Types of Data Analytics that Companies Should Know About

 
What Is Data Analytics?

Data analytics is the science of assessing raw data to draw conclusions about the given information. A lot of the techniques and processes of data analytics have been automated into mechanical processes and algorithms with the help of data analytics services that work over raw data for human consumption.

 
Understanding Data Analytics

Data analytics is a broad term that covers different types of data analysis. Any type of information can be put through data analytics techniques to gain insight which can further be used to improvise previous results. Data analytics techniques can reveal trends and metrics that would otherwise have gone unnoticed or lost in the pile of information. This information can then be used to optimize processes to improve the overall efficiency of a business or system.

Data analytics can do much more than point out hold-ups in the production process. You can see gaming companies use data analytics to set reward schedules for players which results in keeping a majority of players active in the game. Even content companies use much of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or click.

Data analytics is integral because it helps businesses enhance their performances. Executing it into the business model means companies can help bring down costs by picking out more efficient ways of doing business and by storing large amounts of data. A company can also apply data analytics to improve the quality of their decision-making process and results and also help analyze customer trends and satisfaction, which can result in new and improved products and services. 

 

Steps in Data Analysis

The process of data analysis involves the following different steps:

1. The first step is to identify the data requirements or how the data has been grouped. Data may be categorised by its usual points of distinction, like age, demographic, income, or gender. Data values can be numerical or be segregated by category.

2. The second step in data analytics is the process of collecting the data. This can be done through a range of sources such as computers, online sources, cameras, environmental sources, or through personnel. With time, the data collected in this step is so ginormous that it is simply called big data and needs its own experts to analyse them. Big data analytics is its own thing now.

3. After the data is collected, it must be classified so it can be further analyzed. This can take place on a spreadsheet or other form of software that can manage and work using statistical data.

4. The data is then cleaned up or filtered before analysis, meaning it is scrubbed and checked to make sure that there is no duplication or error, and that it is not incomplete. This step helps pinpoint and correct any errors before it goes on to a data analyst for the analysis.

 
Types of data analytics

While the essence of every establishment or company using data analytics is analysing the cumulated data to make effective and efficient decisions, they all have different objectives and goals to accomplish. The main types of data analytics are: 

1.    Prescriptive Analytics

Prescriptive analytics is the process of making use of data to ascertain the optimal course of action. By considering all relevant factors, this type of analysis provides recommendations for the successive steps. For this reason, prescriptive analytics is an integral tool when it comes to data-driven decision-making.

Machine-learning algorithms are often used for prescriptive analytics to go through large amounts of data faster and in a more efficient manner than humans. Using statements like “if” and “else”, algorithms scan through data and make recommendations based on a certain combination of requirements. For example, if nearly 50% of customers in a dataset claim that they were “very unsatisfied” with a customer service team, then the algorithm might recommend some additional training to improve the ratings.

It is important to remember that while algorithms can provide you with data-informed recommendations, they can’t replace human judgement and sophistication. Prescriptive analytics is a tool to create informed decisions and strategies and should be treated the same. Your verdict is valuable and necessary to give context and guard rails to algorithmic outputs. At your company, you can use prescriptive analytics to conduct manual analyses, develop proprietary algorithms, or use third-party analytics tools with built-in algorithms.

2.    Cyber Analytics

With a combination of cyber security skills and analytical knowledge, cyber analytics is a new and rising competence within the business and data analytics industry. Cybersecurity threats have increased in volume and sophistication, while the number of internet-connected devices still continues to expand. Cyber analysts answer the demand for data (big data) sifters with an I.T. background. Cyber analysts use sophisticated tools and software to point out the vulnerabilities and close off the attack using a data-driven approach.

3.    Diagnostic Analytics

Diagnostic analytics is a form of advanced analytics that looks at data or content to answer the question of “Why did it happen?” It is commonly identified by techniques like drill-down, data mining, correlations, and data discovery. Usually, there is more than one contributing factor to any trend or event. Diagnostic analytics can reveal the set of causes, making sure that you see the complete picture. You can also see which factors are most influential and focus on those. For diagnostic analytics, you will need to use some of the same techniques as descriptive analytics, but you will dive deeper with correlations and drill down. You might also need to bring in outside datasets for better support of your analysis. 

Remember that since diagnostic analytics is used to identify the origin of business problems and find apt solutions so as to prevent them from happening in the future, it is also called root cause analysis.

4.    Predictive Analytics

As the name suggests, predictive analytics is used to predict the future using modelling, statistics, machine learning, and data mining, to focus on suggested patterns. It is the most commonly used form of analytics and usually focuses on forecasting the outcome of particular scenarios in the context of different potential responses from a company to a situation. There are various predictive analytics models, but usually, they all make use of a scoring system to predict how likely an outcome is to occur.

5.    Descriptive Analytics

Similar to diagnostic analytics, descriptive analytics dives into the past looking for answers. However, the one difference between the two is that diagnostic analytics asks “why something happened?” whereas descriptive analytics answers “what happened?”

Descriptive analytics is the process of using current as well as historical data to identify trends and relationships. It is sometimes known as the simplest form of data analysis as it describes trends and relationships but doesn’t dig deeper. Descriptive analytics is especially fruitful when it comes to communicating change over time and uses trends as the base for further analysis to drive decision-making. Some of the techniques used in descriptive analytics include summary statistics, clustering, and segmentation. The goal is to look into the specifications of what has happened, but this can sometimes be time sensitive as it’s easier to do a descriptive analysis with more recent data.

 

If using data analytics, it is important for you to know the types of data analytics and determine which works best for you. Using the wrong type can result in irrelevant and falsified results which would make all the effort useless. If in need of business analytics assistance, you can start by availing of Data engineering services followed by hiring a data analyst to take the process forward.