In a data-driven world, organizations have access to more information now than they ever have before. Online data is especially helpful at finding out who customers are, where they come from, and what they want.
Internally, software systems collect and use business data, allowing businesses to drill down into the flow of workflow processes to pinpoint inefficiencies and make improvements.
In short, those who want to remain competitive have no choice but to harness the power of big data to make data-driven decisions.
Business analytics is more than simply collecting and gathering information. It is employed as a way to drill down further and gain insight into areas that may not be outwardly noticeable.
Implementing analytics can help small to mid-sized businesses learn about the competition, address consumer needs, and streamline workflows.
Here is an overview of the components and types of business analytics and how they are used.
Business Analytics is Used To:
What is Business Analytics?
Business analytics encompasses the gathering and analyzing of important business data to gain valuable insights that help improve decision-making.
A business analyst collects data from a variety of sources such as customer relationship management systems, website visitor information, or past financial statements. The data is processed by employing various organizing systems and technologies to extract the most essential information that tells a story.
Software systems and other business intelligence solutions make this process possible with a few simple clicks. Most organizations today employ one or more types of analytics to help save money, attract new customers, and decrease inefficiencies. As a result, workflows are streamlined and operational efficiency increases across the business.
Business analytics systems and methodologies include different components that work together to deliver the most benefit. These include-
1. Data Aggregation
Data aggregation is the procedure of collecting data and introducing it in a summarized format. Users collect the information from a variety of sources and place it into one centralized database to begin sorting it. Any unreliable or duplicate information is discarded to prevent inaccurate insights.
Sometimes, organizations collect tractional or third-party data from sales records or banking transactions. This can provide additional information that allows management to gain further insight that helps address business needs.
For example, many businesses collect first-party or third-party (tractional) customer surveys and compile them to look for patterns in feedback such as customer service complaints or defects in a particular product.
2. Data Mining
Data mining and data analysis helps to drill down into data to look for patterns and trends that may not be immediately obvious to the human eye. The analyst needs to mine through large quantities of information by generating mining models and statistics models. Regression models look at historical trends to forecast future statistical values.
Other frequently used techniques include clustering where analysts group data by different classifications such as customer age or transaction date. This allows the analyst to gain a more comprehensive understanding of a data set.
3. Association & Sequence Verification
Association and sequence verification are two relating types of business analytics that look for trends in customer behavior. Association refers to customers who purchase items similar to each other, such as a toothbrush and toothpaste. Sequence verification looks for a potential sequential purchasing pattern based on historical data.
For example, a person buying an airline ticket will probably need a hotel room, an Airbnb, and a cab ride to get there. This form of business analytics helps organizations curate a marketing strategy geared towards the customer's particular needs and historical patterns.
4. Text Mining
Organizations frequently collect consumer comments and interaction data to improve customer service. Data may be collected from website comments, social media comments, or what was said during customer service calls.
Gathering this type of information is also helpful when figuring out which new products to develop and market. It also provides a way to track the competition and pinpoint the problems and successes they are having.
5. Predictive Analytics and Forecasting
People have different likes, dislikes, hobbies, and needs. Collecting historical information to look for purchasing patterns can help to improve a marketing strategy or understand market demands.
Predictive behavior also helps businesses observe and plan for repetitive behavior based on seasons, holidays, or external events. For example, winter coats are frequently purchased in November to prepare for the winter season. A retailer can then prepare and market to customers based on these anticipated needs.
Predictive analytics also allows organizations to use historical data to improve internal processes and equipment. For example, historical data may show that a set of equipment tends to wear down every 5 years so the organization can budget properly to purchase new equipment by that time.
6. Data Visualization
Data visualization is a graphical representation of information. It uses charts, graphs, and other methods to display data understandably.
Management can use visualization to generate reports and present findings. Because the human eye is naturally drawn to pictorial representations, analysts frequently employ data visualization to tell a story.
Types of Business Analytics
There are four types of analytics that work together or alone to deliver data. They function sequentially to further drill down into information and gain more insights. Each type is used following the stage of a workflow and the needs of the data analyst. Within each of these stages, different components of business analytics are employed, depending on the circumstances. (data mining, visualization, etc.) These four types include-
- Descriptive Analysis - This stage answers what happened historically and what is occurring in real-time. It allows management to take a deeper look at the current performance of the company. Data mining and aggregation are the components most frequently employed during this stage of analytics.
- Diagnostic Analysis - This stage answers why something happened, rather than what happened. Probabilities generated through diagnostic analysis tell the analyst what strategies are needed to improve inefficiencies in the future. Data mining is the component most used during the diagnostic stage.
- Predictive Analysis - Once the analyst knows what happened and why he/she can predict what will happen in the future. Text data is frequently employed as part of predictive analysis to help know which products to make or who will buy them.
- Prescriptive Analysis - This stage takes a step further than predictive analytics to generate models that assist in making reliable forecasts and real-time adjustments to ensure future success. It employs neural networks and machine learning to pinpoint the best recommendations.
Different Ways to Use 4 Types of Analytics: There are several different types of ways to use descriptive, diagnostic, predictive, and prescriptive analytics, including-
In conclusion, here are the key takeaways to remember about business analytics-
- Business analytics uses different methodologies to gather, compile, and drill down into data so decision-makers can generate insights that help improve internal and external processes and influence data driven decision making.
- The top methods and components used in analytics are data aggregation, (collection) data mining, (drill-down) text mining, (customer comments) predictive analytics, (what will happen) association and sequence verification, (purchasing patterns), and data visualization (charts and graphs).
- The four types of analytics are descriptive, (what's happening), diagnostic (why it happened), predictive (what will happen), and prescriptive (recommendations for improvement).