What Is Data Analytics?

Businesses need a constant supply of information from their customers to make sure they’re on the right track to maintain relevance in their respective markets.
If we’re looking at a small business that has a small group of customers, it would be pretty easy for that business to gather most of the needed information by simply asking its customers.
You’re probably more ambitious than that, though. You’d want your business to have more than just a small group of customers.
But more customers means more data, which leads to the need for more complex programs to gather and analyze data. This is where data analytics proves to be most valuable.

What Is Data Analytics?

Data analytics is the process of analyzing and interpreting a company’s raw data. By interpreting the data, data analytics can discover trends.
Such discoveries help businesses understand the needs of their customers. This, in turn, makes it possible for the business to make better decisions and move in the right direction.
Traditionally, businesses resorted to manual collection and classification of data. However, using manual techniques made the process extremely tedious and time-consuming.
This inspired business owners to start utilizing computer programs to make the process easier and more time-efficient.
During the 1970s, companies began to introduce electronic methods to help make accessing and using their data easier, faster, and more specific.
Such methods included relational databases, data warehouses, artificial intelligence algorithms, graphic data representation along with other facilities that aid in data analytics.

Why Is Data Analytics Important?

Data analytics enables businesses to analyze their performance, which, in turn, allows them to make well-informed decisions to enhance their products, use better marketing techniques, and make better business deals.
Data analytics can also help businesses compare their performance to that of other companies within the same market, granting you insight into your competitors’ performance.
With that in mind, it’s convincing that 47% of businesses believe that the implementation of data analytics has improved their ability to compete in their respective markets dramatically.

Examples Where Data Analytics Is Valuable

Here are some of the processes for which businesses commonly use data analytics:
  • Acquiring and keeping customers
  • Choosing effective marketing plans
  • Anticipate and avoid potential risks
  • Product development to match customers’ preferences

What Are the Types of Data Analytics?

Once the data is collected and appropriately stored, data analysts start using different analysis techniques, or types, to answer questions regarding the performance of their businesses.
Here are the main types of data analytics.

Descriptive Analytics

Descriptive analytics is usually the first type of data analytics to be implemented by analysts. It collects past data, and by analyzing them, it tells the analyst where the business has been and whether or not it has made the expected progress.
Descriptive analytics is subdivided into canned and en hoc reports.
Canned reports are created by business users or incorporated into business software. They regularly use and store real-time data regarding various aspects of the business like product performance and sales trends. And they are generally saved to be reused at various stages.
En hoc reports, however, are created to answer a question at the moment. They’re designed to be more concise and to the point, enabling departments or businesses to share them.

Diagnostic Analytics

Diagnostic analytics is a more sophisticated technique usually done after descriptive analytics. It helps in discovering anomalies in past trends and finding the reasons behind these anomalies.
It’s further divided into two categories: discover and alerts, and query and drill-downs.
Discover and alerts serve to warn of a potential issue before it happens. Say a drop in employee work hours, for example, as this is expected to cause a reduction in productivity.
Query and drill-downs refer to techniques that dig deeper to find the root causes of a problem.

Predictive Analytics

Predictive analytics uses historical and current data to forecast future outcomes depending on the trends discovered. This is invaluable to business owners as it can aid in highlighting future business opportunities and avoiding crises.
Predictive analytics is carried out by implementing artificial intelligence, data mining, machine learning, and predictive modeling and statistics.

Prescriptive Analytics

Prescriptive analytics uses insights from predictive analytics to predict the outcomes of different business decisions.
This helps businesses know what’s expected to happen with every possible decision they make, which, in turn, helps them make well-informed business decisions.

Duties of a Data Analyst

The duties and responsibilities of a data analyst boil down to the type of business they work for, but the main tasks that are usually required from a data analyst are:
  • Creating and preserving databases. This is done by coding, fixing any errors in the code, and solving other data-related issues as they arise.
  • Collecting data from primary and secondary sources and storing them in suitable formats that are readable to both humans and computers.
  • Applying statistical tools to analyze data sets and discover trends that can be utilized for predictive analytics.
  • Creating informative reports that indicate data patterns and predictions to be delivered to the executive manager.

Data Analyst vs. Business Analyst vs. Data Scientist

The difference between all three jobs can be quite blurry, but it all comes down to what each does with the data.
Data analysts have more of a technical role. They’re required to code and store information appropriately, as well as present the data in a readable format to stakeholders and managers. Data analysts usually hold a degree in computer science or mathematics.
Business analysts take data analysts' jobs a step further by detecting problems and proposing possible solutions for them. These usually require an additional degree in economics, business administration, or finance.
Data scientists identify flaws and possible business chances by looking at and analyzing data visualizations created by data analysts. This usually requires degrees in human behavior and computer science.

Conclusion

In today’s fast-paced business industries, the role of data analytics in performance evaluation and business opportunity detection is quite vital.
This explains the increased demand for data analysts, with a three to one gap between data analyst job listings and people searching for the job.
Having gone through our article, you should now have enough insight to determine whether or not being a data analyst is your dream job.