What Is Business Analytics | 6 mins read

What is Business Analytics? Complete Guide for Business Owners

what is business analytics complete guide for business owners
Chloe Henderson

By Chloe Henderson

Without insightful information, organizations lack the capability to improve their performance and growth. And while every business has the opportunity to collect data from daily procedures, such as transactions and inventory control, some may not have the necessary technology.

Nearly 84% of modern small businesses rely on manual processes, which require manual recordkeeping. This method is not only inefficient, but it is also unable to collect enough data to perform business analytics.

With analytics, companies are able to utilize data to enhance decision-making processes to improve their overall performance.

What is Business Analytics?

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Business analytics is the use of various methodologies, such as data mining, predictive analytics, statistical analysis, and management solutions, to model data into useful information. This enables businesses to define patterns, anticipate trends, and forecast outcomes to make better data-based decisions.

The primary elements of business analytics include-

  • Data Aggregation is the gathering, organization, and filtering of information from volunteered sources and transactional records.
  • Data Mining refers to sorting through large volumes of information using databases, statistics, and software to define relationships and anticipate trends.
  • Association and Sequence Identification defines predictable actions that are a result of another action.
  • Text Mining explores and organizes datasets to prepare the information for quantitative and qualitative analysis.
  • Forecasting is the analysis of historical data from a specific timeframe to make educated estimates of future events, outcomes, or behaviors.
  • Predictive Analytics uses several statistical techniques and extracted data to develop models that generate a predictive score for different scenarios.
  • Optimization occurs after predictions are made when businesses use various simulation methods to test scenarios.
  • Data Visualization uses visual representations, such as graphs, tables, and charts, to explain complex metrics for easy comprehension.

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Many companies use analytics solutions, such as forecasting software, to automate these data procedures and expand their processing capabilities.

Business analytics are typically categorized under descriptive, diagnostic, predictive, or prescriptive analytics. However, depending on their needs, businesses can utilize multiple forms of analytics to improve their internal processes.

The purpose of business analytics isn't just to enhance operational efficiency, but also to prepare systems for future events. Many industries, such as healthcare and retail, use analytics to improve their management skills.

For example, retailers can track customer preferences, retention rates, employee productivity, and marketing performance to drive sales and income. Even restaurants monitor their peak hours to determine when to start preparing their food assembly line to provide fast customer service.

4 Types of Analytics

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Depending on the industry, modern businesses typically use one of the four primary analytics models-

1. Descriptive Analytics

Descriptive analytics mines and aggregates raw data so analysts can gain an accurate view of real-time performance. Although data mining is often considered to be a non-essential part of the data value chain, it enables analytics models to define patterns and trends to predict future outcomes.

2. Diagnostic Analytics

Diagnostic analytics does not focus on the immediate performance of a campaign or process, but rather, looks at previous outcomes to ascertain what happened and why. The model breaks down historical data so it can define relationships between variables.

3. Predictive Analytics

Predictive analytics, including forecasting and statistical techniques, are used to predict several scenarios of what could happen based on the given insights. This method is often used alongside other complex forecast models.

4. Prescriptive Analytics

Prescriptive analytics takes the predictive model a step further by forecasting future events and determining how a business should take action. This method narrows down the general information big data provides to precise solutions.

Companies often utilize descriptive or diagnostic analytics when they are beginning to establish a loyal client base but are not yet actively working on expansion. Larger, more prominent businesses eventually implement predictive and prescriptive models to maintain their success and optimize internal processes.

Data Science vs. Business Intelligence vs. Business Analytics

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There are various data processes that sound similar but have entirely different elements. Businesses should take the time to learn the differences between the following approaches.

Business Analytics vs. Data Analytics

Data analytics is a broad term used to describe the series of scientific processes performed to mold raw data into useful information. Although both business and data analytics strive to improve operational efficiency, business analytics is tailored to company needs.

On the other hand, data analytics has a much broader application, encompassing business intelligence, reporting, and online analytical processing (OLAP). Therefore, any organization or individual can utilize data analytics.

Analysts, engineers, and scientists all work in data analytics to gather, aggregate, and prepare information for further development and testing. This enables them to plug the figures into models and formulate solutions. Therefore, if a business wanted to use data analytics, they would need to tailor its processes for their specific operations.

Business Analytics vs. Data Science

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Data science is a multidisciplinary study that uses various algorithms, methods, and scientific processes to analyze structured and unstructured data. This enables data scientists to determine where the information originated, what it entails, and how it can be used.

Data science collaborates different methods, from data analysis to machine learning, to make better use of the newest information technology. Analysts are responsible for utilizing analytics solutions and data to present comprehensive digital information that enables businesses to make data-based decisions.

However, data science is not used to answer specific questions like business analytics. Data scientists seek to make sense of the collected information, while business analytics aims to address a particular concern.

Business Intelligence vs. Business Analytics

Business intelligence and business analytics are often used synonymously as they have similar purposes. However, business intelligence typically focuses on descriptive analytics, which gathers and stores data to evaluate historical trends to provide a new perspective on real-time metrics.

On the other hand, business analytics uses prescriptive analytics to mine and formulate data with machine learning to calculate the probability of different scenarios. In other words, business intelligence determines what happened and what elements need to change, while analytics answers why it happened and what will happen next.

