In my last story, I tried to put some light on what is data and why it is the new oil while also putting some focus on Big Data analytics and its advantages. In this story, I will try to dive a bit deeper into the world of Big Data Analytics where we will find out about the types of analytics performed and their meaning.

How do we define Data Analytics

“Data Analytics is the science of analyzing raw data in order to make conclusions about that information”. Also, we can say that Data Analytics is the science of analyzing data using analytical tools (statistics and computing devices). All thanks to the technological advancements, today many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Why is it important?

Data Analytics techniques are used to extract or reveal trends from the raw data which would otherwise be unnoticed or lost in the massive amounts of data. Further, the information extracted can be used to improve, optimize processes to enhance the overall efficiency of a business or system.

It helps companies save so much money, develop better marketing strategies, improve the efficiency in procurement, support the growth of business and differentiate themselves from other competitors in the industry.

“Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.”

The above given statement was made by H. James Harrington, and it very well states the importance of data (measured facts) and also brings forward the importance of data analytics. By performing Data Analytics one can describe the data and in the process get a fair level of understanding about it and listen to the story the data wants to convey. Further, on getting the hang of the story an analyst can take action based on his understanding and need of the hour. One can opt for creating some cool visuals (graphs, figures etc.) from the data, or can even run some predictive models to look into the probable future. Thus, Data Analytics assists us in extracting information/insights from the huge amounts of data available. It is practically not possible to do that on the BIG DATA using conventional tools and techniques. Data Analytics on BIG DATA demands for high end technology, statistical and programming languages to make life easy for humans working on it.

Data Analysis involves a certain process which include some steps which are to be followed:

· The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or be divided by category.

· The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.

· Once the data is collected, it must be organized so it can be analyzed. Organization may take place on a spreadsheet or other form of software that can take statistical data.

· The data is then cleaned up before analysis. This means it is scrubbed and checked to ensure there is no duplication or error, and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Where all Data Analytics is used?

“Whether you’re the world’s greatest detective trying to crack a case or a person trying to solve a problem at work, you’re going to need information, Facts. Data” — — Sherlock Holmes

Wherever there is data one can use Data Analytics. But let me put forward a few areas where Data Analytics is applied:

1. Transportation :

A few years back at the London Olympics, there was a need for handling over 18 million journeys made by fans in the city of London and fortunately, it were sorted out.

How was this feat achieved? The TFL and train operators made use of data analytics to ensure the large numbers of journeys went smoothly. They were able to input data from events that took place and forecasted a number of persons that were going to travel; transport was being run efficiently and effectively so that athletes and spectators can be transported to and from the respective stadiums.

2. Risk Management :

In the insurance industry, risk management is the major focus. What most people aren’t aware of is that when insuring a person, the risk involved is not obtained based on mere information but data that has been analyzed statistically before a decision is made. Data analytics gives insurance companies information on claims data, actuarial data and risk data covering all important decision that the company needs to take. Evaluation is done by an underwriter before an individual insured then the appropriate insurance is set.

3. Healthcare:

One challenge most hospitals face is coping with cost pressures in treating as many patients as possible, considering the quality of healthcare’s improvement. Machine and instrument data use has risen drastically so as to optimize and track treatment, patient flow as well as the use of equipment in hospitals. There is an estimation that a 1% efficiency gain will be achieved and would result to over $63 billion in worldwide health care services.

4. Internet/Web Search:

When one mentions the word ‘search’, the first thing that comes to the mind is ‘Google’. In fact, Google to some point can be used in place of ‘search on the internet’ by saying ‘Google it’. Well, apart from Google, there are several other search engines such as Bing, Yahoo, Duckduckgo, AOL, Ask, etc. Each of these search engines is as a result of data science applications because they use algorithms to deliver the best results for any search query directed at them in just a split second. In respect to this, Google is known to process over 20 petabytes of data daily. Of course, without analytics and data science, this feat wouldn’t have been possible.

Types of Data Analytics

Data analytics is broken down into four basic types.

1. Descriptive analytics describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?

Descriptive analytics is necessary to make raw data understandable to managers, investors, and other stakeholders. Sales of $1 million may sound impressive, but it lacks context. If that figure represents a 20% month-over-month decline, then it is a concern. If it is a 40% year-over-year increase, then it suggests something is going right with the sales strategy, but it still needs the larger context of what the targeted growth was to fully judge. It is meant to provide an accurate picture of what has happened in a business and how that differs from other comparable periods. These performance metrices can be used to flag areas of strength and weakness in order to inform management’s strategy.

2. Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales? It takes a deeper look at data to attempt to understand the causes of events and behaviors. It lets you understand your data faster to answer critical workforce questions.

3. Predictive analytics moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year? Predictive analytics allows organizations to become proactive, forward looking, anticipating outcomes and behaviors based upon the data and not on hunch or assumptions.

4. Prescriptive analytics suggests a course of action. It goes a step further than Predictive analytics and suggests actions to benefit from the prediction and also provide decision options to benefit from the predictions and its implications. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.

Here’s a summary of the stages of data analysis:

Descriptive analytics : What happened?

Diagnostic analytics : Why did it happen?

Predictive analytics : What could happen in the future?

Prescriptive analytics : How should we respond to those potential future events?

The picture above gives us a brief idea about the tools use, limitations and when to use aspects of the most used three types of analytics.

To conclude, Data Analytics is something whose use is growing exponentially across the world for varied objectives. Data should not be dumped as a garbage rather it ought to be used as a brick to lay the foundations of skyscrapers.

In next story I will talk more about Predictive Analytics and what all does it require to perform predictions.

Source: Artificial Intelligence on Medium

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