What is analytics?

Let's understand what is analytics.The word analytics came into existence from Greece. They had a word called analytikos, which meant involving analysis. What is the difference between analysis and analytics? It's very simple. Analytics always has an element of data associated with it and analytics has more to do with the procedures and adherence to the procedures.

Definitions of analytics

So there are two definitions that I like and we've mentioned the names of the persons who have coded these definitions, the analysis of data, typically large sets of data by the use of mathematics, statistics and computer software. It's important to note here that most of the work that you will be doing in the field of analytics would be supported by some or the other computer software, a programming language or a tool. It is almost impossible to work on those large sets of data and do all the calculations manually. The other definition of data analytics, which is more application oriented, is that analytics is the science of using data to build models that lead to better decisions that in turn add value to individuals, companies, and institutions. So this is a more practical definition and if you just pay attention towards the words mentioned in bold, this kind of sums up everything that you need to know and what an analyst does.

Let's get into different types of analytics. So there are three types of analytics.

1. first one is descriptive. This describes what has already occurred. It helps the business understand how things are going. So this is a past data and you're just preparing a summary of that data and trying to understand. This is more reactive in nature and it has mostly to do with the past.
2. Predictive, which is the most popular when we talk about all types of analytics tells us what will probably happen in the future as a result of something that has already happened to now you're leveraging on the information that's available to you from the past data and trying to make decisions for the future. They held the business forecast, future behavior and results. It is proactive. So basis what has happened, we want to take certain proactive measures what's going to happen in future.
3. And the last one, which is closely relative to predictive, is prescriptive analytics, it helps the business prescribe the right course of action. They not only tell us what probably will happen, but also what should be done if it happens. And it is again, a proactive measure. So predictive tells us what's going to happen. Prescriptive is our action that we should take when we know something's going to materialize.

Languages

Initially the tools to perform advanced analytics were only available via licensing and and the companies used to bear a hefty cost for these. The access was always limited to some large size corporations, but now there ample open source solutions supported by large communities across the globe that are constantly working on these open source solutions. And these come quite handy for all data enthusiasts.

The two most popular languages are R and Python. Both offer integrated suites of software facilities for data manipulation, calculation and graphical display. Now you'd see there's a lot of debate available on these when you go online - which one is better, whether we should use R or we should use Python. The simple response without wasting any time on that debate is that both are good. Choose one and move on.

Analytics use cases

Even if you're not directly involved in the business of doing analytics yourself, you can be assured that your buying behavior, your data, your presence in social media and through a lot of other channels is definitely being analyzed by the people who you do your business with. Nobody at this point in time in the digital age is untouched when it comes to analytics.Our use cases will only emphasize that further.

Text Analytics

80% of the data that's available today is not in a structured format i.e., the data does not naturally come into text and columns. It could be a free expression written on internet like a Facebook post. Text analytics is used to analyze customer sentiments.

Let's say we are in the business of providing technical support to the customers and we provide the technical support through a chat based medium where the customers type their concerns and we immediately on a realtime basis try to cater to their requirements. Now if say a customer is repeatedly using these words - repair, not working, slow, and there could be another set of words, trouble, frustrating, waste, refund, return , escalation. You can see that most of these words have a negative connotation . There could be exceptions here and there, but but overall a customer who was initiating a chat and talking about repairs, he's facing some or the other problem and so is the case with similar words.

So, in this case, you can very well imagine what would be the sentiment of the customer. On the other end, he would be a customer who is almost given up on you. If, say for example, he's a new customer and he has to face all of this, you can very well imagine that even if he survives with your product, he is not going to talk positively about you. That's guaranteed, right? So what can we do to prevent these things from happening and can we take charge of the situation immediately and do a course correction.

HR Analytics

Employee attrition if you know, is a big problem for the companies and they spend roughly about 20% of the CTC, the cost to the company of a churned resource to find his or her backfill. This is just to get the backfill. When a backfill takes that position, he needs a learning curve to be able to come to the same level of performance.  It's about money that you spend and the patience that you have to have to bear with the new employee till the time he catches up. Data scientists have developed algorithms, powered by key indicators that taken together, can dramatically predict an employee's intent to stay with the company.

So let's say there are about 25 to 30 attributes that are critical when it comes to predicting employees' attrition. And if a company constantly keeps an eye on those, it would be able to address the employees issues in a more proactive way rather than waiting for the day when an employee comes in, submits as a resignation and then you try to do an exit interview and then, you try to retain that employee which is almost too late. He already made up his mind to move out.

Customer Lifetime Value

I often ask this while teaching people, why do you think the companies provide you a loyalty card? And a lot of responses come, a mixed set of responses. People say that they want to make me feel good.I earned some loyalty points and I invest those points there so they get more business out of me. Of course, it's in a way to pay your respect that you are a valued customer, but the real model is to be able to track what you're doing. So it's easy in terms of an online transaction to figure out that you logged in and you used a particular user ID and password to make a purchase. Say on an online portal so they know who's purchasing what and they keep a track of it.

