Stages of Business Data Analytics
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1. The first step for businesses in data analytics is to identify clear objectives in order to effectively plan the data science process. Performance indicators are also needed to measure progress and identify issues early. 2. Data gathering must be done with full clarity on the objective and relevance of the data collected. Bad or irrelevant data can negatively impact decision making. 3. The properties of big data - volume, variety, and velocity - must be understood. Volume refers to the amount of data, variety to the different types of data, and velocity to the speed at which the data processes. Proper data preparation, including cleaning and organizing, is critical to derive value from the data.
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Stages of Business Data Analytics
- 2. 1.Identify Objectives 2.Data Gathering 3.Understand the Importance of 3 Vs 4.Data Preparation
- 3. Before stepping into data analytics, the very first step all businesses must take is identify objectives. Once the goal is clear, it is easier to plan especially for the data science teams. Initiating from the data gathering stage, the whole process requires performance indicators or performance evaluation metrics that could measure the steps time to time that will stop the issue at an early stage. This will not only ensure clarity in the remaining process but also increase the
- 4. Data gathering being one of the important steps requires full clarity on the objective and relevance of data with respect to the objectives. In order to make more informed decisions it is necessary that the gathered data is right and relevant. Bad Data can take you downhill and with no relevant report.
- 5. Volume, Variety and Velocity The 3 Vs define the properties of Big Data. Volume indicates the amount of data gathered, variety means various types of data and velocity is the speed the data processes.
- 6. Data preparation also called data cleaning is the process in which you give a shape to your data by cleaning, separating them into right categories, and selecting. The goal to turn vision into reality is depended on how well you have prepared your data. Ill-prepared data will not only take you nowhere, but no value will be derived from it.