A Complete Guide to Data Analytics for Business – Uses And Examples
Organizations across the world are considering how to use data analytics for business to improve company performance. Data is a valuable instrument that is widely available to enterprises. It can drive decision-making, influence strategy formation, and improve organizational performance when used effectively. Learning how to successfully analyze data can help you form conclusions, forecasts, and actionable insights to support impactful decision-making.
The role of Data Analytics for Business is to understand the state of the business, the behavior of consumers, competitors, and the market; to discover pain points, errors, or ineffective strategies, to define target customers and buyer personas, to test or dismiss theories, and so on. Without a question, the primary goal of data analytics for business is to aid decision-making by providing a solid, dependable, and trustworthy foundation for decision-making.
Rather than being a collection of items kept in a drawer, data must now be integrated into the whole business culture. In other words, businesses must endeavor to create a data-driven culture in which data analytics plays a critical role in all business functions, not just IT.
What is Data Analytics for Business?
The practice of evaluating data to answer questions, detect trends, and extract insights is known as data analytics. When data analytics is utilized in business, it is commonly referred to as business analytics or data analytics for business. Business analytics, a subset of business intelligence, focuses on the big picture of how data can be used to strengthen weak points in existing procedures or to provide value or cost optimization in a specific business process.
To analyze data, you can utilize tools, frameworks, and software such as Microsoft Excel and Power BI, Google Charts, Data Wrapper, Infogram, Tableau, and Zoho Analytics. These can assist you in examining data from many perspectives and creating visuals that illuminate the story you’re attempting to convey.
Algorithms and machine learning are further examples of data analytics tools that can be used to collect, sort, and analyze data at a bigger volume and faster rate than humans. Although writing algorithms is a more sophisticated data analytics ability, you don’t need to be an expert in coding or statistical modeling to reap the benefits of data-driven decision-making.
This could include using reporting or financial analysis tools, data visualization tools, and data mining to better certain business tasks like sales and marketing.
Business analytics, as opposed to data analytics, focuses on finding solutions and tackling current difficulties that are unique to the business and typically stay at the forefront of the data flow.
Data-derived insights are used to assist decision-making processes and drive practical changes throughout the firm in successful business analytics.
Let us understand the concept of data analytics for business with an example.
Analytics has emerged as a driving force in company development and transformation, giving organizations the capabilities required to design and implement new, innovative strategies that improve customer experiences, expand growth opportunities, and generate new income streams.
However, the term analytics is so wide that distinguishing its purpose and uses can be challenging. Data analytics and business analytics are excellent examples. The terms are frequently used interchangeably, however, they are not interchangeable, as illustrated by the instances below.
When a company is planning its sales strategy for an upcoming festive season or holiday, it may utilize business analytics to forecast product demand so that it can optimize stock and accomplish a certain business goal.
That same hypothetical situation, however, could employ data analytics to learn about the number of women between the ages of 18 and 24 who are the most likely to buy those products—and then adapt their marketing campaign appropriately.
How Businesses Use Data Analytics
Planning and strategy:
Before proceeding with data analytics, organizations must ensure that they have a long-term plan in place and defined objectives in mind. They must ask themselves the following questions regarding their data requirements: Specifically, why they want to collect various sorts of data (for example, to learn more about consumer interactions) and what they aim to achieve.
Collecting data:
Once businesses have determined the goal of data analytics, they must select which data sources they will use, which data points they will focus on, and how they will collect that data. Some rely solely on the transaction and social media data, while others rely on high-tech sources such as GPS and RFID chips.
Ensure Data Relevance:
As previously said, raw data tells us very little at first sight. Businesses must guarantee that the quantitative data they collect is useful and that they understand how to interpret it. Simply collecting massive amounts of data does little good—and may even be intentionally counter-productive.
Making Good Use of Data:
Businesses that plan to use data analytics must carefully consider how they will do it and allocate adequate resources to the task. Which metrics do you want to employ? Some businesses hire in-house data analysts, which can provide them with a competitive advantage; but, for smaller enterprises, employing their own data specialists is unlikely to be practical.
Presenting Data:
We have discussed the usefulness of data visualizations in presenting findings and making them more understandable. Tableau and other data visualization tools can assist businesses in visualizing data in the form of charts and graphs. These can then be used as data display tools. For example, in video courses and webinars, as well as the visually appealing infographics that are popular on sites like LinkedIn.
Acting on Fresh Insights: It’s one thing to obtain all these insights through data analytics, but firms must also have a plan in place to put them to use. What discoveries might assist your company to enhance the service it gives to customers and how could you use them to reach out to new customers?
Types of Data Analytics
Descriptive Analytics
Descriptive analytics is the most basic sort of analytics and serves as the foundation for all others. It enables you to extract trends from raw data and describe what happened or is happening concisely.
Descriptive analytics answers the basic question, “What happened?”
Assume you’re studying your company’s data and discover a seasonal increase in sales for one of your products: a video game system. In this case, descriptive analytics can inform you that “every year, this video game system sees an uptick in sales in October, November, and early December.”
