6 Benefits of Using Big Data for Businesses: The Ultimate Guide

The digital universe currently contains over 44 zettabytes of data and approximately 2.5 quintillion bytes of data are created every day.

The primary advantages of big data are that it provides valuable insights that companies can use to improve their business operations. It is therefore not surprising that the adoption of big data by organizations has increased in recent years.

However, big data benefits can be tangible only if you have appropriate data filtration and data analytics methods in place.

This article will answer the question, “Why is big data important?” It will also explain the pros and cons of big data and review the benefits of data analytics.

What Is Big Data?

Big data is large-volume data arrays, which can be structured or unstructured. Special automated tools are used to process this data. As a result, the businesses access valuable statistics and analysis and can use them to make forecasts and informed decision-making.

The term “big data” was proposed by Nature editor Clifford Lynch in a special issue in 2008. Lynch discussed the explosive growth of information volumes worldwide. He defined bigdata as arrays of heterogeneous data exceeding 150 GB per day, although a single criterion for classification is still not universally established.

How Big Data Is Used By Businesses?

In recent years, many successful organizations have found that the big data and business bond is critical to their success. Data-driven strategies are being used by both new entrants and established companies in a variety of industries to increase competitiveness, market penetration, and product innovation. Big data has found applications in almost every field, from information technology to healthcare to fintech development.

Now, how is big data used in business? Data pioneers in healthcare are studying therapeutic efficacy. Companies have worked hard to find possible risks and benefits that were not obvious at the initial stage of clinical trials. Through the use of big data, it is possible to improve the analysis of trials and predict their results. A few early adopters of the concept have already incorporated sensor data from a wide range of things, from industrial equipment to children’s toys. This is useful information for firms interested in studying product usage in the field. Such data makes it easier to create new services and plan future product ideas.

What is big data in business? Experts believe that big data will open up various new business opportunities. As a result, there may be, for example, business models that analyze and collect industrial data. Almost all of these companies will be at the center of huge data flows relating to anything from services and products to supply chains and customer preferences. Every organization, regardless of industry, must immediately begin aggressively building its big data capabilities.

In addition to the importance of big data in business, the high frequency and real-time nature of the data are also crucial factors. Indicators such as customer loyalty scores, for example, used to be calculated retrospectively. With the introduction of big data, such processes have become more common. All this significantly increases the accuracy of forecasting. Another competitive advantage of high frequency for business is the ability to test theories in real time.

A company can benefit from big data in various areas other than those listed above. Big data is now being used by both the government and the private sector, as well as by enterprises and startups. Below are the most important benefits of big data and machine learning algorithms for companies.

What Is Big Data Analytics, And Why Is It Important?

Big data itself is of no value to businesses. In fact, it is simply a mass of information without insights and conclusions.

Data analytics is what businesses should look for in order to extract useful information from data. Appropriate analytics examines large and varied data sets. It uncovers hidden patterns, unknown correlations, market trends, and customer preferences.

The main benefits of big data analytics are as follows:

Comparative, not absolute values. Instead of just receiving numbers without context, you get indicators that show trends compared to previous periods. This allows you to draw conclusions and understand which business changes should be implemented.

Predicted values. One of the biggest advantages of big data analytics is the provisioning of the most accurate forecast for the values you are interested in. This helps you plan business steps in the short and long term.

Explained patterns and trends. Intelligent algorithms in big data analytics can help you understand why and how often your customers or competitors make specific actions. Based on this data, you can adjust marketing, sales, and other business strategies.

Big Data Pros And Cons

Businesses should be aware of the advantages and limitations of big data usage. I list them below.

Pros of big data

Expanded business intelligence

Business intelligence (BI) is a proven way of analyzing data that mostly relies on computerized systems. However, big data has expanded the capabilities of business intelligence. A big amount of data can provide analysts with both historical background and a glimpse into the future.

Companies can sift through massive amounts of data using big data, including discoveries made using information from other sources. Big data can help businesses of all sizes because it allows them not only to efficiently collect and store vast amounts of data but also to analyze it to gain insights into their own operations. When data scientists mine and analyze big data, they can better understand their operations as well as the interests and behaviors of their customers. 

Lastly, companies can do a better job of meeting customer needs and saving money by using big data to improve business intelligence operations.

Enhanced user targeting

As a result of the availability of big data examples, changes in consumer targeting have become revolutionary. Advertisers can look at a user’s digital footprint and change their messages based on what they find.

