The information that is discovered after a session of data analytics can be used in various ways to boost businesses across industries.
Data analytics is a process where large sets of data are examined. The objective is to search for patterns, trends in the market, preferences by customers and previously unknown correlations.
It can be used to optimize marketing, exploit new opportunities for revenue, increase the quality of customer service, improve employee productivity and promote the efficiency of operations. This information can also be used to create competitive advantage over your rivals. Data analytics is normally performed on big data sets.
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These data sets are normally stored by remote DBA experts in specially designed databases. Known as NoSQL databases, they are designed such that they are infinitely scalable. This means that they can store exponentially large amounts of data. The databases are hosted in machines that perform parallel processing. There are many different types of NoSQL database software. Examples are MongoDB, HBase, Raven DB and Apache Jena. There is a direct correlation between big data and these types of software. This is because the data is stored in the databases for processing and access.
What is the importance of big data analytics?
It is cost effective
The database technologies that are required for the purpose of storing big data make it so that the investment is cost effective in the long-run. Technologies such as Hadoop make it affordable to store thousands of terabytes of data. The revenue that a business gets by leveraging big data is much more than the cost of investing in NoSQL database software.
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It promotes quicker decision-making in the business
There is a technology that is known as in-memory analytics. It increases the speed of retrieving data from NoSQL database software. By doing this, the information stored in the databases can be presented to decision-makers quicker and result in a faster decision-making process.
The innovation of new products and services
The information that is collected from the big data sets can be used to innovate new products or services. Most of the data is usually about customers and their experiences. As such, with a good understanding of what they want, a business can respond to this need by providing a product that fits the requirement. This can lead to increased revenues and better profit margins.
More about In-memory analytics
In-memory data analytics is a process where high-impact data mining is conducted. Special processes and tools are utilized to retrieve relevant data. High computational power is utilized, resulting in fast retrieval. Here are the characteristics of in-memory analytics.
It produces deeper insights into data
In-memory data analytics leverages machine learning to find and retrieve information. Special sectors of memory are set apart for every data retrieval session. As a result, the process retrieves results at lightning-fast speeds. By providing the data analysts with this information at high speed, business insights can be identified and applied in real-world situations much faster.
It can handle big workloads of data
In-memory analytics employs the power of special processors. These are able to complete tasks much faster than standard processors. These processors have very high response time. As such, they can handle heavy workloads, multiple user requests and delivery of iterative results. Business demands are always evolving. In-memory analytics is able to provide relevant, critical information at every stage of the evolution.
Capability to perform deeper analysis
In-memory analytics is structured such that it can handle data sets of various sizes. Each set normally has its own unique level of complexity. Thus, the sessions of data mining in this method are designed such that the information is retrieved at high speed. Moreover, the data sets are analyzed in such a way that there is no loss of integrity.
A short lifecycle
This form of data analysis does not have data preparation latency. It also lacks processing latency. As a result, you can retrieve data and create ideal models as fast as possible. This increases the productivity of your data mining effort. In addition to that, you can utilize a host of USE-cases with in-memory data analysis. Eventually, this gives you great results that you can apply in your business.
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Where is big data analytics applied?
Big data analytics is used in various fields of industry. They include:
Hospitality
One of the main objectives in the hospitality industry is to keep customers pleased. However, it is difficult to measure when they are glad and when they are not. However, big data analytics can help to determine this. By going through the data that customers produce while procuring your services, you can tell if they were satisfied or not. This keeps you informed and always evolving to provide better services.
Healthcare industry
There is a lot of information about the patients in the healthcare industry. Examples of this information include records, insurance data, and health planning and nutritional information. Big data is applied in this industry so as to perform quicker diagnoses. It is also applied so as to collect health data about specific demographic groups. In the healthcare industry, information often means life or death. Therefore, the latest technologies such as big data are always applied to boost the chances of patients recovering successfully.
Conclusion
Big data is here to stay. Its effects are felt in almost all sectors of industry. It has significant advantages for the institutions that adopt it. Moreover, it can give your business critical competitive advantage. There are many ways to collect big data and one of them is in-memory analytics. This is a method that is utilized often so as to conduct high-performance data mining. Whenever data is urgently needed, the resources and methods necessary for in-memory data analytics are collected and implemented.
Sujain Thomas is a data IT professional who works closely with Remote DBA experts to provide her clients with fantastic services to their data problems.