Because charts, graphs, and maps can demonstrate trends in data—as well as dips and spikes—data visualization is a perfect fit for expressing descriptive analysis.
Diagnostic Analytics
The following logical question is addressed by diagnostic analytics: “Why did this happen?”
This form of analysis goes a step further by comparing coexisting patterns or movements, identifying correlations between variables, and determining causal linkages when possible.
Continuing with the previous example, you may dive into demographic data on video game console users and discover that they are between the ages of 8 and 18. Customers, on the other hand, are typically between the ages of 35 and 55. According to customer survey data, one of the main reasons people buy the video game system is to give them to their children. The increase in sales throughout the fall and early winter months could be attributed to the holidays, which entail gift-giving.
Diagnostic analytics can help you get to the bottom of a problem in your organization.
Predictive Analytics
Predictive analytics is used to forecast future trends or events and to answer the question, “What might happen in the future?”
You may generate educated forecasts about your company’s future by examining past data in conjunction with industry trends.
For example, knowing that video game console sales have increased every year in October, November, and early December for the past decade provides adequate evidence to anticipate that the same trend will continue next year. This is a plausible prediction given the overall growing trend in the video game business. Making future predictions might assist your firm in developing plans based on anticipated events.
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Prescriptive Analytics
Finally, prescriptive analytics provides a solution to the question, “What should we do next?”
Prescriptive analytics considers all conceivable aspects of a circumstance and recommends actionable takeaways. When making data-driven judgments, this form of analytics can be extremely valuable.
To complete the video game scenario, what should your team do to give the expected seasonality due to winter gift-giving? Maybe you decide to run an A/B test with two ads: one for product end-users (children) and one for consumers (their parents). The results of that test can help determine how to profit from the seasonal rise and its alleged source even further. Alternatively, you may decide to ramp up marketing efforts in September with holiday-themed messages to extend the rise further for the coming months.
While manual prescriptive analysis is possible and practical, machine-learning algorithms are frequently used to help navigate through enormous amounts of data and identify the best next step. Algorithms utilize “if” and “else” statements to parse data as rules. An algorithm advises a specific course of action if a given set of requirements is met. While there is much more to machine-learning algorithms than those statements, they, along with mathematical equations, are essential in algorithm training.
To generate a complete picture of the story data tells and make educated decisions, the four types of data analysis should be used in tandem. Use descriptive analytics to better comprehend your company’s present status. Use diagnostic analytics to figure out how your organization got there. Predictive analytics is important for predicting a situation’s trajectory—will present patterns continue? Finally, prescriptive analytics can assist you in considering all aspects of present and future scenarios and developing proactive solutions.
Depending on the challenge at hand and your objectives, you may choose to employ two or three of these analytics types—or use them all sequentially to acquire the most in-depth understanding of the story data tells.
Improving your data analytics abilities can enable you to capitalize on the insights provided by your data and help you to make accurate decisions for business or for the organization that you are working with and grow your business and career.
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Which Is Better for Your Business: Business Analytics or Data Analytics?
Everywhere, big data is reshaping and propelling decision-making. Data from a variety of sources is assisting companies in expanding their reach, increasing sales, operating more efficiently, and launching new products or services, from huge enterprises to higher education and government agencies.
Usually, there is confusion concerning these two concepts, which appear to be interchangeable. Let us understand each concept’s aims and compare roles and duties to help you decide which path is best for you.
Working with and manipulating data, extracting insights from data, and applying that information to improve business performance are all aspects of business analytics and data analytics. So, what are the key distinctions between these two functions?
Data analytics for business is concerned with the broader business implications of data and the actions that should be taken because of them. Data analytics for business refers to a set of skills, tools, and applications that enable firms to analyze and enhance the effectiveness of fundamental business processes such as marketing, customer service, sales, and information technology.
Business analytics (BA) is the iterative investigation of an organization’s data to reveal knowledge that might help drive innovation and financial performance. Analytics-driven businesses view big data as a valuable corporate asset that feeds company planning and supports the future, and business analytics assists them in extracting the most value from this goldmine of insights.
Because business analytics necessitates large amounts of high-quality data, businesses seeking reliable results must first integrate and reconcile data from disparate systems before deciding which subsets of data to make available to the company.
Data analytics entails hypotheses and enormous information to uncover patterns and trends, draw conclusions regarding hypotheses, and use data-driven insights to support business choices.
Roles and Duties of a Business Analyst and a Data Analyst
Both business analysts and data analysts deal with data. The distinction is in what they do with it. Data is used by business analysts to make strategic business decisions. Data analysts collect data, alter it, extract meaningful information from it, and turn their discoveries into digestible insights.
People in either capacity must have a passion for data, an analytical mind, strong problem-solving abilities, and the ability to see and work toward the broader picture. However, if you’re trying to choose between these two professional options – data analytics or data analytics for business, it’s critical to understand how they differ.
