What is Data Analytics?

Big data analytics is the process of analyzing massive amounts of data to find hidden patterns, correlations, and other insights. With today's technology, you can analyze your data and get answers practically instantly, but more traditional business intelligence solutions are slower and less efficient. It is the Skill that is need of time it makes this big data era more sophisticated and easy to handle making things imaginable providing insights and predicting the best possible solution according to the trends and demands.

How it works

Now comes the question that how all this happen, all the analysis what are the steps and procedures so let’s discuss them briefly.

A) Determine the Analysts' Needs: Business or Technology

Data analysts are frequently separated into two types in the workplace: business analysts and technical analysts. Because their capabilities and task content are so dissimilar, their tool requirements are likewise fairly distinct.

· Business analysts are frequently employed in marketing, sales, and other departments. The daily work consists primarily of sorting through company reports, conducting unique analyses for specific businesses, analyzing data, and establishing corporate growth plans.

beenfits

Fig: Benefits of Data Analytics

· Technical analysts are usually found in IT departments or data centers. They are classified as database engineers, ETL engineers, crawler engineers, algorithm engineers, and so on, depending on their work links. These processes are frequently handled by a single technical analyst in small and medium-sized businesses. To complete the data development task in large organizations, a standard data center requires a data warehouse, special analysis, modelling analysis, and other groups.

B) Determine the Tool's Attributes: Analysis Tools or Code Tools

Analysts understand the difference between technology and business, and the data analysis tools that support them do as well.

Analysis Tools:

1. Excel is a must-have skill for junior data analysts. You should be familiar with PivotTables and formulae. It will also help if you know how to use VBA. You must also learn how to use a statistical analysis tool. Beginners should use SPSS.

2. The use of analytics tools is a core capability for senior data analysts. VBA is a must-have tool. You must also be proficient in at least one of the three analysis tools: SPSS, SAS, or R. other tools, such as Matlab, can also be learned, but it is up to you.

3. R and Python are required for data mining engineers because they must write code.

Code Tools:

1. You only need to write SQL queries if you're a junior data analyst. You can also learn how to use Hadoop and Hive.

2. In order to collect and process data with less effort, senior data analysts must understand Python in addition to SQL. Other programming languages, of course, are also options.

3. Hadoop, Shell, Python, Java, C++, and other programming languages are used by data mining engineers. To summarize, data mining engineers must be able to program in at least one programming language.

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Fig: attributes and functions of data analysis tools

Most important tools:

· Business intelligence tools.

· Statistical Analysis Tools.

· General-purpose programming languages.

· SQL consoles (these are a type of user interface that allows you to interact with SQL data.)

· Predictive analytics tools that can be used on their own.

· Tools for data modelling.

· ETL (Extract, Transform, and Load) tools.

· Engines for unified data analytics.

steps

Fig: Steps

Huawei Big Data and Analytics:

Some Salient Features of Huawei Big Data and Analytics are as follows

Huawei's big data solution is based on a tiered architecture, with the FusionInsight platform and its two layers: data platform and data service, at its core. The service layer serves applications while the platform layer hosts cross-field data.
HUAWEI CLOUD FusionInsight includes MapReduce Service (MRS) for cloud-native data lakes, Data Warehouse Service (DWS), Data Lake Governance Center (DGC), and Graph Engine Service, among other cloud services (GES). Real-time analysis, offline analysis, interactive inquiry, real-time retrieval, multimode analysis, data warehouse, data mart, data access and governance, and graph computing are some of the scenarios available.
The Universe Big Data Analytics Platform is a business-driven, industry-leading big data analytics platform. Its goal is to assist carriers in addressing the following concerns in order to spur innovation and create an open ecosystem: cross-domain data integration and governance, as well as customer-centric operations (including user experience and operation efficiency improvement, and real-time marketing).
Offline Data Lake: A warehouse within a data lake with shorter analysis links, resulting in a 10 x increase in analysis efficiency.
Based on enterprise-level big data storage, query, and analysis — quickly builds enormous data processing systems, allowing financial institutions to extract real value from data through mass data mining and real-time Analysis and non-real-time analysis.

Conclusion:

In this time where rapid growth of data is creating huge impacts on business Big Data analytics is the main pillar behind these revolution Changes Making things manageable and insightful as well as creating huge opportunities even for non-technical people to have a secure future.

Huawei Big Data Solutions and Analysis allows businesses to tailor their marketing to a specific portion of their customer base. It also aids them in determining which client segment would respond best to the marketing. Furthermore, it reduces the cost of persuading a customer to make a purchase and increases the overall efficiency of marketing operations.

Sources:

https://www.tiempodev.com/blog/the-business-benefits-of-data-analytics/

https://www.snp.com/blog/8-ways-data-analytics-can-improve-your-business

https://dzone.com/articles/what-data-analysis-tools-should-i-learn-to-start-a