Bussiness Analytics

Business analytics, a data management solution and business intelligence subset, refers to the use of methodologies such as data mining, predictive analytics, and statistical analysis in order to analyze and transform data into useful information, identify and anticipate trends and outcomes, and ultimately make smarter, data-driven business decisions.

1.The main components of a typical business analytics dashboard include

Data Aggregation: prior to analysis, data must first be gathered, organized, and filtered, either through volunteered data or transactional records

Data Mining: data mining for business analytics sorts through large datasets using databases, statistics, and machine learning to identify trends and establish relationships

Association and Sequence Identification: the identification of predictable actions that are performed in association with other actions or sequentially

Text Mining: explores and organizes large, unstructured text datasets for the purpose of qualitative and quantitative analysis

Forecasting: analyzes historical data from a specific period in order to make informed estimates that are predictive in determining future events or behaviors

Predictive Analytics: predictive business analytics uses a variety of statistical techniques to create predictive models, which extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes

Optimization: once trends have been identified and predictions have been made, businesses can engage simulation techniques to test out best-case scenarios

Data Visualization: provides visual representations such as charts and graphs for easy and quick data analysis

2.Types of business analytics

There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.

3.Business analytics tools

Business data analytics has many individual components that work together to provide insights. While business analytics tools handle the elements of crunching data and creating insights through reports and visualization, the process actually starts with the infrastructure for bringing that data in. A standard workflow for the business analytics process is as follows:

Data collection: Wherever data comes from, be it IoT devices, apps, spreadsheets, or social media, all of that data needs to get pooled and centralized for access. Using a cloud database makes the collection process significantly easier.

Data mining: Once data arrives and is stored (usually in a data lake), it must be sorted and processed. Machine learning algorithms can accelerate this by recognizing patterns and repeatable actions, such as establishing metadata for data from specific sources, allowing data scientists to focus more on deriving insights rather than manual logistical tasks.

Descriptive analytics: What is happening and why is it happening? Descriptive data analytics answers these questions to build a greater understanding of the story behind the data.

Predictive analytics: With enough data—and enough processing of descriptive analytics —business analytics tools can start to build predictive models based on trends and historical context. These models can thus be used to inform future decisions regarding business and organizational choices.

Visualization and reporting: Visualization and reporting tools can help break down the numbers and models so that the human eye can easily grasp what is being presented. Not only does this make presentations easier, these types of tools can help anyone from experienced data scientists to business users quickly uncover new insights.