Data analytics vs. Data analysis

Data analytics vs. Data analysis

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  Data Analytics vs. Data Analysis Dr.C.V. Suresh Babu
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   Data analyticsis the broad field of using data and tools to make business decisions.  Data analysis, a subset of data analytics, refers to specific actions.
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  Processes in dataanalytics The data analytics practice encompasses many separate processes, which can comprise a data pipeline:  Collecting and ingesting the data  Categorizing the data into structured/unstructured forms, which might also define next actions  Managing the data, usually in databases, data lakes, and/or data warehouses  Storing the data in hot, warm, or cold storage  Performing ETL (extract, transform, load)  Analyzing the data to extract patterns, trends, and insights  Sharing the data to business users or consumers, often in a dashboard or via specific storage
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  Type of dataanalysis  Text analysis. This is also referred to as Data Mining. This method discovers a pattern in large form data sets using databases or other data mining tools.  Statistical analysis. This analysis answers “What happened?” by utilizing past data in dashboard form. Statistic analysis involves the collection, analysis, interpretation, presentation, and modeling of data.  Diagnostic analysis. This analysis answers “Why did it happen?” by seeking the cause from the insights discovered during statistical analysis. This type of analysis is beneficial for identifying behavior patterns of data.  Predictive analysis. This analysis suggests what is likely to happen by utilizing previous data. The predictive analysis makes predictions about future outcomes based on the data.  Prescriptive analysis. This type of analysis combines the insights from text, statistical, diagnostic, and predictive analysis to determine the action(s) to take in order to solve a current problem or influence a decision.
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  Data analysis Dataanalytics Data analysis is a process involving the collection, manipulation, and examination of data for getting a deep insight. Data analytics is taking the analyzed data and working on it in a meaningful and useful way to make well- versed business decisions. Data analysis helps design a strong business plan for businesses, using its historical data that tell about what worked, what did not, and what was expected from a product or service. Data analytics helps businesses in utilizing the potential of the past data and in turn identifying new opportunities that would help them plan future strategies. It helps in business growth by reducing risks, costs, and making the right decisions. In data analysis, experts explore past data, break down the macro elements into the micros with the help of statistical analysis, and draft a conclusion with deeper and significant insights. Data analytics utilizes different variables and creates predictive and productive models to challenge in a competitive marketplace. Tools used for data analysis are Open Refine, Rapid Miner, KNIME, Google Fusion Tables, Node XL, Wolfram Alpha, Tableau Public, etc. Tools used in Data analytics are Python, Tableau Public, SAS, Apache Spark, Excel, etc. Data analytics is more extensive in its scope and encompasses data analysis as a sub-component. The life cycle of data analytics also comprises data analysis as one of the significant steps. Data analysis is actually studying past data to understand ‘what happened?’ Whereas data analytics predicts ‘what will happen next or what is going to be next?’
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   Through dataanalytics and data analysis, both are essential to understand the data as the first one is useful in estimating future demands and the second one is necessary for gaining insight by analyzing the details of the past data.