Most of what we do in today’s digital world (Google searches, tweets, Facebook likes, and comments) generates data that can be used to customize the ads we are exposed to. This means that firms can better tailor their data analysis and marketing to specific consumers.

But the better targeting of users that big data makes possible has benefits beyond business.

Companies, for example, can use geotargeting in social media to determine where a consumer has recently been. Businesses can use your location data to personalize ads if it turns out that you frequent car dealerships, for example, by providing discounts on a certain type of vehicle, car insurance, or in the dealerships themselves.

Improved customer service

Chatbots, artificial intelligence systems that we are used to interacting with in virtual conversations, such as a chatbot when visiting a store website, are one of big data examples in business to respond quickly to customer issues.

To gain insights into a customer’s current and previous contacts with a firm, modern chatbots and customer support teams can benefit from a combination of AI and big data. Using information about what people do online, businesses can guess what their customers want and need.

A corporation can use big data analytics to view a customer’s account in real time and determine if there are any issues that the customer needs help with. As a result, customer service staff can better assist consumers and respond to their queries. With the help of predictive analytics, businesses can also reach out to customers who are likely to have problems soon. Use our executive guide to data analysis for further ideas.

Increased efficiency and reduced costs

In addition, big data is used to improve the operational efficiency of offices. They allow businesses to learn from the data they collect and store. For example, in the business processes, big data allows you to study production, customer feedback, and product returns to assess product quality and overall profitability.

Companies that use predictive analytics to reduce unexpected downtime by anticipating customer requests can increase production efficiency. So, data machine use lowers the overall cost of operations for businesses of all sizes in a number of ways, such as by cutting down on indirect costs, keeping an eye out for cyberattacks that could cost a lot, and even making it easier to keep employees when hiring.

Influenced customer behavior

Improved user targeting can be used not only for better advertising but also to advise or support customers throughout the buying process. Using big data analytics tools, the behavior of customers when they get to the website can be analyzed in the smallest detail. Keeping track of keystrokes, mouse movements, and clicks can help predict what a user will do next.

Companies can increase their chances of selling to or converting customers by analyzing their patterns and responding to them accordingly. Customers are more likely to buy if this data is used to predict by the AI systems (for instance) what they will do in the future.

Lower operational risk

Potential losses caused by dependence on people or mistakes made by them are called “operational risk.” This area includes fraud, computer hacking, disaster response, and disrespect for company rules. The use of big data gives competitive advantages. Big data can help cybersecurity experts investigate, monitor, and detect network breaches. As a result, the time required to detect and solve the problem is reduced.

Big data uses two fraud detection technologies to protect against threats: statistical methodologies and artificial intelligence. Big data has made a big difference in the security of businesses in many fields, whether by using open source algorithms to find outliers or machine learning to automatically spot signs of fraud.

Сons of big data

Data privacy concerns

This is probably one of the biggest negatives of big data usage. By collecting big data, you may gain access to such user details as

• demographic information
• contact information
• financial information
• health data
• online behavior
• location data
• employment information

You should use this information in accordance with the General Data Protection Regulation (GDPR) and other data privacy regulations. It is crucial that no private data is accessed by unauthorized persons.

High costs of implementation

Big data usage comes with high costs. The costs mainly result from setting up infrastructure for big data collection and processing. In particular, you need to invest in specific software and hardware. Additionally, you need to hire a skilled and experienced team of big data scientists and big data analysts.

Data quality issues

Most of the data you collect will not be of much value to your business. To extract valuable insights, you need to pay special attention to data filtering and categorization. To achieve this, you need to use advanced data processing and data analytics tools. At this stage, you will need the knowledge of big data experts who can advise you on suitable big data tools and configure them so that they have maximum effect on your business.

Complexity and skill gaps

Another downside of big data is that you need narrow-focused and highly skilled experts to work with it. It may be challenging to find staff with the necessary technical skills. To speed up the hiring process, you can create a request for proposal (RFP) and send it to multiple companies providing IT outsourcing services and custom software development. After receiving proposals for them, you can choose the company that best fits your project needs.

Scalability challenges

As the volume of big data grows, maintaining and scaling the infrastructure to handle it becomes increasingly difficult. If you don’t take timely measures, you may face delays in data processing. This may ultimately negatively impact the timeliness and effectiveness of insights.

