Introduction to Data Analytics

Data Analytics – An Introduction

We all are growing in a data-driven world and have tons of data getting generated every day. Almost every company is generating a huge amount of data and that data is needed to be analyzed in any possible way so that meaningful insights can be drawn that help to create different strategies, make better decisions based on the data for the future and increase the revenue altogether.

It is no brainier that the data is going to be the new fuel for our lives in the coming years; besides we will need more trained resources who can work on all of this raw data and make meaningful stories out of it for the rest of the world. Even though this is the ever-growing field, a lot of young crowd is still not familiar with most of the key components of Data Analytics and at the same time young professionals are trying to dive deep into the ocean of analytics. The reasons are pretty simple; they need a career that is fascinating and at the same time can pay them handsome packages. Through this article, we are trying to make young professionals educate about the field of data analytics.

Data Analytics: What Exactly is it?

The process of Data Analytics is vast and has some key steps that are needed to be followed in a sequence. This sequential following of procedures will at the end allow the user a deep understanding about what exactly the data is trying to tell you in terms of your past (where you have been), your present (where you are at the current situation) and most certainly about your future (where you should be).

Data analytics can be considered as a bunch of techniques using which we read, transform, and analyze the raw (unstructured data) and make some meaningful conclusions based on it.

The sequence is pretty simple, and we have tried to explain it below:

• The first step of any data analytics project is Descriptive Analysis. This is a method uses the data in order to extract what has happened? Till date. This step usually also considered as a step where you prepare your data for further advanced level of analytics which possibly move towards the prediction. This method includes the tracking of historical trends, measuring indicators etc. However, this method does not do the direct predictions or does not provide any conclusive decisions to be made based on the data.

• The next step that comes in the picture is Advanced Analysis. This technique uses the data already prepared under the first step, feeds it to the system, and tries to find out a strong statistical/machine learning technique that can be appropriately used to describe the future trends based on this data. This technique can also be considered as Pre-Prediction Analysis in the data analytics cycle. This technique usually tries answering the question: What happens if?

• The third and last step is all about using the analytical information collected in the previous two steps to allow the system to make predictions. This step uses all the data we have used to build a suitable model, trains the machine based on that model data, and then once the model is ready, it tests it on different sets of data with the same attributes to make predictions.

These predictions make management aware of what could happen in the future and accordingly allows them to make better decisions for business growth.

Whereas if we go further deep, we could also segregate the analysis processes into Qualitative Analysis and Quantitative Analysis.

Qualitative Analysis

qualitative approach of analysis includes the methodologies that allow you to analyze the data based on the qualitative factors/attributes of data. Some of the major qualitative attributes are the quality of the product in terms of star rating. One Star, Two Start, etc. are the qualitative measures of any product; where one star means the product is below average or not good and on the other hand, five stars mean the product is very good to purchase. Another example can be considered as the quality in terms of worst, good, and best of a product. Where best considers that the product is outstanding and worst considers that the product is below expectations. One more example can be used as short and tall. Short is comparatively below that the tall. The same is the case with big and small. These are the qualities that don’t have any particular measure to describe who’s superior and who’s inferior. Instead of that the word themselves are suggestive about who’s superior and who’s not.

Quantitative Analysis

The quantitative approach of analysis ideally works on the numerical data values which can be quantified to be analyzed. For Example we can use the number of products sold across different categories to analyze which product is the highest selling product or product in demand. We also can use the revenue generated through each country as data to analyze which country is the frequent buyer of our product. Etc. are the examples of quantitative data values and analysis associated with those.

However, whether you analyze the qualitative or quantitative data, the steps involved for analyzing the data are the same, which we already have discussed earlier.

Let’s dive deeper into the concepts of analytics. We first will try what is data and where it can be stored for easy retrieval and access.

Data: What it is actually?

Data is a new fuel in this century to analyze the customer purchasing behavior, their purchasing capabilities or power etc. It is considered as a row unformatted information about customers and their buying, purchasing, etc. behavior. Which can either be in numbers, texts, audio, or in video format.

Since we are using smartphones for most of our needs or trying to visit and order necessary stuff from online shopping stores, the world of data has enlarged and we are generating a lot of data based on our transactions, billings, prices of items which we purchase frequently, a period of month/year we purchase particular items on regular basis, etc.

