Analytics is the structured statistical analysis of real-time data or information. It’s used for the discovery, identification, interpretation and communication of useful trends in unstructured data. It also involves applying statistical methods towards effective decision making.
Analytics has two fundamental concepts: process and data point. Process refers to the steps an analyst takes from collection of data points to recommendation of action. Data points are the actual analytics results. Both concepts are interrelated.
The core elements of Analytics include process and data sets. Process will be the driving force of Analytics. It starts with collecting the needed data set and working it through the analytic processes. Analyzing a data set is done through a mathematical algorithm. This process of Analyzing involves prediction and predictions are made on available information based on historical case studies. After the analysis is complete, recommendations are made based on statistical methods.
Business intelligence (BI) and analytics are closely related and often they use the same techniques. However, business intelligence is more specific in its scope. It covers a wide range of activities that involve analysis, research, design and implementation of strategies, tactics, and operational activities. These strategies and tactics are used by a company in order to achieve certain business results. These strategies and tactics are generally formulated based on statistical analysis and research.
Descriptive modeling is one way of providing quantitative insights into business models. A descriptive model is a graphical or textual representation of the data and the business context, which allow analysts to evaluate models given by other analysts. Valuable insights can be derived from descriptive models and this provides a framework in which to explore various types of business intelligence.
Another application of analytics is to create models from structured and raw data. The structure of the data makes it difficult to create a model from the raw data without the help of some sophisticated software. Therefore, an analyst who has the right analytical skills can extract relevant information from the structured data and make a suitable model from it. In fact, the best applications of analytics to help make better decisions and provide better insights in the data.
There are many ways in which analytics help organizations. One of the major advantages of using analytics to improve data management and decision making is to make better decisions. Through analytics, companies can monitor key performance indicators such as customer satisfaction, employee engagement and the satisfaction of customers. They can also make better decisions on products and services that will help organizations serve their customers better and build a stronger customer base. Data is an essential part of today’s business and therefore the importance of analytics is undeniable.
The benefits of using analytics are increasing with time. However, organizations should be cautious when using analytics for data management. Some of the common benefits of using analytics include the following: data analysis, easier managerial control, faster decision making, leveraging insights from available data, improved customer relationships, improved operational efficiencies and more. Organizations should ensure that they do not misuse the tools and data such as the analytic tools and the data mining techniques. These tools can help organizations achieve results but organizations should ensure that they take only what is necessary and don’t abuse the tools.
Organizations should use analytics in tandem with data analysis. Data analysis is often considered more or less redundant with analytics because most of the challenges organizations face in today’s market are caused by data analysis. Organizations should therefore ensure that they get the best of both the worlds i.e. use analytics to monitor the performance of the market and data analysis to derive actionable insights from the market data. However, it must be understood that data mining is more of a process and not a single technique, therefore the benefits of analytics vs. data analysis are very much dependent on the process and not on its outcome.
There are many areas in which analytics proves valuable. Some of the more prominent uses of analytics are as follows: assessing customer needs and tastes, analyzing marketing performance, improving product and service designs, analyzing market trends and patterns, understanding customer behavior, evaluating financial performance, improving business development and planning and many more. Analytics makes organizations understand the various nuances of business processes and improves business growth by providing information about how and why things are done and why they are not done. All these and more prove the effectiveness of analytics in many fields and this is the reason why organizations use them. Organizations should thus use analytics wisely and with due diligence to reap maximum benefits.
A good example of data analytics is customer service. Analytics provides companies with insights into customer satisfaction and performance. It also provides organizations with the ability to fine tune their customer service strategies and to fine tune their services to ensure that they provide value to their customers. By using data analytics, organizations can improve customer service by: measuring responses to marketing efforts, analyzing the nature and quality of service provided, analyzing the reasons for unsatisfactory customer service and offering retraining or retreats, offering personalized attention, offering explanations and answers, understanding the root causes of unsatisfactory customer service and offering solutions, etc. This will enable organizations to offer better services to customers, which will ultimately increase profits.