Data is used by business analysts to identify problems and solutions, but they do not do an in-depth technical study on the data. They are interested in the business implications of data and work at a conceptual level, establishing strategy and engaging with stakeholders. Data analysts, on the other hand, devote most of their time to acquiring raw data from diverse sources, cleaning and changing it, and employing a variety of specialized procedures to extract relevant information and draw conclusions.
Typically, business analysts have a substantial domain or industry experience in sectors such as e-commerce, manufacturing, or healthcare. People in this profession are less reliant on technical parts of analysis than data analysts, but they must be familiar with statistical tools, common programming languages, networks, and databases.
Business analysts must be skilled in modeling and requirements gathering, whereas data analysts must be skilled in business intelligence and data mining, as well as familiarity with in-demand technologies like machine learning and AI.
A strong experience in business administration is a valuable tool for business analysts. Many business analysts have credentials in management, business, information technology, computer science, or a similar subject. A math or information technology background, on the other hand, is preferred for data analysts, who must grasp sophisticated statistics, algorithms, and databases.
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Frequently Asked Questions
Q1. Who uses Data Analytics?
Any business professional whose role includes making judgments must have a solid understanding of data analytics. Data access is more common than ever. If you develop strategies and make decisions without considering the data you have access to, you may lose out on significant opportunities or red signals that it communicates.
Data analytics abilities can aid the following professionals:
- Marketers who develop marketing plans based on customer data, industry trends, and performance data from previous campaigns
- Product managers study market, industry, and user data to improve the products of their companies
- Finance professionals who estimate their companies’ financial destinies using prior performance data and industry trends
- Human resources and diversity, equality, and inclusion specialists who use data on employee perspectives, motives, and behaviors to make real changes in their organizations
Q2. What are the responsibilities and roles of a business analyst?
Business analysts or individuals responsible for managing data analytics for business serve as the link between the worlds of information technology and business. They create and convey goals and plans to all levels of the firm, from stakeholders to management to information technology. They approach circumstances and issues as problem solvers by looking at the business to generate answers using data.
There are so many responsibilities of a data analyst and its further applications for data analytics for business that it is hard to list them all. However, the following are some of the most common among businesses:
- Introduce change into an organization, such as a new business model, and assist in its management
- Identifying and establishing business requirements, as well as efficiently communicating with company leaders or stakeholders
- Defining business issues and developing organizational solutions
- Business reality interpretation
- Obtaining a worldwide, comprehensive, and dependable picture of the organization and all its aspects
- Capabilities for critical thinking
- Obtaining insights and important information to improve business activity monitoring
- Increasing inter-departmental collaboration
- Process optimization
- Workflow optimization
- Errors, weaknesses, and potential areas for improvement are identified
- Future scenario prediction (forecasting)
- Increased customer knowledge
- Buyer personas and target customers are defined
- Identification of business possibilities and development of market initiatives
- Increasing client satisfaction
- Existing market optimization
- Business strategy must be reoriented
- Adaptation to a volatile and unpredictable market
- Boosting Return on Investment (ROI)
- Risk mitigation
Q3. What are various data analytics techniques used for business?
The process of collecting and analyzing raw data to form conclusions about it is known as data analytics. Every company gathers vast amounts of data, such as sales numbers, market research, logistics, and transactional data. The true value of data analysis is found in its ability to detect patterns in a dataset that may signal trends, hazards, or opportunities. Data analytics enables firms to make better decisions by modifying their operations based on their learnings. This could include determining which new items to bring to market, devising ways to retain key clients, or assessing the efficacy of new medical treatments.
The majority of regularly used data analysis procedures have been automated to speed up the analytical process. Because powerful analytics tools are widely available, data analysts can go through massive amounts of data in minutes or hours rather than days or weeks using these techniques:
Data mining is the process of searching through enormous data sets to uncover trends, patterns, and relationships.
Predictive analytics collects and analyzes historical data to assist firms in responding properly to future events such as customer behavior and equipment problems.
Machine learning: The use of statistical probability to educate computers to process data more quickly than traditional analytical modeling.
Text mining is the detection of patterns and sentiments in papers, emails, and other text-based content.
As more businesses migrate important business apps to the cloud, they gain the potential to innovate more quickly with big data. Cloud technologies provide a fast-paced, inventive atmosphere in which data analytics teams may store more data and rapidly access and study it, resulting in a shorter time to value new solutions.
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Conclusion
In a world that is more reliant on information and statistics, data analytics assists individuals and companies in ensuring the accuracy of their data. A set of raw numbers can be converted into instructive, educational insights that drive decision-making and deliberate management using a range of tools and methodologies.
Whether it is good or bad news, it is impossible to deny that we rely on data nowadays, especially in the corporate world. Data has become the lifeblood of any organization, and this tendency will only continue soon.
Even though organizations have more data than ever before, it appears that harnessing it and turning it into a value is not as simple as it may appear. Companies will need data analysts to accomplish this, as well as a complete data strategy that spans all areas of an organization.