Certain ERP (Enterprise Resource Planning) systems track this kind of data and believe me these are not the limits at all. Nowadays data can also be the number of times you hit like button on Facebook, or types of post you always share (meme, social awareness post, relationship quotes, celebrity images, etc.) number of times you visit certain webpage of online shopping portal and even the location detector (GPS) from your smartphone generates the data of places you frequently visit.

All this data which is of conventional and unconventional types should be stored somewhere. The first tool that we consider when it comes to storing the data is an Excel Spreadsheet. However, this huge data can’t be stored always into Excel spreadsheets due to the limitations of the tool itself. Therefore, we needed something more versatile when it comes to store the data and manage it. That is how we moved towards databases which can be considered as a part of the data warehousing system. These databases are stored at a server and can be accessed as per the requirement using different data management tools. The most common tool to make query and extract data is SQL.

The process of storing data into databases is not as simple as dumping a row data into databases. There are certain rules associated with the relationships of the attributes which should be followed to have a proper data storage on servers under database. Over the period, technologies are evolved so much so forth that this time-consuming job of figuring out the attribute relationships and storing them correctly has been automated to save time. Even if that is the case, the analyst has to be more than precautions while extracting as well as analyzing the data so that only required data is getting exported as well as analyzed by them.

Now as we have discussed what data is and it can be of any type varying from categorized data types such as numeric to the uncategorized data type such as posts you like on Facebook; we need to discuss the analytical techniques in detail to be well versed of those.

Descriptive Analysis

Now, we are discussing the different analytical techniques. First of those all is Descriptive Analysis which is a first step analysis we do for any data.

Suppose you purchase online groceries for your daily need (and believe me that is the need of the hour through this pandemic situation) and every time you purchase from them, their internal server gets your transactional information saved at the base. For Ex. if you take 10 KG Rice for a month, it gets stored. If you purchase 3KG of detergent every month it gets stored with them and so on. Over the period, they have decent data associated with your purchasing behavior and they can analyze the same to study your buying behavior. This is something a part of descriptive analysis is. You have past information. You are studying/analyzing it with different angels to have better information about the behavior of the customer. This is what comes under Descriptive Analytics. It can consist of conclusions such as average purchase of groceries per month of customer is $3,000 and so on.

Predictive Analytics

After all the primary analysis is done under the Descriptive Analysis section and we have a decent amount of information about the buying behavior of customers, the next part comes in to predict what he could purchase most probably in his next visit. The pattern of your purchase history is being stored every time by the database of online shopping center as we have discussed in the earlier section, the more challenging job is to decide what would be your next purchase from the online store next time you visit the website. To get an idea about this, the store already studied your previous buying behavior and used some advanced analytical techniques to predict that on your next visit you could more likely to purchase the wheat and rice and detergent.

Therefore, as soon as you visit the site next time, you’ll see wheat, rice, and detergent as suggestions to purchase on the home page of the website. And at the same time, they will also suggest some products which they feel you are likely to purchase. Because, these online stores and other concepts are based on a theory/assumption that, if a person buys something; say product A then what are the chances that he or she will purchase product B. This is where the  Predictive Analytics comes in picture.

Machine Learning

The most handsome part of the analytics world is Machine Learning! Machine Learning is a technique using which machine/computer learns itself the patterns and trends within the data and tries to apply different statistical methods to generate thousands of models which can be used to have a stronger prediction about the future based on data available. In the end, the system itself chooses a model that has more close matching towards the data provided and then predicts the future based on the learning it acquired. This is a lengthy and time-consuming procedure for human beings but not for the machines. The origin of Machine Learning methods is as old as the machines (computers) itself. However, the recent two decades have all the roots of its utilization into day to day analytical theory. This is because of two reasons –

1. The volume of data has increased enormously that an analyst can’t study it manually without the help of systems.
2. The machines have more advanced and accurate computing power which is compatible with Machine Learning algorithms.

Conclusion

Through this article, we tried to touch the aspects of data analytics in some way or the other. We tried looking into what is data analytics, what are the steps involved in Data Analytics, What is a data and it’s recent explode in the world, we also tried to see how this large data can be analyzed and how they try to identify the customer buying behavior using data analysis techniques and so. It is never a claim that the information provided in this article is a one and only. Since the field of Data Analytics is so vast that I might have missed some of the other components. But the ones mentioned here, I guaranty are the ones which are the most commonly used methods while analyzing the data.