I remembered a particular product of a carbonated drink manufacturer, I then found out what happened to the product and searched on twitter to see the public opinion about the product in recent times. I later noticed the promoted tweets that appeared to me were from the manufacturer whose product I searched. This was analytics at work they presumed I had an interest in their products and so I kept seeing advertisements prompting me and keeping that interest alive. This event made me decide to pen this article. The effect of analytics in our modern connected world runs in the shadows, but when you know where to look it brings order to the perceived chaos that happens all around the modern world.

Data analytics is the science of analyzing raw data to transform such data into information. Usually a database seems to be random and chaotic. Analyzing such chaos often extracts order and ensures that we can extract unnoticed patterns. We often find trends and metrics after we have analyzed data. We regard these trends and metrics as information. The information extracted are often used to optimize the goals of businesses and industries. Businesses have woken to the benefits of collecting and analyzing data. 

We can say investigative research which is common in academic environments is a subset of analytics. Analytics usually collates data from all sources possible normally without a general aim as at when collation occurs. Investigative research collates data, usually with the research in view. We can then investigate the data collected by analytics companies arbitrarily with the specific question being asked. One benefit of this method is that often, wild-card trends get noticed as opposed to a narrow scoped data collection exercise where certain tendencies lose out. Some of the biggest data centers are: Prolifics, Clairvoyant, IBM, HP Enterprise, Teradata, Oracle, SAP, EMC, Amazon, Microsoft, Google, VMware, splunk, Alteryx, Cogito, etc.

There are four basic types of analytics: Descriptive, Diagnostic, Prescriptive and predictive analytics. Descriptive tries to explain what has happened within a given time frame. Has more sales occurred this month compared to last month? Has the number of views gone up with adjustments made? Are the publications from this view point drawing increased views as opposed to the other viewpoint? Etc. Diagnostic tries to explain why events occurred. This often involves using hypothesis and is more closely related to academic investigative research. Why did the company fail? Why did the public reject a particular candidate in the polls? Did the renovation of the store cause a drop in sales? Etc. Prescriptive suggests actions based on the data collated. Hot summers translate to an increase in demand for carbonated drinks therefore production should increase in the summer months. People spend longer on websites that use webdings font as opposed to the vivaldi font therefore a website using the vivaldi font should switch to webdings. Predictive tells what is likely going to happen. Last year there was a dip in sales in the winter, so we should expect the same this year. There’s an increased advocacy against carbonated drinks and data suggests people are responding positively to the campaign therefore there would a decrease in sales in response to these advocates, etc. 

The power of analytics cannot be over emphasized it is a very powerful tool any industry can use to achieve the desired goals. Healthcare professionals use these outputs in different aspects of their field. They know when to increase orders for specific drugs, know when to encourage consultants to take holidays as demand for their services reduce in specific periods of the year, know the demography of people to advocate a change in lifestyle for, as those who continued similar paths had adverse health issues later on. Retail shoppers know from their large trove of data what to do and when to do these actions to meet the dynamic demands of their customers. These same data are used to recommend changes and shifts to the producers of goods being hawked in these outlets to optimize profits for all the parties involved.

The reporting of the news baffled me a lot till I recognized the influence of analytics, from the formation of headlines to the interpretation given to news analytics play major roles in the decision processes by the various news agencies. I could report a cat killing a dog as “THE FELINES ARE TAKING OVER THE ANIMAL KINGDOM” it is all for dramatic effect and to increase the interest of readers in the stories. The downside for readers is that the dramatization of these stories make them lose their value. Also, the perception based on analysis that certain information is not of mainstream interest means that the suppression of news sometimes occurs. News agencies are usually for-profit organizations and strive to make their profits just like most companies would. However, they are open to libel and lawsuits whenever they lie, so even though they might push the boundaries of truth for dramatic effect, they recant stories they find to be false. The next time someone tells you news is fake, ask them to sue. Recognize the influence of analytics the next time you are reading the news so you can digest the information provided in a better way.