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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.
Mobile dashboards have similar components to business dashboards, but with a few key differences. The components of business dashboards include:
- Data Aggregation
Before 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 Mining
In 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 Identification
In 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 Mining
Companies 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
A 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
- Predictive Analytics
Companies can create, deploy, and manage predictive scoring models, proactively addressing events such as:
- Customer churn with specificity narrowed down to customer age bracket, income level, lifetime of existing account, and availability of promotions
- Equipment failure, especially in anticipated times of heavy use or if subject to extraordinary temperature/humidity-related stressors
- Market trends including those taking place entirely online, as well as patterns which may be seasonal or event-related
Companies can identify best-case scenarios and next best actions by developing and engaging simulation techniques, including:
- Peak sales pricing and using demand spikes to scale production and maintain a steady revenue flow
- Inventory stocking and shipping options that optimize delivery schedules and customer satisfaction without sacrificing warehouse space
- Prime opportunity windows for sales, promotions, new products, and spin-offs to maximize profits and pave the way for future opportunities
- Data Visualization
Information and insights drawn from data can be presented with highly interactive graphics to show:
- Exploratory data analysis
- Modeling output
- Statistical predictions
These data visualization components allow organizations to leverage their data to inform and drive new goals for the business, increase revenues, and improve consumer relations.
There are four types of business analytics, each increasingly complex and closer to achieving real-time and future situation insight application. These analytics types are usually implemented in stages, starting with the simplest, though one type is not more important than another as all are interrelated.
The following BA examples provide insight into the roles of each type in the analytics process. By leveraging these four types of analytics, big data can be dissected, absorbed, and used to create solutions for many of the biggest challenges facing businesses today.
Descriptive analytics describes or summarizes a business’s existing data to get a picture of what has happened in the past or is happening currently. It is the simplest form of analytics and employs data aggregation and mining techniques. This type of business analytics applies descriptive statistics to existing data to make it more accessible to members of an organization, from investors and shareholders to marketing executives and sales managers.
Descriptive analytics can help identify strengths and weaknesses and provide insight into customer behavior. Strategies can then be developed and deployed in the areas of targeted marketing and service improvement, albeit at a more basic level than if more complex diagnostic procedures were used. The most common physical product of descriptive analysis is a report heavy with visual statistical aids.
Diagnostic analytics shifts from the “what” of past and current events to “how” and “why,” focusing on past performance to determine which factors influence trends. This type of business analytics employs techniques such as drill-down, data discovery, data mining, and correlations to uncover the root causes of events.
Diagnostic analytics uses probabilities, likelihoods, and the distribution of outcomes to understand why events may occur and employs techniques including attribute importance, sensitivity analysis, and training algorithms for classification and regression. However, diagnostic analysis has limited ability to provide actionable insights, delivering correlation results as opposed to confirmed causation. The most common physical product of diagnostic analysis is a business dashboard.
Predictive analytics forecasts the possibility of future events using statistical models and machine learning techniques. This type of business analytics builds on descriptive analytics results to devise models that can extrapolate the likelihood of select outcomes. Machine learning experts and trained data scientists are typically employed to run predictive analysis using learning algorithms and statistical models, enabling a higher level of predictive accuracy than is achievable by business intelligence alone.
A common application of predictive analytics is sentiment analysis. Existing text data can be collected from social media to provide a comprehensive picture of opinions held by a user. This data can be analyzed to predict their sentiment towards a new subject (positive, negative, neutral). The most common physical product of predictive analysis is a detailed report used to support complex forecasts in sales and marketing.
Prescriptive analytics goes a step beyond predictive analytics, providing recommendations for next best actions and allowing potential manipulation of events to drive better outcomes. This type of business analytics is capable of not only suggesting all favorable outcomes according to a specified course of action, but recommending specific actions to deliver the most desired result. Prescriptive analytics relies on a strong feedback system and constant iterative analysis and testing to continually learn more about the relationships between different actions and outcomes.
One of the most common uses of prescriptive analytics is the creation of recommendation engines, which strive to match options to a consumer’s real-time needs. The key to effective prescriptive analysis is the emergence of deep learning and complex neural networks, which can micro-segment data across multiple parameters and timelines simultaneously. The most common physical product of prescriptive analysis is a focused recommendation for next best actions, which can be applied to clearly identified business goals.
These four different types of analytics may be implemented sequentially, but there is no mandate. In many scenarios, organizations may jump directly from descriptive to prescriptive analytics thanks to artificial intelligence, which streamlines the process.
When it comes to business analytics, success often depends on whether or not all parties of an organization fully support adoption and execution. Successful BA examples—and subsequent deployment of new predictive-based initiatives—include:
Predictive Maintenance: Shell
Royal Dutch Shell PLC recently implemented predictive maintenance driven by artificial intelligence to cut down on time lost to machine failure. The AI-powered tools predict when maintenance is needed on compressors, valves, and other equipment, can autonomously analyze data to help steer drill bits through shale deposits, and will soon be able to identify and alert station employees of dangerous behavior by customers, reducing risks from the drilling platform to the gas pump.
The systems can anticipate when and where more than 3,000 different oil drilling machine parts might fail, keep Shell informed about the location of parts at their worldwide facilities, and plan when to make purchases of machine parts. These systems also determine where to place inventory items and how long to keep parts before putting them into rotation or replacing/returning them. Shell has since reduced inventory analysis from over 48 hours to less than 45 minutes, saving millions of dollars each year thanks to reduced costs of moving and reallocating inventory.
Predictive Deliveries: Pitt Ohio
Pitt Ohio, a $700 million freight company, was significantly impacted by Amazon’s same-day delivery initiative, which ramped up customer expectations. Customers also became more demanding, requesting up-to-the-minute tracking and estimated times of delivery that were much narrower than formerly acceptable windows. The company turned to data analysis to find a way to improve customer experiences.
A cross-departmental project involving market research, sales operations, and IT was launched internally, leveraging data that was previously unused. The historical data, predictive analytics, and algorithms that calculated freight weight, driving distance, and several other factors in real-time allowed Pitt Ohio to estimate delivery times at a 99 percent accuracy rate. The company estimates that repeat orders increased its revenue by $50,000 per year, and customer churn reduction equaled retained revenues of $60,000 per year.
Predictive Banking: Axis Bank
Axis Bank, the third-largest private sector bank in India, implemented robotics process automation and deep learning to identify customer behavioral patterns and recommend next best actions to prevent customer churn, including streamlining document processing, identifying “events” when customers were more likely to leave, and preemptively offering special promotions targeted to those segmented audiences to prevent churn.
For better customer experience, 125 “customer journeys” were identified, analyzed, and retooled, and time spent verifying customer-provided data across multiple documents in the back office dropped from 15 minutes to 2–3 minutes. Axis is now developing a chatbot to speed customer interactions and reduce wait times for service at busy branches and during peak interface times.