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What is "DATA ANALYTICS"?

What is Data Analytics?

Insights about the market and customers are essential for any business's success. In this Digital era, businesses need data analytics solutions that integrate the best analytics and management capabilities for quick and easy access to data and analyze the information necessary for a business. Before learning “what is data analytics”, one should know why businesses need data analytics. If a business is successful, then there is strong data analytics. Typically, getting the desired information or insights your business needs to compete often takes too long and requires too much effort. The ability to get KPI (Key Performance Indicators) from data can be difficult, as the data is scattered throughout the organization. This happens due to a lack of proper analytic capabilities. In some businesses, the data may be available, but they don’t have fast access to show the insights.  Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance lacking because they cannot uncover hidden patterns and gain other insights. So, every organization needs Data Analytics for its success. Finally, “What is Data Analytics?”

Data Analytics is the process of analyzing raw data into meaningful and actionable insights, i.e., finding trends, and answering questions using a wide range of analysis techniques, including math, statistics, and computer science. Data Analytics can help businesses to improve their performance and help to improve decision-making for business growth. There are four kinds of Data Analytics:

1.     Predictive Analytics

2.     Descriptive Analytics

3.     Prescriptive Analytics

4.     Diagnostic Analytics

Predictive Analytics:

          This is the most common method of analyzing data. Most businesses use these predictive analytics to identify trends and correlations. The main purpose of this analytics is to take raw data, identify trends, and offer a report on what happened.  In short, we can say “What Happened?”. An example of this analytics is, to determine the month-to-month sales, profits, and growth. How many customers are attained per month? Etc. This gives that organization a report on how much revenue was generated over a certain period. By using these analytics organizations can predict what will happen in the future.

Descriptive Analytics:

          This analytics attempts to address the question “What happened?”  This type of analytics analyzes historical data for a better understanding of changes that occurred in business. For example, we can use data like price, and number of customers, to understand the increase or decrease in their revenue. Addressing these questions helps us understand the strengths and weaknesses of that organization like what is working and what isn’t working. If the organization understands these points, then they can address their weaknesses to improve the results.  Descriptive statistics examples in a research study include the mean, median, and mode. Descriptive analytics aims to summarize data to provide key insights.

Prescriptive Analytics:

          This type of analytics is used by businesses which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk. Prescriptive analytics is a statistical method that focuses on finding the ideal way forward or action necessary for a particular scenario, based on data. This analytics is a data and model-based process of understanding what is occurring and then making well-informed decisions with the insights. This analysis uses some tools like machine learning or artificial intelligence to understand the outcomes and graph analysis to interpret the results. How to do this Prescriptive Analysis?

      Pre-process the data: Pre-processing the data means cleaning the data. i.e. the data often involves removing the outliers and dealing with gaps or blanks in the data.

      Use the data to drive the model: Using the data to drive the model often means training and testing from tools like Python and using the model to predict the result.

     Interpret the results: Interpreting the results often uses techniques like graph analysis to understand what the model results say and convey them as a story to others.

Diagnostic Analytics:

          This type of analytics is used to understand the root cause of events, behaviors, and outcomes. This analytics also identifies the outliers in the dataset and makes us understand why something happened. With these takeaways, businesses can make data-driven decisions. Types of diagnostic analytics:

                         

Hypothesis testing: this statistical method gives quality hypotheses using data, it helps data scientists evaluate whether the patterns are statistically relevant or not.

Anomaly detection: Diagnostic Analytics also helps you to identify unusual patterns or if there are any outliers in the given data.

Root cause analysis: This technique helps to understand the root cause and to identify the solution. First, we need to analyze the data to identify the problem and second, learn about the cause of the problem. Finally, we can take action to fix the problem.



Correlation: This method helps to analyze the data by putting various variables into practice by rigorously examining data to establish true cause-and-effect relationships.

Diagnostic regression analysis: Regression analysis identifies the relationship between dependent and independent variables. This helps to understand different factors on outcome.

How does data analytics work?

Data analytics involves a series of steps to get an accurate analysis.

1.     Data Collection:  There are two ways to get the data, the first approach is to identify the data you need for analysis and assemble it for use. secondly, If the data is from different source systems, the data analyst would have to combine the data using data integration routines. But in some cases, the data needed might just be a subset of a dataset. Data analysts include a series of steps to extract the relevant subset and create a new dataset. Doing this allows us to analyze the data without affecting the overall data.


2.     Adjusting data quality: This step involves cleaning of data. Some datasets may have quality problems while collecting data. Data quality problems include inconsistencies errors, and duplicate entries. Data analysts also manipulate and organize the data according to the requirements.


3.     Building an analytical model: Data analysts build models that run accurate analyses. These models are built using analytical software, modeling tools, and programming languages like Python, R, and SQL. After building the model, they will test it using a sample dataset. The results are reviewed, and necessary changes are made to the model.


4.     Presentation: The final step of data analytics is presenting the model results to the organizations or business executives. It is best to use charts and infographics for presentation. It is easy to understand the results.


In conclusion, If an organization wants to build a more insight-driven, there are many data analytics products on the market. Ideally, the solution is to be predictive, intuitive, self-learning, and adaptive. For trustworthy insights, data should be consolidated into a single source which allows the accuracy of the insights. Finally, If the dataset is garbage, it gives garbage insights. To get meaningful insights we need cleaned data with no outliers or blanks in the data. Data analytics is important because it helps businesses increase revenue and improve operational efficiency and customer satisfaction across multiple industries. It also helps to personalize customer experiences, predict future trends, reduce operational costs, improve security, provide risk management, and measure performance. In general, data scientists concentrate efforts on producing broad insights, while data analysts focus on answering specific questions.

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