Make informed decisions with Big Data Analytics
A survey by NVP revealed that increased use of Big Data Analytics to make more informed decisions has proven to be remarkably successful. More than 80% of executives confirmed that investments in big data were profitable, and nearly half said their organization could measure the benefits of their projects.
When it is difficult to find such extraordinary results and optimism in all business investments, Big Data Analytics has established that doing it the right way can be a brilliant result for companies. This post will explain how big data analytics is changing the way companies make informed decisions. Additionally, it explains why companies use big data and elaborate processes to enable you to make 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 made based solely on experience and intuition. However, in the technological age, the focus was on 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 rather than following conventional methods in business. which largely depend on the customer’s response.
Five exabytes of information were created between the dawn of civilization and 2003, vastly increasing to the generation of data of 2.5 quintillion bytes every day. That’s a lot of data available to CIOs and CMOs. They can use the data to collect, learn, and understand customer behavior along with many other factors before making important decisions. Data analysis undoubtedly leads to the most accurate decisions and highly predictable results. According to Forbes, 53% of companies use data analytics today, up from 17% in 2015. Ensures prediction of future trends, success of marketing strategies, positive customer response and increased conversion and much more.
Various stages of Big Data Analytics
Being a disruptive technology, Big Data Analytics has inspired and directed many companies not only to make informed decisions, but also to help them decode information, identify and understand patterns, analysis, calculation, statistics and logistics. Using it to your advantage is both art and science. Let’s divide the complicated process into different stages to better understand data analysis.
Identify objectives:
Before getting into data analysis, the first step that all companies must take is to identify objectives. Once the goal is clear, it is easier to plan especially for data science teams. Starting from the data collection stage, the whole process requires performance indicators or performance evaluation metrics that can measure the steps from time to time that will stop the problem at an early stage. This will not only ensure clarity in the remaining process, but will also increase the chances of success.
Data collection:
Data collection is one of the important steps that requires full clarity about the objective and the relevance of the data to the objectives. To make more informed decisions, it is necessary that the data collected is correct and relevant. Bad data can take you downhill and without a relevant report.
Understand the importance of 3 Vs
Volume, variety and speed
The 3 Vs define the properties of Big Data. Volume indicates the amount of data collected, variety means various types of data, and speed is the speed with which the data is processed.
Define how much data is required to measure
Identify the relevant data (for example, when you are designing a game application, you will have to categorize according to age, type of game, medium)
Look at the data from the customer’s perspective, which will help you with details like how long to take and how much to respond within the customer’s expected response times.
You need to identify the accuracy of the data, capturing valuable data is important, and making sure you are creating more value for your customer.
Data preparation
Data preparation, also called data cleansing, is the process where you give your data a shape by cleansing, separating it into the correct categories, and selecting. The goal of turning the vision into reality depends on how well you have prepared your data. Not only will poorly prepared data get you nowhere, but no value will be derived from it.
Two key areas of focus are what type of information is required and how you will use the data. To streamline the data analysis process and ensure that you get value out of the result, it is essential that you align data preparation with your business strategy. According to the Bain report, “23% of the companies surveyed have clear strategies for using analytics effectively.” Therefore, you need to have successfully identified the data and insights that are important to your business.
Implementation of tools and models
After completing the lengthy data collection, cleaning, and preparation, statistical and analytical methods are applied here to gain the best insights. Among many tools, data scientists need to use the statistical and algorithm implementation tools most relevant to their goals. Choosing the correct model is a thoughtful process, as the model plays a key role in providing valuable information. It depends on your vision and the plan you have to execute using the insights.
Turn information into knowledge
“The goal is to turn data into information and information into knowledge.”
– Carly Fiorina
Being the heart of the data analysis process, at this stage, all the information is converted into knowledge that could be implemented in the respective plans. Insight simply means decoded information, understandable relationship derived from Big Data Analytics. Thoughtful and calculated execution gives you measurable and actionable information that will bring great success to your business. By implementing algorithms and reasoning about data derived from modeling and tools, you can receive valuable information. The generation of information is largely based on the organization and preservation of data. The more accurate your insights, the easier it is for you to identify and predict outcomes, as well as future challenges, and address them efficiently.
Insights execution
The last and important stage is to execute the insights derived from your trading strategies to get the most out of your data analysis. Accurate information implemented at the right time, in the correct strategy model is important where many organizations fail.
Challenges that organizations tend to face frequently
Despite being a technological invention, Big Data Analytics is an art that, managed correctly, can lead your business to success. Although it might be the most preferable and reliable way to make important decisions, there are challenges such as the cultural barrier. When making important strategic business decisions based on their understanding of the business and experience, it is difficult to convince them to rely on data analysis, which is objective, and a data-driven process in which one embraces the power of data and technology. However, aligning Big Data with the traditional decision-making process to create an ecosystem will allow you to create accurate information and efficiently execute your current business model.
According to Gartner Global, revenue in the business intelligence (BI) and analytics software market is forecast to reach $ 18.3 billion in 2017, a 7.3 percent increase from 2016. This is already a large number. you too would like to invest in a smart solution. .