But in a brick and mortar store where people just walk in and pick a number of products that they want to buy, say it's a supermarket, how would you track, what's my lifetime value if you do not have a loyalty card.
So if I have a particular number associated with the final billing that I do at the counter, you know that I've logged in after two months and these are the items that I purchased. This could be a good way to track the outcomes of different promotional campaigns that the supermarkets run from time to time. Say, for example, if I gave a very good offer, a huge discount on a consumable that is in regular use of my customers, I will be able to see if they have been able to take advantage of it. 

Crime prevention

This is a very interesting case. But can be generalized across industries and scenarios. we are talking about the problem of tiger poaching in India. What happens is there are a lot of groups that are into this business of tiger poaching and they follow some superstitions that the tiger bones can be used for making certain medicines which are a solution to certain incurable diseases.

Now this has been happening for years and then these people who are poachers are pretty smart so they know the jungles really well at times better than the forest officers themselves. And that's why there is a chase between the two all the time. So over the past few years, computer codes were written and about 25,000 data points have been analyzed.Since 1972 , and across 605 districts on wild life poaching crimes, used this data and applied appropriate techniques, to narrow down on certain hotspots where the tiger crime was more likely to happen and this helped them prevent and control tiger poaching. Read this article for more info.

Health care analytics

Let's talk about healthcare analytics, this is a very important well-known study called Framingham heart study. It is performed in the United States at this name of a town where the people participated in the study. It originated in 1948 when the scientists and participants embarked on an ambitious project to identify risk factors for heart disease.

Today the study remains a world-class epicenter for cutting edge heart, brain, bone and sleep research. What it enabled you to do is that there, when you go to the site, you'll be able to see that putting your characteristics, you can very well figure out what is your risk score to acquire a cardiovascular disease, say in a timeframe of 10 years. You can generate a Framingham risk score that tells you that you are prone to a heart disease given the kind of habits that you have right now, and it's an interesting area so you can see that it's not just in terms of business applications that we talk about analytics.

Conclusion

Analytics is not just about targeting the right customer for a promotion. It goes on to prevent crimes. Analytics goes on to save your live ensuring that you live a longer and healthier life. It can be applied to almost all the industries and sectors. That's why it's fair to say that the world around us is changing and we need to keep ourselves with the right set of skills. Gain those skills with my analytics courses. It's a short post we tried to put together to ensure that you get a sneak peek into what is analytics. If you liked this post, please don't forget to share it. Thank you for your time.

Data analytics: Why it is so important?

Introduction to data analytics

Data analytics is used to find meaningful insight from a set of data. Such a process is conducted with the help of certain qualitative and quantitative approaches. The basic task of big data analytics is to collect, organize, classify, transform and convert into easy to understand data along with visualization.

Today the need for data analytics is so high because every organization is data-driven and continues collecting data from a various internal and external source that may help with the organization's progress, and thus this data needs to be collected, categorized, analyzed and stored in order to derive valuable meaning out of it.

The needs for big data analytics will continue with the amount of data collected every day in the online world.

Types of data analytics

1. Descriptive analytics: With descriptive analytics, descriptions are created based on incoming data and this data is mined with the help of analytics and collected descriptions are made.

2. Predictive analytics: In predictive analytics, the data analytic makes sure that a path for the future course of action is predicted and is reliable based on the available data.

3. Prescriptive analytics: In this, the analytic suggests a course of action based on the predetermined rules and procedures so as to provide the best path that is helpful for the organization.

4. Diagnostic analytics: This type of data analytics is used to find out why a certain situation occurred. Other types of analytics help with forecasting but for this type, it helps in looking in past history to find answers.

Importance of Data Analytics

In today’s world, everything turns into data and to control and properly use such incalculable amounts of data, data analytics has a major role to play.

1. Better and quick decision making: Data analytics help in quickly collecting and analyzing new data from various sources that provide helpful information. With such a quick response, the decision makers will be able to make effective new decisions instantly and save time.

2. Improved efficiency: Since every organization is now data-driven, they collect data from both internal and external sources. The data, when processed with the help of data analytics, internally provides information about employee performance and the various courses of action that the organization can choose from. This increases the efficiency of the business.

3. Reduction in cost: Using technologies like big data analytics can save a lot of money spent on storing a large amount of data. Such analytics also help with making the most effective and efficient business decisions, which in turn help in making products that will provide the highest profit once launched in the market.

4. Better market understanding: Today, collected data is analyzed with the help of various developed algorithms that can analyze and collect a large amount of data at the same time. This process is called mining. This enables data collection from various sources and is later refined in order to get fruitful marketing plans and strategies.

5. Information about the industry: It is also capable of collecting industry-related information and can provide future market and industry scenarios. It can forecast the economy that will be beneficial for any kind of business expansion. Such industrial information will open up many opportunities for the growth of the organization and will help in building a strong brand.

6. Launching new products and services: Data analytics help in collecting information related to the customer which in turn help in understanding the customer demands and satisfaction levels. The organization can then work on launching products and services that will satisfy the target customers.
7. Identifying opportunities: With the continuously changing economy and market conditions, data analytics help in refining the stored data that help with understanding the available opportunities.