Why Data Analytics is Important for IT Industry?
Role of Data
Data has become a primary source for everything in the Digital Era, commencing from requirements gathering for a business or a product to the Door-Step Customer Services.
In order for businesses to capitalize on data’s potential, organizations must fundamentally change how they view and use it. Businesses are investing billions in unlocking the secrets of data and its enormous disruptive potential. Data is at the heart of new business models, technologies, and an ecosystem of companies providing almost anything as a service.
It is one of the most important assets for any organization. For this reason, many companies have a Chief Data Officer (CDO) to oversee and manage their data. These companies understand data as a valuable asset because they realize how it can help them make strategic decisions.
Table of Contents:
- Understanding the need for Data Analytics
- Role of Data Analytics in Engineering
- Modern Decision-Making using Data Analytics Mechanism
- Data Warehousing
- Data Analysis
- Data Engineering
- Business Analysis
- Conclusion
Understanding the need of Data Analytics
Without getting associated with the Data and making an Analysis with it, the Idea or a Product can get no ware. That is when the role of Data Analytics, Data Engineering and Data Science comes into the picture.
The rise of Social Media connectivity, the boom of Content Creation and Data sharing has drastically enhanced the importance of handling Big Data through various new technologies and intelligence tools.
Data analytics is the process of taking raw data and analysing it for decision making. It can be broken down into many cycles and designed as Datasets so that they can be used for many Use Cases.
This article specifically concentrates on why every Engineer has to know about the importance and application of Data Analytics to make effective decision making and come up with Innovative ideas while building the products and delivering the services.
Role of Data Analytics in Engineering
Usually, the Engineers, be it a
- Software Engineer
- Mechanical Engineer
- Chemical Engineer
- Civil Engineer
- Chemical Engineers etc. are meant to work on creating a sustainable product that is useful for the current market needs. No Engineer or a Business would want to create an outdated system when the world is changing at an inconceivably fast pace.
Hence, before approaching a problem statement or a business idea, researching the current market needs is an ultimate need.
Certain Businesses conduct end-user feedback on their Business Idea, which might suit for few sets of businesses but of course, that cannot be the feasible idea for the rapidly changing customer needs.
There should be a flow of Data research and analytics to be performed before arriving at decision-making.
This portrays how important is to take up the Data Analytics training for Engineers and Businesses.
Modern Decision-Making using Data Analytics Mechanism
The process of data analytics in engineering is made up of four different steps.
The first step requires the decision to determine what data requirements are needed or how the data is assembled.
The next step involves gathering the data, which should be coordinated so it may be dissected properly.
The third step in the process is cleaning up or organizing the gathered information to make it easier for Decision-Making
And finally, it is up to the businesses to arrive at their respective business models and architectures after properly analyzing the data.
- Data Warehousing
In today’s business environment, companies have to deal with a lot of data. Data warehousing is a process of storing and organizing this data so it can be analysed. A data warehouse is a large database that stores all the information needed by various departments within an organization.
The concept of a data warehouse is a blend of technologies and components which aids the strategic use of data. It is electronic storage of a large amount of information by a business, which is designed for query and analysis instead of transaction processing. A process that transforms data into information and makes it available to users in a timely manner to make a difference.
A data warehouse is a structured repository for business-critical information. It is used to store and manage data from varied sources to provide meaningful insights and reports. It is the core of the BI system which is built for data analysis and reporting.
2. Data Analysis :
Data analytics helps individuals and organizations make sense of data. Data analysts typically analyse raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.
There are four types of data analysis: Descriptive, Diagnostic, Prescriptive and Predictive. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, if a company wants to know why something happened, they would use diagnostic analytics to figure out why it happened.
This is generally done using the Analytics tools such as Tableau, Power BI, Spark etc.
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3. Data Engineering
Data Engineers apply the required machine learning algorithms to the collected data sets. This helps us to arrive at a meaningful relationship between the data on how to
- Correlate and distinguish the data
- What creates impact and what cannot?
- What changes can be done to the existing business model?
- What can be the new requirements? Etc.
These analytics provide efficient understanding to give keen inputs while driving through the solution regarding a problem statement.
4. Business Analysis :
Business Analysts work with business stakeholders to capture requirements for Dashboards and reports. The purpose of these tools is to help businesses make informed decisions.
Henceforth, the approach will be planned on how to design a product or a service that completely satisfies the End users.
Conclusion:
This article gave a comprehensive overview of the Data Analytics in the Engineering stream and also how that can be leveraged within an organization. It also highlighted the brief history of how we got here and what the roles look like in a Data team.
It also gave a structured analysis on the Importance of Data Analytics training for Engineers and Businesses.
