Using Data Analytics for Improving Operations
Analytics is the scientific study of quantitative data or statistics. It is typically used for the analysis, discovery, and communicating of relevant patterns in empirical data. It also involves applying statistical methods towards efficient decision making. Analytics pertains to both the processes and principles used in the science of statistics. As such, it covers a wide variety of topics and includes various application areas, which can be classified into five main subtopics.
Data analysis is a process of gathering, managing, organizing, analyzing and communicating information. Analytics has three main components, including basic research designs, building the data collection, analyzing the data and communicating the results. The primary goal of Analytics is to predict trends by exploring relationships between variables. Some of the common techniques used in analytics are:
Data mining is a technique of finding previously unknown, actionable data that will reveal underexplored opportunities and trends. Data mining is often used in marketing analytics to predict where there is room to improve in current strategies. Another popular form of analytics is machine learning, which refers to the use of mathematical algorithms to make statistical analyses from large amounts of unstructured data sets. This technique is often used in fraud detection.
Model management is an area of expertise for analytics project managers. Model management involves creating, maintaining, and deploying a collection of real or imaginary data models. The models in a model management system are designed to solve problems by minimizing the predicted parameters. Analytics project teams commonly use machine learning and data mining to identify the predictive power of these models. For example, if a team of model management experts identifies two main factors that affect sales growth, the team can build an algorithm that identifies the factors and applies it to the sales data set to identify what they believe to be the key factors that affect growth.
Machine learning and statistical analysis techniques can also be applied to behavioral analytics and web analytics. Behavioral analytics describes the process of collecting and organizing large sets of real-time data to provide insights into customer behavior. Examples include retail web analytics, which provides insight into customer shopping habits and impulse purchases; internet analytics, which provides insights into what people are searching for on the internet; and online marketing analytics, which provides insight into the effectiveness of current internet marketing campaigns.
Analytics can improve operations by discovering efficiencies that existing business practices may not detect or notice. Analytics can help a business prevent common errors that occur when conducting day-to-day business activities. These include common errors such as planning for the future, overloading resources, not scheduling enough staffing, not scheduling enough time, not establishing enough strategic goals, not defining the end result, not measuring enough, and other common mistakes.
Analytics can be applied to a wide variety of business activities including data presentation, customer support, product testing, and product optimization. Data presentation analytics provides insight into how a business presents its product or service to potential customers. Analytics can be applied to many different types of communication including email, text messages, calls, presentations, and many other channels. Data presentation analytics can help a business optimize its visual presentation so that it reaches the maximum number of people with the greatest potential for buying.
Another way to apply analytics to business is by using descriptive analytics. A descriptive analytics approach focuses on understanding the characteristics of the customer or client, while an analytic approach only focuses on understanding what the customer is buying. A descriptive approach may involve observing a salesperson at work, or talking to people who have bought a particular product or service in the past. A descriptive approach has many different applications and techniques including market research, surveys, product lifecycles, product development, optimization for competitive positioning, and many other techniques.