Five Types of Data Analytics: Things you should know how it helps your organization
Big Data Analytics is a growing field, and many companies are interested in it. Businesses have understood that they are wasting a golden opportunity by not collecting and analyzing the data they receive from their customers and visitors using Big Data. It is not just that Big Data as a technology is trend, there are various trends in Big Data as well that are doing the rounds and catering the best piece of the cake to the businesses based on various industries.
The growth of big data market is expected to be phenomenal, and according to a report by Frost and Sullivan, the data analytics market is expected to grow at a CAGR of 29% to $40 billion by 2023.
This explosive growth has prompted many Big Data Analytics Firms to come up with great solutions. Having a leading company providing the best Big Data Services to solve your issues and to gain the immense benefits that big data analytics offers can help your business go a long way.
Before you dive into the exciting world of Big Data, it is essential to know some basics.
Types of Big Data Analytics
Descriptive analytics deals with summarizing raw data and converting it into a form that is easily digestible. Also, by using descriptive analytics, one can easily infer in detail about an event that has occurred in the past and derives a pattern out of this data.
One of the most crucial data analytics, descriptive data analytics helps in revealing critical information about a business. It is impossible to create ideal Business Intelligence tools and dashboards without conducting robust descriptive analytics.
Descriptive analytics helps in addressing some fundamental questions of data analytics (4Ws One H). Descriptive analytics contains two subsets, Canned Reports, and Ad-hoc reports. While a Canned story is a report which includes information on a particular subject but on previously designed parameters like monthly reports. While an Ad-hoc report is not pre-determined and is more of an ad-hoc thing.
In an ad-hoc report, you can attain in-depth information about a specific query. For example- an ad-hoc report can help you in identifying the types of people who have liked your page. The hyper-specific nature of an ad-hoc report will help you in gaining previously unseen insights into your business.
In diagnostic analytics, we explore a specific situation in-depth to identify the root cause of a problem or to explore an opportunity. In diagnostic data analytics, we examine a particular data set and try to ascertain a cause-effect relationship.
In diagnostic data analytics techniques like data discovery, data mining, and drill-down are employed.
There are two main categories of diagnostic data analytics.
One is the discovery and alerts category in which the primary purpose of analytics is to notify the concerned people about a potential issue before it arises. For eg, it can alert a purchase manager about the low quantity of raw material beforehand. We can even use diagnostic analytics to discover something particular like who will be the best person for a specific job.
We use Query and drill-downs to know in detail about a particular event. For eg, if you notice that the productivity of a few employees has dipped, then on conducting a query and drill-down analysis, you will identify that they were on vacation.
Similarly, you can identify why sales have decreased or increased over a specific period. The ability of diagnostic analytics to give you insights is limited as it can just provide an understanding of a causal relationship.
The primary purpose of diagnostic analytics is to determine the causes of a particular event by comparing it with past events. One can use diagnostic analytics to identify the outliers, to isolate the patterns, and to uncover the relationships between various activities.
Predictive analytics is the type of data analytics which tries to forecast the future trends based on what is happening in the present, instead of focusing on the past. Predictive analytics also helps in estimating when the event will occur in the future.
Predictive analytics is commonly used in the healthcare industry to assess the probability of a patient contracting a disease. For example- based on lifestyle choices, habits, environment, and genetics, a predictive algorithm can determine whether the patient has a risk of heart failure or not.
Predictive analytics is the outcome of your descriptive and diagnostic analytics, where you turn the insights gained from these two analytics into actionable steps. Predictive analytics helps in describing what will happen if certain conditions are met.
The predictive analytics tools help your business in taking a peep at the future. They help in predicting and planning for the future. You can also enable statistical modeling using predictive analytics, but bear in mind that to harness the full power of predictive analytics, you will require using Artificial Intelligence and Machine Learning.
While conducting predictive analytics, take enough care that the data that you input is accurate as even small inaccuracies can extrapolate and lead to significant mistakes in the output.
In prescriptive analytics, you will go to the next level of data analytics, as you will evaluate a large variety of options and see how you arrived at a particular outcome. A pretty standard example of prescriptive analytics is the GPS app, as it looks at various available route options before zeroing in on the best possible route towards your destination.
The prescriptive analytics helps you in moving up the data analytics maturity model by allowing you to make fast and effective decisions. Using new techniques like Machine Learning and AI prescriptive analytics can help you in trying the various possibilities without actually spending time experimenting with all the variables.
Prescriptive analytics helps you in identifying the right variables quickly, and it even suggests new variables. The primary purpose of prescriptive analytics is to advise you on the next action to take so that you can eliminate a future problem.
Many tools, like Machine learning and sophisticated algorithms, are required to implement prescriptive analytics properly. Hence it would help if a cost-benefit analysis is done before going ahead with the implementation of prescriptive analytics.
Prescriptive analytics can suggest outcomes based on a specific course of action and also suggest various tracks to get your desired outcome. Prescriptive analytics uses an active feedback loop to continually learn and update the relationship between a particular cause and action so that it can predict the future with sufficient accuracy.
Augmented analytics utilizes the power of machine learning and AI to automate various data analysis processes like data preparation, gaining insights from data, and allowing for gaining insights from data. The very basis of augmented data analytics is to provide the power of data analytics into the hands of users who do not have any data science training.
Augmented analytics uses NLP and helps you in getting immediate results for your queries. Augmented analytics gives quick results because it automates the process of data science and machine learning deployment.
Understand that as data analytics is a growing field, data scientists are hard to find. Now add this to the fact that data scientists waste most of their time in trying and trivial tasks like labeling and cleansing data. Thus data scientists are not able to make effective use of their time.
Augmented analytics helps in solving this problem by automating the process. The augmented analytics solution can quickly sift through the data of a company, analyze it after cleansing it, and also convert the result of data analytics into actionable steps. It substantially reduces the role of a data scientist and speeds up the process.
To use the power of augmented analytics, we will need to invest in advanced technologies like machine learning and AI and also take into consideration various aspects of data like data quality, data integration, master data management, data governance, and data cleansing.
The result of analytics
We hope that by now, you have an excellent idea about the various types of data analytics. In the future, more and more businesses will adopt data analytics. Do not worry if all these sounds overwhelming, we as expert Big Data Analytics Company will help you with all the requirements and get to know every aspect that would help in more effective application. We’ll do all the ground work required and create a plan of action to get started with the actual big data analytics services. Knowing some basics about the types of data analytics will help you in selecting the best option according to your requirements.
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