Data analytics in business

A Brief Insight  with Data Science

It is the field of study that involves extracting knowledge and insights from noisy data and then turning those insights into actions that our business or organization can take

so let’s dig into it a little bit more and discuss what are the different areas that are covered by data science

so really data science is the intersection of three different disciplines

we start with computer science then we also cover the area of mathematics and then what I think is the most important is business expertise

So the intersection of these three disciplines is data science and true data science initiatives involve collaboration across all these three different areas types of data science that you can do now what we need to understand here is that we have different data science methods for different questions that we might ask in an organization and these questions can vary by complexity and the value that we get out of them so let’s chart them here by complexity and value okay so the first one that we have here is descriptive analytics.

so this is really about what is happening in my business right and it involves having accurate data collection to make sure that we know what’s happening so a good question we could ask here is well did sales go up or down the next level is diagnostic analytics and this is more about why did something happen so why did sales go up or down and it involves drilling down to the root cause of our problem.

now the next one that we have is predictive analytics so this is about what is likely to happen next right so what will our sales performance be next quarter it involves using historical patterns in our uh in our data to predict outcomes in the future and then finally we have prescriptive analytics so this is about what do I need to do next what is the recommended best action for a particular outcome

so the question we could ask here is what do I need to do to improve sales by 10 right okay so now we can talk about how data science is done and who actually does it so let’s look at the data science life cycle and the first thing that we always must start with is business understanding so this is really critical.

Make sure we’re asking the right question before we go down a lengthy data science initiative and this is where you can see that having the business expertise and the domain expertise can be incredibly critical to make sure that we’re asking the right questions okay so once we’ve defined that we can move on to data mining

This is the process of actually going out into our data landscape and procuring the data that we need for our analysis so once we’ve done that we can move on to data cleaning so the reality of the marketplace is that once we when finding data it’s probably not in the best format that we need it in and it probably has uh some issues with it right it might have rows that have missing values it might have duplicates in it so there’s some preparation and cleaning that we have to do before it’s ready for our analysis

so once we’ve done that cleansing we can move on to exploration, okay so this is the part of the process that allows us to use different analytical tools that can start helping us answer some of the types of questions that I mentioned here earlier and if we actually want to get into some of these higher value questions like predictive and prescriptive

Then we must start using advanced analytical tools such as :

Machine learning tools

These Tools leverage massive amounts of computing power and massive amounts of high-quality data to make predictions and prescribe actions for the future now once we’ve done our exploration and perhaps our advanced analytics what do we do next

well we need to visualize the insights and outcomes of our analysis, in an organization you may have roles like a business analyst you might have data engineers and then you might have data scientists so business analysts are obviously involved in formulating the questions they have the domain expertise

they can help with the business understanding but they’re also involved with visualizing our insights in a way that’s useful for the business right and then we have folks like data engineering folks so these are the people that can help us find the data clean the data and then also help with some of the explorations

we move on to our data scientists so these are the people that will really help us with the exploration they’ll help us with the advanced machine learning techniques and they’ll also assist in the visualization so you can see there’s some overlap between the roles and that’s why it’s critical to have collaboration across these roles

Business analysts have to do some machine learning they have to help out with exploration data scientists sometimes need to go and find the data on their own so there’s a lot of overlap and these different roles must collaborate with each other.

Practical Approach of Data Analytics in Business

If I provide you the data of all Indian citizens according to their latitude and longitude, and if I can tell you (with the help of that data) which shop or mall, or someone’s house is at which latitude and longitude, then you will tell me something like this after seeing the data,

That 30% of the people go out for dining on Sunday. and 10% of the people are spotted on Wednesday, and 20% of the people go out to have fun and enjoy food & drink on Friday night. So you have given me some insights from that raw data.

All I had were the data of latitude and longitude, only in numbers. You have transformed the data into meaning. And that’s what a data scientist does As in, transforming the data into meaningful insights. Now it doesn’t matter what tool you are using for this. As long as you are providing the data to me, you are a data scientist. Recently, data science is becoming a great career option.

But if we go back by 15 to 20 years when the internet has just arrived and data have just started getting collected, there was no existence of Big Data, Data at that time was in a very small amount. If we get the data of the employees of any company, we used to maintain that data in applications like excel sheets.

Someone could calculate and extract the insights by watching it. Not many efforts were needed to transform a huge amount of data into insights. Because at that time, huge meant only 100, 200, or 300 rows of data. Nowadays we have millions, billions, and even trillions of rows of data. We have such a huge amount of data that 20 minutes will be a mock number, it will take a person’s entire life to read that data.

But the insights won’t come out from the data. In the 1990s, the internet was shown existence newly, and only people who had a good knowledge of technology were there on the internet. A very less number of data is used to generate. Smartphones were not there. As time evolved, we got YouTube in 2005.

Facebook came in 2004. After that we got Instagram, and we got Snapchat. These applications were highly influential to bring people online. And people got addicted to this. We have so much data that we have realized that one person is required to extract the insights dedicatedly from the data through the computer so that he can help the business and some excellent decisions can be taken by that meaningful insights.

As time evolved, our machines, computers, the amount of RAM & storage in the computer, and the way we are approaching things from the technology standpoint, are getting lot improved and making the machines a lot more powerful. And the opportunity for data science as a career is getting wider for people.

If I can answer “What is a Data Scientist” in simple words, so Data Scientist is a person who transforms data into meaningful insights. from which, a good decision can be taken by looking at the layman. for example, the example of latitude and longitude if anybody can say that 30% of the people dine out on Sunday by looking at the data Then the owner can increase the capacity of the restaurant on Sunday and he can also hire more employees specifically for Sundays so that everyone gets benefitted from it.

currently, most of the data science tasks are related to the data that are collected from the footprint of the people that are left on the internet. In today’s time, we all are leaving our footprints on the internet. You see any Instagram feed, watch any video anything you do on the internet you are leaving a footprint, you are making a data point This way, every time people interact through the internet, phones, or computers, they generate the data.

Most of the time, that data is collected and stored and then hand it over to the data scientists so that, they can draw insights. and make a profit for the company with the help of that data. Now data collection is another important job for data scientists.

Data science involves collecting the data, analyzing the data, and in turn building models from that data. All three things are very important. Now Machine Learning also comes under data science. The difference between a machine learning engineer and a data scientist is that a machine learning engineer only focuses on the machine learning algorithm. And a data scientist focuses on the overall pipeline of the data including where the data are getting collected and how it has been analyzed.

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