Data Analytics for Business: 7 Comprehensive Aspects

In this competitive world, every Businesses and organization are in search of techniques that can boost their Growth rate. Data Analytics acts as a one-stop solution for the Business to make strategic decisions based on the Insights present. Data Analytics for Business can be defined as a technique to analyze data so that the Business Productivity increases and the Revenue takes a hike. 

This article will give you a comprehensive guide to Data Analytics for Business. Along with exploring the importance and practical applications of Data Analytics for Business, you will also have a look into different types of Data Analytics for Business. You will also read about the top 5 Data Analytics Tools for Business, the advantages of Data Analytics for Business, and the challenges that come along with it. 

Table of Contents

• What is Data Analytics for Business?
• Importance and Strategies of Data Analytics for Business Growth
  • Goal Setting
  • Customer Retention
  • Better Targeting Customers with Business Analytics
  • Decision-Making
• Analyzing Data to Identify Business Opportunities
• Key Types of Data Analytics for Business
  • Descriptive Analytics
  • Diagnostic Analytics
  • Prescriptive Analytics
  • Predictive Analytics
• Data Analytics vs Business Analytics: Key Factors
• Top 5 Data Analytics Tools for Business
  • Hevo Data
  • Tableau Public
  • RapidMiner
  • KNIME
  • Looker
• Advantages of Data Analytics for Business
• Challenges Faced in Implementing Data Analytics for Business
• Benefits of Data Analytics for your Business
• Implementing Data Analytics for Business
• Conclusion

What is Data Analytics for Business?

Data is gathered from a variety of sources and this data might be in a structured, unstructured, or semi-structured format. Data Analytics acts as a key tool for categorizing the data to gain Behavioral Patterns and Insights. To sum up, Data Analytics contributes to enhancing and developing more optimal solutions and Growth for the Business. Let’s have a look at the process involved in Data Analytics for Businesses. Below is the procedure that must be fulfilled for the Data Analytics process:

• Setting up your Goal: You must determine the primary goal for which the Data Analytics is being used.
• Data Collection: Authentic Data needs to be collected for proper Data Analysis.
• Data Cleaning: It is essential to get a proper dataset for analysis without any errors or missing data.
• Data Interpretation: You should review the data using proper procedures so that relevant data is achieved to reach a valid conclusion. 
• Data Manipulation: Manipulate the data using some Statistical tools like Excel, Google Sheets, Pivot Tables, etc. Try deeper analysis of data by finding correlations or outliers between datasets. 
• Data Visualization: A Graphical representation of data provides better insights. Data can be visualized using Pie Charts, Bar Graphs, Histograms, etc.

After following the entire procedure, a Data Analyst approaches Senior Executives, Project Managers, and other team members to decide on strategies for the wellness of the Business. The below image depicts the components of Data Analytics for Businesses.

Importance and Strategies of Data Analytics for Business Growth

A lot of trends and approaches are leading today to help Businesses build their strategies and enhance their growth. Although technology is progressing in the direction of providing ideal solutions, these technologies can only help Businesses if used correctly. Data Analytics for Business has become crucial for increasing the Growth Rate, Market Rate, and Revenue of the Business. 

Unfortunately, Businesses are frustrated with the sorting of unstructured data, hence they are constantly on the lookout for accurate solutions and effective ways that make the process simpler. Well, Businesses also want to reaffirm how important is Data Analytics for Business strategies and growth. Let’s have a look at a few solid reasons why Data Analytics for Business is important:

• Goal Setting
• Customer Retention
• Decision-Making
• Better Targeting Customers with Business Analytics

Goal Setting

The foremost important reason to approach Data Analytics for Businesses is to set realistic goals. This isn’t possible without Data Analytics. Well, you don’t want your business goals to be changed all the time, it’s better to set goals with the help of the right information instead of guessing the goals. Data Analytics helps to gather records, and perform some analysis of historical data to spot trends. These activities will help in defining proper goals.

Customer Retention

The key to success for every business is to retain customers and build a stronger customer base. Without customer satisfaction, a business won’t last in the longer run. Data Analytics for Business plays a vital role in finding key patterns about the customers and recommending the same. This locks the mindset of the customer and boosts customer retention.

Better Targeting Customers with Business Analytics

According to a study by McKinsey & Company, using data to make smarter marketing decisions can boost marketing productivity by 15-20%. Target’s “pregnancy prediction score” is an excellent illustration of this. Target assigns a score based on a customer’s purchases that indicates the likelihood of pregnancy; the company uses purchase data to determine what types of coupons and special discounts to send to a customer’s email address.

Companies may use a lot of data for predictive analytics to assist streamline a customer’s experience with a brand. Finding the correct tools to study your consumers’ buying and Internet surfing patterns, and putting them in place to deliver accurate and actionable knowledge, may stimulate buyer instincts and implant your brand in the brains of your customers.

