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Business Analytics: Everything You Need to Know


What is Business Analytics?

Business analytics is the process of collating, sorting, processing, and studying business data, and using statistical models and iterative methodologies to transform data into business insights. The goal of business analytics is to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue.

A subset of business intelligence (BI), business analytics is generally implemented with the goal of identifying actionable data. Business intelligence is typically descriptive, focusing on the strategies and tools utilized to acquire, identify, and categorize raw data and report on past or current events. Business analytics is more prescriptive, devoted to the methodology by which the data can be analyzed, patterns recognized, and models developed to clarify past events, create predictions for future events, and recommend actions to maximize ideal outcomes.

Sophisticated data, quantitative analysis, and mathematical models are all employed by business analysts to engineer solutions for data-driven issues. They can utilize statistics, information systems, computer science, and operations research to expand their understanding of complex data sets, and artificial intelligence, deep learning, and neural networks to micro-segment available data and identify patterns. This information can then be leveraged to accurately predict future events related to consumer action or market trends and to recommend steps that can drive consumers toward a desired goal.

Components of Business Analytics

Mobile dashboards have similar components to business dashboards, but with a few key differences. The components of business dashboards include:

  • Data AggregationBefore data can be analyzed, it must be collected, centralized, and cleaned to avoid duplication, and filtered to remove inaccurate, incomplete, and unusable data. Data can be aggregated from:
    • Transactional records: Records that are part of a large dataset shared by an organization or by an authorized third party (banking records, sales records, and shipping records).
    • Volunteered data: Data supplied via a paper or digital form that is shared by the consumer directly or by an authorized third party (usually personal information).
  • Data MiningIn the search to reveal and identify previously unrecognized trends and patterns, models can be created by mining through vast amounts of data. Data mining employs several statistical techniques to achieve clarification, including:
    • Classification: Used when variables such as demographics are known and can be used to sort and group data
    • Regression: A function used to predict continuous numeric values, based on extrapolating historical patterns
    • Clustering: Used when factors used to classify data are unavailable, meaning patterns must be identified to determine what variables exist
  • Association and Sequence IdentificationIn many cases, consumers perform similar actions at the same time or perform predictable actions sequentially. This data can reveal patterns such as:
    • Association: For example, two different items frequently being purchased in the same transaction, such as multiple books in a series or a toothbrush and toothpaste.
    • Sequencing: For example, a consumer requesting a credit report followed by asking for a loan or booking an airline ticket, followed by booking a hotel room or reserving a car.
  • Text MiningCompanies can also collect textual information from social media sites, blog comments, and call center scripts to extract meaningful relationship indicators. This data can be used to:
    • Develop in-demand new products
    • Improve customer service and experience
    • Review competitor performance
  • ForecastingA forecast of future events or behaviors based on historical data can be created by analyzing processes that occur during a specific period or season. For example:
    • Energy demands for a city with a static population in any given month or quarter
    • Retail sales for holiday merchandise, including biggest sales days for both physical and digital stores
    • Spikes in internet searches related to a specific recurring event, such as the Super Bowl or the Olympics

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