Business intelligence and analytics tend to overlap as they hold similar structures, practices, and purposes.

How Business Analytics Can Improve Performance

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Business analytics has the power to improve any operation; it all depends on the company's application. Typically, organizations utilize business analytics because it-

  • Empowers Businesses to Make an Impact
Analyzing data enables companies to understand both their overall performance and the market they operate in. This gives businesses the ability to cross-examine their profitability with competitors to improve expansion efforts.

Specifically, business analytics gives companies clarity, competitiveness, and timeliness. Clarity on where a business stands within their industry gives management the knowledge to develop more effective strategies. Competitiveness enables enterprises to utilize their data to stay ahead of the competition. And timeliness allows immediate insight into processes for quick decision-making.

  • Keeps Companies Up-to-Date in Fluctuating Markets
In order for a company to remain competitive, they cannot rely on strategies that were once effective. Instead, they must continuously adapt their models and operations. With business analytics, organizations can stay aware of fluctuating market trends to improve their strategies and scalability.

Analytics also provides businesses with the opportunity to capitalize on emerging trends and avoid industry challenges.

  • Enhances Operational Efficiency
Business analytics provides clear insights into internal processes, enabling management to pinpoint inefficiencies and inaccuracies to implement prompt resolutions.

For example, if the inventory management system consistently shows discrepancies within a product line, management can determine if there is an issue with the barcode numbers, scanners, or employees.

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  • Promotes Business Expansion
Access to analytics gives businesses the option to implement forecasting models, enhancing a company's agility, competitive advantage, and preparation skills. This enables organizations to anticipate and avoid potential risks, such as fluctuating customer demands or market prices.

With improved insight, businesses can make better and quicker decisions that are supported by data.

  • Builds Stronger Customer Relationships
By collecting and analyzing customer data, companies can better understand consumer behaviors, concerns, and preferences. It also allows management to actively monitor the public's perspective and opinion of the brand through social media, surveys, and review websites.

Marketing teams can even track the results of promotions to determine what type of content shoppers prefer, enhancing customer experience and sales.

  • Increases Profits
A McKinsey study shows that businesses that invest in big data analytics experience a 6% increase in profits, which spans to 9% for investments that exceed five years. By quantifying collected data, companies can reduce internal malfunctions, which boosts efficiency and lowers operation costs.

This, along with enhanced marketing strategies, gives companies the ability to invest in lucrative campaigns that drive sales and revenue.

7 Steps for Successful Business Analytics

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Business analytics is an iterative process, which means it is cyclical. As companies gather and evaluate their data, the analysis will have a direct impact on their future needs. Therefore, businesses must carefully adapt and perform the analytics process to ensure reliable insights.

While organizations can tailor business analytics to their operations, the process typically consists of seven steps-

1. Set a Goal

First, business owners must determine their needs, objectives, and goals. This can be anything from improving a lagging system to driving customer reach and sales. Regardless of what the goal is, the business must be able to collect relevant data to enhance their decision-making.

For example, a company looking to improve their order fulfillment process needs to have an appropriate management system in place, such as inventory or warehouse software. These solutions generate detailed reports that enable managers to formulate actionable insights.

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2. Collect Data

The next step is to begin collecting data. Information can be structured, unstructured, qualitative, and quantitative, depending on the company's goals.

Structured data is any information that is already molded into comprehensive metrics, such as demographics, genders, age, and key performance indicators (KPIs). Unstructured data is raw information that needs human intervention to draw conclusions, such as customer feedback and surveys.

Some companies may only need one type of data, while another may require multiple forms. For example, businesses looking to improve customer experience often collect data from multiple sources, from social media review to point-of-sale (POS) data, to gain a well-rounded perspective.

3. Analyze Data

After the data is collected, it needs to be analyzed. More straightforward tasks may only need quick calculations from management teams, while others may require analytical tools, such as software, to compute complex algorithms.

Businesses should consult with developers and data scientists to determine how to analyze the information. Software can be programmed to generate reports on real-time metrics, trends, and relationships. However, they often require additional work to develop actionable insights.

4. Make Predictions

Based on the data analysis, businesses can make predictions. Again, this step can be streamlined with modern solutions, such as forecasting software, which incorporates historical and real-time data to estimate future outcomes.

For example, if data shows that the average customer age for a retailer is 45, marketing teams can develop promotions that appeal to this demographic. This includes which sales channels to place advertisements, what time to run commercials, and even how a promotion is communicated.

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5. Evaluate Each Option

The predictions may present more than one option the business can take. In this case, management must evaluate each route to determine which one is feasible, more cost-efficient, and provides fast results.

6. Implement the Solution

After the option is chosen, it is time to implement the solution. This can mean several things, from making corrections within a standard procedure to implementing an actual technological solution.

7. Monitor the Solution

The process is not finished once the solution is in place. Management needs to actively monitor how the solution is performing and if it is meeting the original objective. If not, the process may need to be repeated or reevaluated.

Business analytics is a powerful tool that sheds light on how companies can improve their overall performance using insightful data. This enables stakeholders to enhance their operations to promote profitability and growth.