Decision-Making

Experienced Business leaders can often make informed decisions for the well-being and profit of the Business. Although some due experience might influence decisions, precise data-driven conclusions have a greater impact. Data Analytics acts as a helping hand to make accurate decisions based on dynamic trends. Without Data Analytics, it is impossible to make robust decisions about pricing, planning inventory, and other changing Business factors. 

With the above-mentioned factors, you can understand the importance of Data Analytics for Business growth and strategies. 

Analyzing Data to Identify Business Opportunities

Data Analysis not only improves productivity but also aids in the discovery of new business prospects that could otherwise go unnoticed, such as untapped client categories. As a result, the potential for Growth and Profit becomes limitless, as well as more intelligence-based.

Many professionals can spot short-term patterns, but they are less adept at foreseeing problems that may haunt their company in the future. Computer models based on data analytics assist businesses in identifying trends in client purchases and providing a clear picture of which items should be emphasized or updated. Whether it’s a production issue, a customer service issue, or a weakness among your personnel, analytics may help you identify significant areas of concern that are affecting your company’s capacity to generate a profit.

Human Resources can also benefit from Data Analytics. Many firms are revolutionizing their hiring processes using AI and Machine Learning, while data analytics in people management is informing promotions, performance reviews, employee engagement, and professional development decisions.

Key Types of Data Analytics for Business

To perform effective Data Analytics for Business, it is very important to understand some key terms to gauge high-level Insights. There are four key terms that Data Analysts must understand. These are as follows: 

• Descriptive Analytics
• Diagnostic Analytics
• Prescriptive Analytics
• Predictive Analytics

Descriptive Analytics 

Descriptive Analytics is the process in which the Business applies techniques to interpret data using various tools like Excel, SPSS (Statistical Package for the Social Sciences), Stata, MATLAB (MATrix LABoratory), etc. For instance, Businesses can spot the trending patterns based on Sales data using Descriptive Analytics. 

Diagnostic Analytics

Diagnostic Analytics answers questions raised by the datasets defined in the previous step. For instance, the reason for decreasing Sales, the selling of particular products, etc. These questions that Businesses discovered from Descriptive Analytics are answered by Diagnostic Analytics. 

Prescriptive Analytics

This type of Analytics preferably relies on some advanced Machine Learning Algorithms and Big Analytics to interpret data on a real-time basis. Prescriptive Analytics defines the accuracy of data based on the previous dataset. For instance, a cab booking app connects the nearest driver to customers through Google Maps. This is possible due to Prescriptive Analytics.

Predictive Analytics

Predictive Analytics leverages the previous dataset received from Diagnostics Analytics and Descriptive Analytics. This type of Analytics utilizes the previous datasets to train the models to get better outcomes. With the help of Predictive Analytics, professionals can interpret data and build predictive models. 

Data Analytics vs Business Analytics: Key Factors

Data Analytics and Business Analytics are similar but not the same. The table below depicts the differences: 

Parameter	Data Analytics	Business Analytics
Goal	Data Analytics benefits the organization by identifying the dataset and predicting accurate decisions based on the dataset.	Business Analytics sets goals to support more and more improvement in technology. Its main focus is to find the trending patterns that help to improve overall Business performance.
Data	Data Analytical professionals keep on adding data from sources because of uncured correlations.	Business Analytics sources data after the project goals are finalized.
Approach	Focuses deeply on Prescriptive Analytics and Predictive Analytics. It answers more questions to get better Insights against competitors.	It is more Descriptive and Retrospective which means defining goals as per the project requirements.
Professional Teams	The team includes Data Analysts and Business Managers.	The team includes CIO (Chief Information Officer), CDO (Chief Development Officer), Analytical Manager, and Data Warehouse Engineer.

Top 5 Data Analytics Tools for Business

There are plenty of Data Analytics Tools for Businesses. To select the best tool, one needs a proper comparison of Features, Pricing, and Use Cases. Keeping this in mind, the top 5 ultimate Data Analytics Tools for Business are discussed below:

1. Hevo Data
2. Tableau Public
3. RapidMiner
4. KNIME
5. Looker

1) Hevo Data

Features

Hevo Data is a premium tool for Data Analytics with layered features for Data Management and Manipulation. Following an initial setup, you can configure a completely automated and fully managed system for data replication, transfer, and integration. Hevo Data is a no-code platform, which means no technical knowledge is required for users to be able to draw Data Analytics Insights and use its other features. 

Pricing

The Basic Plan for Hevo Data is available at $249, Starter Plan at $499, and the Business plan at $999 per month. Hevo also offers a 14-day free trial that you can use to try out features. 

