Business Data Analysis | 2 mins read

Business Data Analysis- Introduction for Business Owners

business data analysis introduction for business owners
Chloe Henderson

By Chloe Henderson

Business data analysis uses sophisticated software and machine learning to collect and analyze information throughout a company, enabling access to valuable insights.

Data collection is a complex process that involves gathering information from business systems across various operations. However, this data is of little use without proper evaluation and analytics.

Business data analysis not only sources information but also uses advanced tools, such as mining and forecasting, to detect trends and project future events. This provides companies with a competitive edge, allowing them to prepare operations for upcoming changes in demand.

Aspects of Business Analytics

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Business analytics handles various data science components to generate reports and insights, enabling companies to enhance their decision-making. The primary operations business analytics orchestrate include-

  • Data Consolidation
Before analysis, data needs to be gathered, aggregated, and centralized to eliminate duplicates, inaccuracies, and irrelevant figures. Data can be sourced from transactional records and volunteered information.

Transactional records refer to datasets from a company or third-party, such as bank statements, sales reports, and shipping records. Volunteered data is a combination of documents that are shared by a consumer or authorized party, including personal information.

  • Data Mining
Data mining involves searching through large volumes of data sets to discover trends and patterns. Mining uses several statistical methods to clarify data, including classification, regression, and clustering.

Classification sorts data based on known variables, such as demographics, products, or timelines. Regression is a function that is used to predict metrics based on historical trends. Clustering is used when classifiable variables are unavailable, requiring software to detect trends to group data instead.

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  • Association and Sequence Identification
Many consumers have similar actions, enabling data analytics to predict customer activity using pattern detection, such as association and sequencing.

Association groups different products that are frequently purchased together, such as shampoo and conditioner. Sequencing refers to operations that commonly follow each other, such as researching a product line followed by purchasing the item online.

  • Text Mining
Text mining is used to collect textual data from external sites, such as social media, blogs, review sites, and call centers, to extract information on customer interactions.

This data can then be used to develop new product lines, improve customer service, personalize the customer experience, and evaluate competitors.

  • Forecasting
A forecast analysis predicts future events based on historical patterns. For example, retailers can estimate customer demand for upcoming seasons based on previous years' metrics and emerging trends. This enables management to optimize stock levels and organize staffing to drive sales.

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  • Predictive Analytics
Although similar to forecasting, predictive analytics create scoring models to estimate outcomes. These models actively monitor real-time metrics and adjust themselves accordingly, improving accuracy and responsiveness.

For example, retailers can use predictive models to track transactions and report trends for specific buyer personas according to different variables, such as age, income, or gender. Management can use these insights to improve marketing campaigns, customer experience, and service.

  • Optimization
Analytics allow companies to define best-case scenarios to create a plan of action and optimize operations.

For example, businesses can implement different pricing strategies during slow and peak seasons to drive profits to maintain cash flow year-round. It also ensures that inventory levels are healthy to avoid stockouts and backorders.

  • Data Visualization
Data visualization transforms volumes of metrics into comprehensive models, such as graphs, charts, and reports. This enables companies to better leverage their data, optimizing workflow to achieve goals.

Types of Business Analytics

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Companies can utilize four primary types of business analytics to gain access to real-time and predictive data. Working from the simplest to most complex forms, the types of business analytics include-

1. Descriptive Analytics

Descriptive analytics consolidates a company's existing data to create a general synopsis, allowing management to gain an overview of their past and present performance. This method uses data aggregation and mining to centralize information so that all organization members can access critical metrics.

Descriptive analytics also defines the business's strengths and weaknesses, as well as insight into customer activity. Common descriptive models use statistics and data visualization to generate digestible reports. Marketing teams can use this information to develop and launch targeted promotions.

2. Diagnostic Analytics


Diagnostic analytics prioritizes finding the aspects that caused historical trends, rather than solely focusing on what occurred.

Identifying catalysts enables businesses to manipulate impactful factors to influence trends. This method uses data discovery, mining, and cross-examinations to link variables and uncover why events happened.

Diagnostic analytics also uses probabilities, scenarios, and algorithms for data classification to diagnose causes. However, due to its retrospection, this model is not able to generate detailed actionable insights.

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3. Predictive Analytics

Predictive analytics uses statistical models and machine learning to forecast possible future events. This technique often requires human intervention from data scientists and analysts to develop algorithms and models.

Many businesses use predictive analytics to gather text data from online sites, such as social media, to determine the common customer opinion on their brand. This model is used to discover consumers' reactions to a new product line or service.

4. Prescriptive Analytics

Prescriptive analytics exceeds the predictive technique by providing suggestions on how to react to future events to yield the best outcome. However, this model relies on extensive feedback and alterations to ensure relationships between different actions and outcomes are accurate.

Businesses commonly use prescriptive analytics to create recommendation engines to anticipate customer wants and needs. Through deep learning and complex networks, this method can segment data using set parameters and timelines to determine emerging customer preferences. This enables retailers to improve production, services, and new product lines to drive sales and profits.


Data collection is vital for every business as it holds the knowledge of its strengths and weakness. However, it is up to each company to discover which type of business data analysis best fits their needs.