Do you know that around 2.5 quintillion bytes of data is created every day? It is an obvious thing as data is an inevitable force that is growing like wildfire every single day. This number is going to reach 149 zettabytes by 2024 if the trend continues. Besides the data, the businesses need to ensure the technical SEO to increase organic traffic

The major reason for this is the businesses going digital these days. Due to the increased usage of the data, the complexity to extract meaningful insights from it will also increase. Consequently the difficulty of businesses has grown way too much. 

The best way forward for the businesses is to resort to data analytics. Data analytics will lead to the data informed decision making and will lead to more leads and conversions which in turn will generate revenue. 

Let us throw some light on the benefits of data analytics and some of the best practices for the same in the post mentioned below. 

About Data Analytics 

With the help of data analytics, collecting, analyzing and extracting the meaningful information from the abundance of available data. Getting data analytics software will enable fast, data-driven, and informed decision making. 

Doing efforts in data analytics will aid in problem solving, minimizing risk, decision making and also predictive analysis. There is no doubt in this that it is a powerful tool providing insight into the past and future. 

One powerful data analytics tool is none other than Google Analytics which every other organization is using for understanding how their web pages are performing. Google trends is also a popular example. 

Kinds of Data Analytics 

There are usually four kinds of data analytics which includes descriptive, diagnostic, predictive and prescriptive analytics. Let us learn about the same in the sections mentioned below. 

Descriptive analysis- It refers to the answers of what has happened or happening. 

Diagnostic analysis- It refers to the reason like why something has happened or happening. 

Predictive analysis- As the name suggests, predictive analysis answers what is going to happen next. 

Prescriptive analysis- Prescriptive analysis answers what should happen next. 

Process of Data Analytics 

Let’s go through the process of data analytics in the sections mentioned below. 

Defining the problem 

The very first thing is defining the problem. The businesses should clearly know what they need from data or what are they looking for? There are businesses who want to analyze their customers like what they are expecting of them and perceiving about their brand. 

Data Collection 

You need to collect the data from available multiple sources. As such, there are two sources to collect the data which include: structured and unstructured data. Structured data is organized data which includes name, email address, phone number, location and others. It is a kind of custom data collected from internal sources such as Google analytics, social media engagement metrics and others. 

Unstructured data is unorganized data which follows no format and accordingly is challenging to collect. The businesses can collect this data from external sources such as social media APIs, audio and video files and others. 

Data cleaning 

The next comes data cleaning. The data analytics team will assist you in cleaning the data i.e removing the redundancies, or any inaccuracies from the collected data. This is the most crucial task of the thorough process but is quite time consuming. 

Data Analytics 

By using the data mining, business intelligence and data visualization tools, the data analytics can be easily implemented. Consequently there comes meaningful and actionable insights. 

Make decisions 

Once you have the data and valuable insights, you can easily make data informed decisions. The further way forward will be taken by the business drivers. 

Benefits of Data Analytics 

Lets have an insight into the benefits of data analytics. 


  • Provides a competitive edge against others. 
  • Improves conversion rate optimization 
  • Enables fast paced and effective decision making 
  • Plan, budget and innovate strategically. 


Best Practices for Data Analytics 

Whenever creating product with data analytics capabilities, make use of the best practices to follow: 

Identify pain points 

Identifying what problem you are trying to solve is the first best practice of data analytics. It is crucial to know why you need the data analytics software before you hire a team for the same. Doing so, you’ll have a better understanding of what data you need to analyze and the objective of doing so. 

Recruit experts 

The second best data analytics practice is recruiting a team of experts and professionals. It is because the analytics team will collect, clean, and organize data from multiple sources. In addition to this, the design team will assist in creating a dashboard design keeping in mind the best data visualization practices. 

Another great practice for data analytics is product development. The development team is entrusted with the responsibility of creating a data analytics software that works efficiently and also on time. By creating an MVP, they begin with adding features in incremental fashion and that too based on the feedback. 

Introduce data analytics software 

Once your data analytics software is ready, host it on the cloud and make it available for everyone. Run the training sessions for the workforce if your tool is for internal usage. But if you want to turn it into a business model, then running the marketing campaigns would be beneficial as this will create awareness in the market. 

Keep updating 

Compared to the project mindset, follow a product mindset. On the basis of the research and data analytics trends in the market, keep on updating the software and be ahead of your competitors. 

Tools of Data Analytics 

Data Mining 

The foremost tool and technique used for data analytics includes data mining. Data mining refers to the accessing and analyzing the databases for knowledge discovery. It can also analyze large datasets to reveal the hidden patterns such as statistics, trends and other data which is invisible. 

With the help of effective data mining, you can organize the distributed data in one place. In addition to this, it sorts the relevant data and eliminates redundancies across the data sets. 

Business Intelligence 

The business intelligence technology helps in analyzing the historical and current data related to the business information. Besides this, it also runs the predictive analysis which takes the business towards growth. 

Some of the strategies which you can use for implementing business strategies include setting clear objectives, finding the right software and gathering the clean data. 

Data Visualization 

With the help of data visualization, you can create visually delightful and easy to understand charts and reports which in turn leads to fast decision making. Its main purpose is to present complex data in an easy to understand format. 

It minimizes the cognitive load of the users along with bringing a significant improvement in their decision making. Some common kinds of data visualization includes videos, images, funnels, charts, graphs and many more to go. 

The five steps leading to the data visualization includes: 


  • Collecting data 
  • Running data analytics 
  • Visualize 
  • Report generation 
  • Lastly share 

Final Say 

By now, you must have acquired in-depth information about data analytics. There is no doubt in this that it helps you in discovering every small thing regarding your business, customers and assists you in taking informed decisions. It would not be wrong to say that data analytics is highly relevant for your business.