Use Case

Hevo Data is best suited for those looking for a complete package for Data Analytics and its peripheral activities. It is also best suited for those looking for real-time updates and integrations with their Business data. It is a one-stop destination for those looking to centralize their Business data requirements. 

2) Tableau Public

Features

Tableau Public is one of the best Data Analytics Tools that contributes to creating Charts, Visuals, Graphics, Applications, and Dashboards that are shareable and provide great Insights. It is packed with a wide range of features that provide integration with several techniques, enabling dashboard previews, customized layout, and much more. Tableau is supported on Windows, MAC, Linux, and many more. Thus, wide compatibility for features can be accessed.

Pricing

Tableau Public and its apps are completely free for use. Tableau Desktop (Personal) can be purchased at $35, totaling annual maintenance of $999. On the other hand, Tableau Desktop (Professional) is available at $70, totaling annual maintenance of $1,999.

Use Case

Tableau Public offers a much higher impact to make proper decisions. It is best suited for professionals to view the dashboard publicly and upload it on Tableau serves Businesses with several products including Tableau Server, Desktop, Online, Prep, Public, and Reader. It is a go-to tool for accessing data anywhere and anytime.

3) RapidMiner

Features

RapidMiner is a software application used for Machine Learning, Deep Learning, Text Mining, and Data Mining. This tool helps to automate Productivity without writing a single line of code. It has multiple features including security, eliminating the lines of codes, centralized workflow management, etc. 

Pricing

RapidMiner Studio has a free version available for open use and RapidMiner Go is available at $10 per month. A variety of custom pricing options are also available for RapidMiner.

Use Case

This is for professionals who have coding experience and want to access Data Analytics. It just requires plotting based on the dataset. RapidMiner provides 5 products which include RapidMiner Server, RapidMiner Turbo Prep, RapidMiner Auto Model, and RapidMiner Radoop. In terms of security and Data Analytics, RapidMiner is a premium option.

4) KNIME

Features

KNIME provides tools that are best recommended because of its open-source nature. With these, you can create professional dashboards, data science applications, and many more. It provides uncountable resources to build Machine Learning Models. KNIME software also provides various Machine Learning algorithms KNIME Analytics platform, KNIME Server, and KNIME Integrations. 

Pricing

KNIME’s small, medium, and large servers can be availed at $14,500, $29,000, and $52,000 annually respectively. 

Use Case

It has a contemporary KNIME GUI interface and in-memory processing which opens the way to lessen the memory. It is, thus, very well suited for small ventures having a limited infrastructure. KNIME doesn’t require any coding skills, it is based on drag and drop features. It provides extended platforms and servers for data uploading and accessing anywhere. So, any Business type can use it. 

5) Looker

Features

Looker is a cloud-based platform that helps Businesses in using Business Intelligence, Analytics, Visualization, and Data Management. It is easy to learn and anyone can use it with zero programming knowledge. It provides various features like mapping features, robust security, drag, and drop features, role assignment, and the provision of data accuracy. It provides customized reports generated from Google Ads, and Facebook Ads. 

Pricing

You can request pricing quotations for Looker services. The pricing starts at $3,000 per month for the entire data platform for up to 10 users. A free trial is also available.

Use Case

To sum up, Looker is best recommended for Data Visualization as it fetches data directly from the resources. To visualize the data, the direct interaction from the source makes it more reliable. If you’re looking to simplify your data machinery greatly, Looker is a great suitable option for you.

Advantages of Data Analytics for Business

Multiple advantages are associated with Data Analytics for Businesses. Out of several advantages, some key benefits are discussed below: 

• High Performance: Business Enterprises always seek to fulfill their tasks with the best processes and on-time delivery. This hasn’t been possible with traditional approaches. It is also a bit tricky to communicate with the customers and fulfill their requirements. With the evolution of Data Analytics, Businesses have an access to better ways to deal with the requirements filling them with better quality and high performance. 
• Consistency: Data Analytics on several datasets helps gain in-depth information and insights to make strategic decisions. It also helps to maintain quality and consistency because of ETL (Extract, Transform, and Load) procedures. It gives quality output in the interactive form of patterns. Typically, Data Analytics should be performed over exact information so that Businesses gain quality output. 
• Ease in Accessing Data: Data in structured form becomes more easily accessible. Users can access this anywhere and anytime. It improves data efficiency and even helps the group of associates/team members to access appropriate data without any hassle. 

