What is Data Analytics? Why is Data Analytics Needed?
What is Data Analytics?
The term data analytics can be defined as the process of analyzing datasets to figure out conclusions about the information that these datasets consist of. Through data analytics techniques, you can take raw data and find patterns to extract valuable insights from it. Various data analytics techniques use specialized software and systems that integrate automation, machine learning algorithms, and other capabilities.
Benefits of Data Analytics Services
Offer a personalized customer experience
Businesses gather customer data from multiple channels. These channels comprise of physical, ecommerce, retail, and social media. As businesses use data analytics solutions to create comprehensive customer profiles from the data, they gain valuable insights into customer behavior to offer them a highly personalized experience.
Streamline Business Operations
Data analytics enables enterprises to hone operational efficiency. As they collect and analyze data on supply chain, they can easily figure out where bottlenecks or production delays crop up and help anticipate where future problems may arise. Through data analytics solutions, retail businesses can also ascertain optimal supply for all the products of an enterprise basis factors, like holidays, secular trends, and seasonality.
Guide Business Decision-Making
Enterprises can use data analytics services in USA to inform business decision-making, reducing financial losses at the same time. For instance, predictive analytics can recommend what might happen in response to business changes, and prescriptive analytics can show how business should react to such changes.
In this post-Covid-19 world, businesses are largely witnessing the issue of data security. Enterprises can use data analytics to determine the reasons behind past data breaches and they can do so by visualizing and processing relevant data. For example, the IT department may use applications related to data analytics to analyze, process, and visualize their audit logs to ascertain the course as well as origins of an attack. The information may help IT explore vulnerabilities and patch those.
Minimize Risk and Tackle Setbacks
Businesses must face different types of risks, such as employee or customer theft, employee safety, legal liability, and uncollected receivables. As you hire a data analytics consulting services company, you can better understand risks and adopt preventive measures. Besides, data analytics services companies, like Compunnel Digital, help your business to limit losses post a setback. Enterprises can also create statistical models to automatically recommend on how to resolve repetitive problems.
The Process of Data Analysis
Data analytics conducted by the best data analytics companies comprises of the following phases being repetitive in nature:
Specification of Data Requirements
The data needed for analysis depends on an experiment or a question. Depending on the needs of those directing the analysis, the data required as inputs to the analysis is detected. Specific variables regarding a population may be defined and obtained. Data may be categorical or numerical.
Data collection refers to the process of collecting information on targeted variables detected as data requirements. This step in the data analytics process guarantees that data collected is accurate such that the related decisions are valid. But, as data is collected from multiple sources ranging from the information in web pages to organizational databases, it may not be structured and contain unnecessary information. The collected data should thus go through the steps of data processing and data cleaning.
The data gathered by data analytics services companies should be processed or organized to analyze it. This comprises of structuring the data as needed by the right analysis tools. Let’s take an example to understand this better. The data might have to be positioned into rows and columns in a table within a statistical application or spreadsheet.
The processed and organized data may look incomplete, contain errors, or duplicates. Data Cleaning can be defined as the process of avoiding and correcting such errors. Depending on the kind of data in question, there are multiple types of Data Cleaning.
Once the data is processed, organized, and cleansed, it is ready for the analysis of data analytics companies. The key here is to understand, interpret, and derive conclusions basis your business requirements. You may even use data visualization to assess the data in graphical format, to gain additional insight on the messages that lie within the data. Besides, statistical data models, such as regression analysis, correlation may be used to detect the relationships among the data variables, thus simplifying analysis and communicating results.
Data analysis results must be reported in a format as needed by the users to inform their decisions and further action. Data analysts may even select data visualization techniques, like charts and tables, to communicate the message clearly and efficiently to the users. Through the analysis tools, these analysts can highlight the requisite information with formatting in charts and tables and color codes.