptive Analytics. some recommend flexibility on this one as it seems not everyone is in the same place.
First things first. This is the first of multiple articles about data analytics and although it would certainly be easy to talk about it all in one shot, I recommend baby steps. However, not to worry, there’s more to come and as Descriptive Analytics is the basis for everything else you’re hearing about from vendors and probably your boss, everything starts right here.
Descriptive Analytics – The Basis & Basics of All Things Dashboard
Descriptive Analytics answers the “what happened.” I also think of descriptive as answering the question “what is”. You see this type of analytics every day. For example, when I receive my electric bill for the month it shows the energy consumption for my address for the current month as well as the last six months. It unfortunately also shows how much I owe the utility because of that consumption.
This bill shows usage data in a graphical way which helps me quickly grasp how my usage has changed over time. The key point is that the data is a simple report on “what happened” at my address and why the charge on my bill is the charge.
Three Types of Analytics
The big data revolution has given birth to different kinds, types, and stages of data analysis. Boardrooms across companies are buzzing around with data analytics offering enterprise-wide solutions for business success. The key to companies successfully using Big Data is by gaining the right information which delivers knowledge, that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.
https://github.com/businessdataevolution
Big data analytics cannot be considered as a one size-fits-all blanket strategy. In fact, what distinguishes the best data scientist or data analyst from others, is their ability to identify the kind of analytics that can be leveraged to benefit the business – at an optimum. The three dominant types of analytics –Descriptive, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data that they have. Each of these analytic types offers a different insight. In this article, we explore the three different types of analytics -Descriptive Analytics, Predictive Analytics and Prescriptive Analytics – to understand what each type of analytics delivers to improve on, an organization’s operational capabilities.
https://www.dezyre.com/article/types-of-analytics-descriptive-predictive-prescriptive-analytics/209
Example of companies that use big data
Starbucks, Burberry and more. Nathan Sykes lists innovative ways companies are using big data and AI to improve performance and boost sales. The world is advancing at a breakneck pace on many fronts, especially when it comes to technology, innovation, and modern experience.
The American Express Company is using big data to analyze and predict consumer behavior. By looking at historical transactions and incorporating more than 100 variables, the company employs sophisticated predictive models in place of traditional business intelligence-based hindsight reporting.
Understanding Predictive and Descriptive Analytics
A lioness hired a data scientist (fox) to help find her prey. The fox had access to a rich Data Warehouse, which consisted of data about the jungle, its creatures, and events happening in the jungle.
On its first day, the fox presented lioness with a report summarizing where she found her prey in the last six months, which helped the lioness decide where to go hunting next. This is an example of Descriptive Analytics.
Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. This is Predictive Analytics. Also, it identified routes in the jungle for the lioness to take to minimize her efforts in finding her prey. This is an example of Optimization.
Finally, based on the above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically. This is Automation.
Prescriptive Analytics
Big data might not be a reliable crystal ball for predicting the exact winning lottery numbers but it definitely can highlight the problems and help a business understand why those problems occurred. Businesses can use the data-backed and data-found factors to create prescriptions for business problems, that lead to realizations and observations.
Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximize key business metrics. It basically uses simulation and optimization to ask “What should a business do?
Vilakazi,s Helen Digital Blog 