With our lives being increasingly dominated by data, it is only expected that methods for the recognition and interpretation of the information represented by these data, be developed. This is where a data analytics model can be of help. Data is an integral part of every system and organization. From our homes to schools, offices and even social clubs, we all collect and use data and rely on its results to make decisions.
A person planning a party, for instance, would need to make a guest list, menu list, entertainment, and refreshment list, these lists would have to be drawn with a financial budget in mind. If the budget is not sufficient to cover the menu, for example, some items may have to be cut. This decision can only be made from various comparisons and drawing conclusions from the data collected.
Data Analytics Model Explained
Data analytics models are formulas or equations that are developed to for the purpose of applying them to data sets in order to identify or establish relationships between variables such as correlation, causation, and to determine expected outcomes. Analytical models are created by algorithms and these algorithms come in different forms. Classification algorithms such as clustering, logistic regression, use a variety of techniques to create formulas that separate data into groups. Online businesses use these algorithms to determine which products to recommend to buyers based on their present or past purchases.
Much of the craft of analytical modeling involves knowing the right data sets and variables to select from, and how to format them into specific data models. Modelers could start with over a hundred variables, then through the course of-of the experimentation and transformation process, narrow them down to less than twenty variables. The chosen variables are the ones that most predict the desired outcome.
Models are usually validated by testing them against random subsets of data which have been set aside in advance for that specific purpose. If the results remain consistent across testing and validation of data sets, it is decided that they have a fairly relevant and accurate model. Once the right analytics model has been designed and deployed, it has to be constantly maintained. Models may become obsolete with time as the market or environment in which they have used changes, therefore the need for regular updates and maintenance is necessary.
What is Data Analytics
Data analytics is the process of sorting through data sets in order to understand the information they represent and drawing conclusions from them with the aid of specialized systems and software. As humans, our judgments are restricted by subjective experiences and incomplete knowledge that impairs our decision-making process. The insights from data analytics are therefore widely relied on in organizations for decision making and can be used to optimize processes which help to increase the overall efficiency of the business or system.
Essentially, any type of information can be subjected to data analytics techniques to get insight that can be used to improve outcomes. For example, banks and credit card companies analyze customer spending and withdrawal patterns to prevent identity theft or fraud. Hospitals use patient data to determine disease prevalence, predict outcomes and map out effective methods of prevention and/or control.
Data analytics involves more than just analyzing data, most of the important work takes place before the analysis begins. The required data has to be collected, integrated and prepared first, then the appropriate analytic model is developed, tested and revised to ensure they produce the needed results.
Types of Data Analytics Models
Data analytics are generally categorized into any of these four categories;
Descriptive analytics as the name implies gives you an overview of what happened over a given period of time. How many customers came into the coffee shop over the last three months? What was the preferred type of coffee customers requested for?
Predictive analytics tends to forecast what would happen in the future. These types of analytics are commonly used in meteorological stations to forecast weather.
Prescriptive analytics suggests the course of action to achieve a target goal.
Diagnostic analytics focuses on the reason behind a particular outcome. It involves a lot of investigation, inputting diverse data and hypothesizing. Did the fan base reduce because of the change in the music genre? Will a new music video improve fan ratings?
Data analytics can also be categorized based on the nature of the variables they represent. Hence, we have quantitative data analysis and qualitative data analysis. It’s easy to determine from their names, the kind of data sets they analyze. Quantitative involves analysis of numerical data with variables that can be measured statistically, for example, the number of bank customers who own and use credit cards.
Qualitative, on the other hand, is more visual. It focuses on non-numerical data like images, videos, human reactions, texts. Methods of data analytics also include exploratory data analysis (EDA) which is aimed at establishing patterns and relationships in data, and confirmatory data analysis (CDA) which determines the accuracy of the hypotheses drawn from a given data set. Once you have determined the type of analytics you want to carry out, the desired data is collected.
The next step is to identify problems that may affect the quality of the analytics applications, and fix those problems; a process known as data cleaning. The purpose of data cleaning is to prevent problems that may arise from the way data are entered and stored. Additional data preparation work is then done to organize the data and ensure it conforms to corporate standards. At this point, the data analytics work begins fully; an analytics model is built using analytics software and programming languages.
Conclusion on Data Analytics Model
Data analytics tools and analytics models are important to any organizational structure. Companies can reduce costs by identifying more efficient ways of doing business, increase revenues, improve operational efficiency. Healthcare organizations can use analytics data to make preventive, predictive and diagnostic decisions to better the health of it’s population. All these lead to an ultimate goal which is performance boosting.