Beginner's Guide to Data Analytics
Understanding how to analyse and extract information from data is critical for many businesses in an age when data collection and storage is more widespread than ever. Introduce data analytics, a field that dates back to the 1960s.
Daily, 2.5 quintillion (1,000,000,000,000,000!) bytes of new data are created. Our data production rate is increasing every year. In the era of Big Data, information itself has become a valuable commodity.
Whoever has the most accurate and complete data will come out on top, regardless of how advanced their technology is.
Now more than ever, businesses around the globe are in a race to protect their customers' personal information and maximise the value of the information they have collected. This is due to the fact that businesses can use the patterns and trends revealed by large data sets to guide operational decisions. Given this, it's clear why Data Analyst positions are in such high demand. The field of data analytics has wide-ranging practical applications. New possibilities in fields as diverse as finance and healthcare can now be explored.
We will cover the fundamentals of Data Analytics and the steps involved if this is your first time learning about it. In later posts, I'll go into detail about each step of data analytics, so stay tuned!
A Definition of Data Analytics
Analysing data sets to identify patterns and draw inferences from the data is known as data analytics.
Customer information from an online retailer, for instance, could reveal which products are most popular with shoppers. The inference drawn from customer data may aid the company in increasing stock of that product or making a crucial business decision.
Step one in any Data Analytics project is to clearly identify the issue that needs fixing.
This preliminary step is essential because it will serve as a compass for the rest of the project's phases.
Sometimes it's hard to pin down exactly what the issue is. Even more so when there are a number of different factors to consider. However, if the issue can be properly identified, half of the battle is already won. Asking yourself a few questions can be an easy way to start addressing this (consider a GAP analysis).
1. What is the current state?
2. What is the target state?
3. What factors are blocking progress?
To formulate a problem statement, try asking these questions.
For example, if a company is experiencing a dip in sales from new leads, the contact centre managers might want to find out the reason. So they answer these questions:
1. What is the current state? — Fewer sales
2. What is the target state? — Increase sales by x% from new business leads
3. What factors are blocking progress? — Contact centre making fewer calls, potential lack of motivation or reduction in volume of leads
After answering these three questions, we will have a clear statement of the issue upon which to base our investigation.
Why are we converting fewer new business leads into customers?
We use this problem statement as a base to proceed with our analysis.
The Data Analytics Life Cycle
In data analytics, there is no one set procedure that applies to every possible problem formulation. However, the Google Data Analytics course suggests that the following six steps can solve most problem statements.
Now, let's quickly run through each of these:
At this stage, you should consult with affected parties, process owners, and upper management to get a complete picture of the issue.
To better understand contact centre behaviour and formulate a problem statement, the data analyst in our example might consult with the business function, project managers, and upper management.
In this stage of the analysis, we will be gathering information from a number of different resources.
In our example scenario, the data analyst would identify sources of call data to later understand if call volumes to new business leads have changed and distribute their findings to the relevant parties.
In reality, data quality is lower than we assume. In this step of data analytics, the data is prepared for analysis by being cleaned, transformed, and made more usable.
It is normal to run through a series of data cleansing responsibilities.
Now we enter the phase of serious analysis. In order to find patterns and trends in a dataset, data analysts apply their expertise once the data has been cleaned and prepared for analysis. Furthermore, this is where you'll discover the solution to the problem description.
Remember that the analysis phase involves some extra formatting, sorting, and filtering to see a better dimension of the data set.
In our example, this is where we learn about the factors like call volumes and the number of new leads that led to a reduction in sales to new customers.
Data visualisation tools such as dashboards, graphs, charts, etc. are used to present the results of the preceding steps to management and other stakeholders. This stage is crucial because we need to present our findings in order to gain the attention of our stakeholders.
What good are these results if nothing is done with them? The last step of data analytics is to put what was learned into practice and fix whatever it was that was causing the problem statement, in this case, a reduction in sales to new customers.
In today's world, data analytics has become a crucial tool for any company looking to expand. I'm hoping you've grasped the fundamentals and can see how they affect the bigger picture of analysis.