Challenges Faced in Implementing Data Analytics for Business

Data Analytics is the best-recommended technique for Businesses to scale up and be able to gain an optimized revenue and increased growth rate. However, there are several layered challenges that you may come across:

• Inaccurate Data: To perform Analytics, data is the base. If the base isn’t strong, then the entire foundation can be in danger. Similarly, if the dataset is inaccurate, this may lead to heavy loss because of inappropriate and irrelevant results.
• Support: Without organizational support, Data Analytics is quite difficult. Risk managers need the support of employees as well as senior executives to handle the data. If employees fail to relay data on time, this can lead to delays in the analysis as well as Data Visualization.
• Confusion: Professionals are still attached to traditional Data Analytical techniques. The mix of both might be a bit confusing to translate into proper decisions. To bring their old setup to par, they can rely on automated techniques or continue to use a dual system which can cause mix-ups and errors.

Benefits of Data Analytics for your Business

Business Decision Making

Data Analytics can help businesses make better decisions and reduce financial losses. Predictive Analytics can predict what will happen as a result of business changes, while Prescriptive Analytics can recommend how the business should respond to these changes.

For example, a company can simulate changes in price or product offerings to see how these affect client demand. A/B testing of product offerings can be used to validate the hypotheses generated by these models. Enterprises can use data analytics tools to determine the success of the modifications and visualize the results to help decision-makers decide whether to roll the changes out across the firm after gathering sales data on the altered items.

Streamline Operations

Data Analytics can help businesses enhance their operational efficiency. Gathering and analyzing data about the supply chain can reveal the source of production delays or bottlenecks, as well as predict future issues. If a demand estimate indicates that a certain vendor will be unable to handle the volume required for the Christmas season, an organization can augment or replace that vendor to avoid production delays.

Furthermore, many organizations, particularly those in the retail industry, have difficulty optimizing their inventory levels. Based on characteristics like seasonality, holidays, and secular trends, data analytics may assist establish the best supply for all of an organization’s products.

Customer Experience

Customers’ data is collected through a variety of channels, including physical retail, E-Commerce, and social media. Businesses can get insights into client behavior by employing Data Analytics to construct full customer profiles from this data, allowing them to give a more personalized experience.

Behavioral Analytics models can be run on client data to improve the customer experience even more. For example, a company could use E-Commerce transaction data to construct a Predictive Model to identify which products to promote at checkout in order to boost sales.

Handle Risks

In business, there are risks everywhere. Customer or employee theft, uncollected receivables, staff safety, and legal responsibility are just a few of them. Data Analytics can assist a company in identifying hazards and taking preventative steps. A retail chain, for example, may use a propensity model, a statistical model that predicts future behaviors or events to figure out which outlets are most vulnerable to theft. The company might then use this information to decide the level of protection required at the stores, as well as whether or not it should divest from any of them.

Enhanced Security

Data Security is a concern for all businesses. By analyzing and visualizing relevant data, organizations can use Data Analytics to diagnose the reasons for previous data breaches. For example, to discover the course and origins of an attack, the IT department can employ Data Analytics software to parse, analyze, and visualize Audit Logs. This data can assist IT in locating and patching issues.

Statistical Models can also be used by IT departments to prevent future assaults. Abnormal access behavior is common in attacks, especially load-based assaults like a distributed denial-of-service (DDoS) attack. These models can be set up to run indefinitely, with monitoring and alerting systems stacked on top to detect and flag irregularities so that security professionals can respond quickly.

Implementing Data Analytics for Business

A few fundamental principles can help a company get the most out of its Data Analysis. For many businesses, determining what data to collect is the first step. Data can be gathered from Social Media, GPS, and other similar technologies, as well as transaction data and a variety of other sources.

It’s also crucial to assess the Data’s accuracy and relevance. Although data can be collected and evaluated, this does not guarantee its accuracy or suitability for a company’s objectives or strategy. In order to check for defects, inconsistencies, or relevance, it’s a good idea to look at how the data was acquired.

Finally, Data must be conceptualized such that actionable steps can be taken. The Data Analytics of a company should offer enormous and obscure data sets in easily understandable informative representations. These representations must then be put into action in order to increase the likelihood of success. A corporation may aim to improve security, increase online traffic, improve customer service, or directly enhance income.

Learning to evaluate and implement data analytics can be time-consuming. However, expertly skilled and well-resourced assistance is available to help with these difficult and vital tasks.

Conclusion

Data Analytics for Business plays a vital role in the success of any organization. It helps in gaining high-level Insights and making strategic decisions. This article gave an in-depth overview of Data Analytics for Business.

Moreover, you are in the position to pick the right Data Analytics tool from the listing discussed above. While Tableau Public can be a great free option for those looking for some robust features within a small Business budget, some extensions of data automation can help with the analytics process. 

Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you with a hassle-free experience.

Try Hevo Data by signing up for the 14-day free trial to experience a fully managed system for data replication, integration, and analytics with advanced features!

You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

Share your experience of learning about Data Analytics for Businesses in the comments section below!