Steps to Start Using Big Data In Business

If you decided to implement a big data solution but don’t know where to start, consider taking the steps described below:

Find experienced big data team

Assemble a team of data science, analytics, and strategy experts to help develop a flawless big data strategy that generates profitable benefits for the organization. Members of this team should know how to use modern analytical methods, be able to work with large data sets, and be experienced consultants who can see the big picture.

Identify end goals

If the right goals are not defined, it’s now clear how big data helps business. The information and analysis should help the organization reach its stated goals, such as making more money, getting the word out about its brand, and getting a bigger share of the market.

Capture the right data

After defining the fundamental business goals, the next step is to understand the data in its entirety. Proper identification, collection, and monitoring of data will be the foundation of the big data process. The core question is: How do companies use big data? Using the wrong data sets can have devastating consequences and lead a company astray.

Apply proper analytic methods

A skilled data analyst can extract information from large amounts of data quickly and accurately. Easy-to-understand visual data summaries help support teams learn what they need to know and make quick business decisions.

Use advanced data analytics tools

Utilize specialized tools and big data platforms to process and analyze vast amounts of data efficiently. These tools can help uncover hidden patterns and insights, allowing for more informed decision-making and strategy development.

Top BI Tools For Managing Big Data

Business Intelligence (BI) tools play crucial role in efficient big data management. There are many such tools on the market. Below, I list the tools that we often use in our projects at DashDevs:

Amazon QuickSight

An advanced BI tool for big data management that is often chosen by AWS users.

Key features:

• Interactive dashboards
• SPICE (Super-fast, Parallel, In-memory Calculation Engine)
• Machine learning insights
• Connection with a wide variety of data sources
• Integration with AWS Identity and Access Management (IAM)
• Natural language query (NLQ)

Pros:

• Cost-effective and scalable:
• Integration with AWS ecosystem
• User-friendly and intuitive

Cons:

• Limited customization
• Occasional performance issues with large datasets
• Insufficient documentation

Best for AWS users

Cloudera Data Platform

An integrated data management platform that perfectly copes with big data across hybrid and multi-cloud environments.

Key features:

• A single platform to manage, secure, and govern data
• Comprehensive data security and governance policies
• AI/ML integration
• Real-time data processing
• Multi-function analytics
• Open-source support

Pros:

• Unified data management
• Scalability to handle large volumes of data
• Robust security and governance features

Cons:

• Complex configuration
• High licensing costs
• Challenges of integrating with existing infrastructure

Best for: large enterprises with complex data sets

Microsoft Power BI

A business analytics tool that allows gathering valuable insights from various data sources in one place.

Key features:

• Easy connectivity to many data sources
• Seamless data modeling and transforming
• Predictive analytics, ML, and NLP
• Wide collaboration options
• Mobile accessibility

Pros:

• Intuitive interface
• Integration with Microsoft tools
• Many options for visualization

Cons:

• Steep learning curve for advanced features
• Limitations in a free version
• Dependency on Internet connection

Best for: Microsoft users

Qlik Sense

A powerful business intelligence platform that provides users with a variety of options to analyze data for insights.

Key features:

• Ability to analyze data without predefined queries
• Drag-and-drop interface
• Responsive design
• Self-service analytics
• Integration with advanced analytics tools
• Ability to share insights within the Qlik Sense ecosystem

Pros:

1. Associative data model
2. User-friendly interface
3. Scalability

Cons:

1. Relatively high cost
2. Learning curve to master full capabilities
3. Limited customization opportunities

Best for: organizations that require self-service analytics 

Tableau

A powerful data visualization and analytics platform that helps users visually explore and understand their data.

Key features:

• Support of a wide range of data sources, including databases, spreadsheets, cloud services
• Intuitive drag-and-drop interface
• Advanced forecasting and trend analysis
• Sharing and collaborating tools
• Accessibility on mobile devices
• Ability to handle complex analyses

Pros:

• Powerful data integration
• Strong customer support
• Extensive user community

Cons

• Quite expensive, especially for small businesses
• Limited customization for visuals
• Occasional performance issues with large datasets

Best for: enterprise teams

Conclusion

Big data is not only used by huge organizations, but even the smallest firms can benefit from it. First, examine the existing material, then look for additional sources of information to collect and evaluate. Then think about using the information to benefit your business executives and customers.

Feel free to get in touch with us to find out how you can manage and analyze big data in your product.