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1. Make Informed Decisions With Big Data Analytics A survey conducted by NVP revealed that increased usage of Big Data Analytics to take decisions that are more informed has proved to be noticeably successful. More than 80% executives confirmed the big data investments to be profitable and almost half said that their organization could measure the benefits from their projects. When it is difficult to find such extraordinary result and optimism in all business investments, Big Data Analytics has established how doing it in the right manner can being the glowing result for businesses. This post will enlighten you with how big data analytics is changing the way businesses take informed decisions. In addition, why companies are using armstrong number in java. and elaborated process to empower you to take more accurate and informed decisions for your business. Why are Organizations harnessing the Power of Big Data to Achieve Their Goals? There was a time when crucial business decisions were taken solely based on experience and intuition. However, in the technological era, the focus shifted to data, analytics and logistics. Today, while designing marketing strategies that engage customers and increase conversion, decision makers observe, analyze and conduct in depth research on customer behavior to get to the roots instead of following conventional methods wherein they highly depend on customer response. There was five Exabyte of information created between the dawn of civilization through 2003 which has tremendously increased to generation of 2.5 quintillion bytes data every day. That is a huge amount of data at disposal for CIOs and CMOs. They can utilize the data to gather, learn, and understand Customer Behavior along with many other factors before taking important decisions. Data analytics surely leads to take the most accurate decisions and highly predictable results. According to Forbes, 53% of companies are using data analytics today, up from 17% in 2015. It ensures prediction of future trends, success of the marketing strategies, positive customer response, and increase in conversion and much more. Various stages of Big Data Analytics Being a disruptive technology Big Data Analytics has inspired and directed many enterprises to not only take informed decision but also help them with decoding information, identifying and understanding patterns, analytics, calculation, statistics and logistics. Utilizing to your advantage is as much art as it is science. Let us break down the complicated process into different stages for better understanding on Data Analytics. Identify Objectives: 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

2. 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 chances of success. Data Gathering: 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. Understand the importance of 3 Vs 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. Define how much data is required to be measured Identify relevant Data (For example, when you are designing a gaming app, you will have to categorize according to age, type of the game, medium) Look at the data from customer perspective.That will help you with details such as how much time to take and how much respond within your customer expected response times. You must identify data accuracy, capturing valuable data is important and make sure that you are creating more value for your customer. Data Preparation 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. Two focus key areas are what kind of insights are required and how will you use the data. In- order to streamline the data analytics process and ensure you derive value from the result, it is essential that you align data preparation with your business strategy. According to Bain report, "23% of companies surveyed have clear strategies for using analytics effectively". Therefore, it is necessary that you have successfully identified the data and insights are significant for your business. Implementing Tools and Models After completing the lengthy collecting, cleaning and preparing the data, statistical and analytical methods are applied here to get the best insights. Out of many tools, Data scientists require to use the most relevant statistical and algorithm deployment tools to their objectives. It is a thoughtful process to choose the right model since the model

3. plays the key role in bringing valuable insights. It depends on your vision and the plan you have to execute by using the insights. https://bigdata-world.net/armstrong- number-in-java/