Components of Business Analytic
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.