Analysis of data is the revealing results based on evaluating raw data.
The main essence of this analysis is that every person who has his own business has the opportunity to make the right decision by using various types of data analysis.
Each type of data analytics is great in its own way, but each has its native pros and cons.
Let us take a look at every type of data analytics, keeping in mind, that
the correctness of the decisions made should depend mainly on the information that you received. To achieve this, you must use a combination of data analytics types. And if you need a custom-made solution to facilitate you with dynamic data collection and structuring, an advanced big data developer team should be invited.
Four types of data analytics:
We can highlight four types of data analytics:
– Descriptive analytics;
– Diagnostic analytics;
– Predictive analytics;
– Prescriptive analytics.
Typically, the most complicated analysis is more beneficial in doing business.
But the creation of this analytics requires resources, which sometimes fail to demo the useful results because of discrepancies in the raw data. Check this article about data preparation to be ready to implement the analytics properly.
Let us describe each type of analytics in order of complexity and usefulness.
Descriptive analytics explains what happened in the past and based the description only on the facts.
It combines the raw information, facts of the past from multiple sources. However, explaining the reasons for the mistakes is not really.
For this reason, it is necessary to combine this analysis with other types of analytics.
In conclusion, descriptive analytics use many visual tools like charts that are easy to understand by a big business audience.
Diagnostic analytics works primarily with descriptive analytics.
It allows you to identify (diagnose) the problem of something. These analytics work like this.
Firstly, it accumulates data.
Secondly, it begins to compare it with other data. When an error is detected, it indicates the location of the error.
Thanks to this analytics, it becomes possible to identify many events and solve many future errors.
This analytics gives an idea of future changes (forecasts) in something. It works mainly with two types of analytics, which I wrote about above.
Predictive analytics can be automated with ML technologies. However, predictive analytics can only give a rating of future actions. The correctness of this assessment largely depends on the truthfulness of historical information from the past.
This type of analysis uses mathematical statistics to forecast the trends.
This analytics uses patterns of historical performance data to identify many risks and predict opportunities.
Prescriptive analytics – analytical, the main aim is to write an action plan to eliminate a future problem or fully exploit a future analytic trend for any errors.
The main reason for using these analytics is that it does not use historical data. Therefore it is most effective in writing a promising future for the company.
But despite all the advantages of this analytics, it has the main disadvantage – the complexity of implementation and execution.
Prescriptive analytics can continually receive new data for re-assignment, thus automatically improving forecast accuracy and suggesting better solutions. Prescriptive analytics takes hybrid data: a combination of structured (numbers, categories).
Moreover, it takes unstructured data (videos, images, sounds, texts) to predict what lies ahead and prescribe how to take advantage of that predicted future without compromising priorities.
What types of data analytics do companies choose?
Any organization needs data analytics to make correct decisions based on the forecasts and trend evaluations.
Many companies use almost all types of data analysis. But the most useful of them are predictive and prescriptive analytics since they combine additional data analytics that provides the most important historical data to help the main types of analytics work.
Combining data analytics types is called advanced analytics.
It is significant for the development of your business intelligence.
In summary, companies choose how deeply they should dive into data analysis depending on the needs of their organization. Descriptive and diagnostic analytics offer an operational approach, and recommendatory analytics enable users to make decisions. Today, trends indicate that there are more situations in which organizations need deep insights into their data. That is why organizations are interested in the quality of their products that admit them to get to know the business from all sides and make the right decisions. With the assistance of ML experts and programmers, the custom systems are designed to answer the particular wishes of the businesses, including dynamical collection, structuring, and storage of the raw data, analysis, and representation of reports as constantly updating widgets.
What kinds of data analytics does your business need?
Determining the correctness of data types for use in your business is determined four questions:
– What kind of business data analytics is the current state?
– How deep do I need to explore the data?
– Are my answers obvious?
– Does my information have any similarities with the information I need?
Many organizations use answers to the questions to find a good way to solve any problems. This strategy will allow you to most correctly limit the types of analysis data for your business or organization.
Now it’s time to develop a data analytics solution with an optimal technology stack for further implementation and launch.
To solve all these problems, many companies hire specially trained people and spend a lot of money and time on it.
However, regardless of the type of analytics, dynamic data collection and all analysis can be built on machine learning, i.e. simplifying the organization’s business processes.
Mastering these business analytics skills will help you in running it and solving many tasks. Analyzing data is a must first step towards improving your organization of using business.
There is a lot number of tools on the market that promise to do everything you want. We’ve introduced the most commonly used ones to help you narrow your search.
To find a service that suits your needs perfectly, define your analysis expectations, the level of detail you want, and the types of data you will be analyzing. You must use all analytics to solve complex problems