Very many companies are currently collecting a lot of data from their business activities, and are sitting on a gold mine of data that could help propel their businesses to the next level. This data, to the companies that are unaware, is collected in raw form and could help propel their businesses to the next level.

So What is Data Analytics?

Simply put, data analytics is the process of analyzing raw data to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions.

The primary objective of data analytics is to address specific questions or challenges that are relevant to an organization to drive better business outcomes.

The demand for data analysts is constantly rising, with a report in 2020 showing that it is one of the seven high-growth emerging professionals, at 41% per year.

Its good to note that there is a difference in a data analyst and data scientist, mainly:

1. What they DO with data
   A data analyst will look at specific problems or challenges to be addressed by the business. They do this by collecting the data, identify trends and patterns and visualise their finginds in form of charts, graphs etc. to the business stakeholders.
   Data Scientists on the other hand consider what the business should be asking. They devise models and algorithms based on the data, running custom analysis, writing algorithms and devising predictive models to elp the business.
   Data analysts ofen work on request to solve specific questions at the time, nad data scientists build systems to to automate and optimize the overall business functions.

2. Tools and skills
   Analysts are typically proficient in spreadsheets, SQL, R, Python, SAS and data visualisation(PowerBI and Tableau); while data scientists are good with most of the above, including object-oriented programming, statistics and machine learning.

Importance Data Analysis

Data analysis is important in improving how we work, make choices and solve problems.

it is applied in:

• Measuring Performance: To measure organizational performance, employee productivity, and the effectiveness of strategies.

• Improving Efficiency: Enhances efficiency by identifying areas for improvement and optimizing processes.

• Better Decision-Making: Helps in making smarter and more informed decisions by analyzing patterns and trends in data.

among others.

Steps of Data Analytics

1. Business needs: What question or challenge you hope to solve. At this stage, you’ll take a clearly defined problem and come up with a relevant question or hypothesis you can test.

2. Collecting Data: Getting data from different places like websites, surveys, or business records. you will use tools like SQL here.

3. Organise the data: Often the most time consuming and a very crucial step, it involves cleaning up the data by fixing mistakes, removing duplicates, and putting it in order so it's easier to look at.

4. Analyzing Data: Using math and computer programs to find patterns, trends, or interesting facts in the data.

5. Sharing your findings: Understanding what the data is telling us and using that information to make choices or plan what to do next. Its also a good times to mention your limitations and what fusrther analysis can be conducted.

The Types of Data Anaytics

1. Descriptive Analytics: Looks at past data to tell you what happened before.

2. Diagnostic Analytics: Tries to figure out why something happened.

3. Predictive Analytics: Uses past data to guess what might happen in the future.

4. Prescriptive Analytics: Suggests what you should do based on the data analysis.

Industry Application of Data Analytics

Data Analysis is applied to a wide range of idustries around the world. Data analysts collaborate with different teams all to determine organisational goals and needs, gather, analyze and report their findings.

Below are some examples of areas of application:

• Technology: to analyse which software functions users like.
• Marketing: to asses effectiveness of past campaigns to see what strategies to replicate or change.
• Insurance: to improve processes, generate leads and increase customer retention as well as analyze a customer’s risk and detect potential fraud.

Data Analytics is major factor driving the future of technology given that it is all around us. With the increasing rise in remote and hybrid workiing, many people can consider data analytics as a possible career path with flexibility and independence.

References:
1. An Introduction to Data Analytics

2. What Is Data Analytics: The Ultimate Guide

3. A Complete Guide to Data Analytics

4. What is Data Analytics? A Complete Guide for Beginners