Make informed decisions with Big Data Analytics

A study by NVP revealed that increased use of Big Data Analytics to make decisions that are more informed has proven to be noticeable success. More than 80% of executives confirmed the investment in big data to be profitable and almost half said their organization could measure the benefits of their projects.

When it is difficult to find such exceptional results and optimism in all business investments, Big Data Analytics has found how it can be the glowing result for companies to do it the right way. This post will tell you how much data analytics is changing the way the company makes informed decisions. Additionally, why companies use big data and a detailed process to allow you to make more accurate and informed decisions for your business.

Why do organizations harness the power of Big Data to reach their goals?

There was a time when crucial business decisions were made based solely on experience and intuition. In the technological era, however, the focus shifted to data, analytics and logistics. Today, while designing marketing strategies that engage customers and increase conversions, observers, analysts and executors conduct in-depth customer behavior studies to get to the roots rather than following conventional methods, where they are highly dependent on customer response.

Five exabytes of information were created between the daytime civilization through 2003, which has vastly increased to the generation of 2.5 quintillion bytes of data each day. That’s a huge amount 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 certainly leads to making the most accurate decisions and highly predictable results. According to Forbes, 53% of companies use data analytics today, up from 17% in 2015. It ensures prediction of future trends, success with marketing strategies, positive customer response and increase in conversion, and more.

Various stages of Big Data Analytics

Being a disruptive technology Big Data Analytics has inspired and directed many companies to not only make informed decisions but also help them decode information, identify and understand patterns, analysis, calculation, statistics and logistics. Using to your advantage is as much art as it is science. Let’s divide the complicated process into different stages for better understanding of Data Analytics.

Identify goals:

Before embarking on data analytics, the very first step that every business should take is identifying goals. Once the goal is clear, it is easier to plan specifically for data science teams. Starting from the data collection stage, the entire process requires performance indicators or evaluation methods that can measure the steps over and over that will stop the problem at an early stage. This will not only ensure clarity in the remaining process, but also increase the chances of success.

Data collection:

Data collection, which is one of the important steps, requires full clarity as to the objectives and relevance of data with regard to the objectives. To make more informed decisions, it is necessary that the data collected is accurate and relevant. Bad data can take you downhill and without any relevant report.

Understand the importance of 3 Vs

Volume, variation and speed

3 Vs defines the properties of Big Data. Volume indicates the amount of data collected, variation means different types of data and speed is the speed of the data processes.

Define how much data to measure

Identify relevant data (for example, when designing a game app, categorize by age, game type, medium)

Look at the data from the customer perspective. It helps you with details such as how long you need to take and how much to respond within your client’s expected response time.

You need to identify data accuracy, it is important to collect valuable data and make sure you create more value for your customer.

data Preparation

Data preparation, also known as data cleaning, is the process where you give shape to your data by cleaning, dividing it into real categories and choosing. The goal of making the vision come to fruition depends on how well you have prepared your data. Poorly prepared data will not only take you nowhere, but no value will be derived from it.

Two key areas of focus are what kind of insight is required and how will you use the data. In order to streamline the data analysis process and ensure that you derive value from the result, it is important that you align data preparation with your business strategy. According to the Bain report, “23% of companies surveyed have clear strategies for using analytics effectively.” That is why you need to identify the data and insights are important to your business.

Implementation of tools and models

After completing the long collection, cleaning and preparation of the data, statistical and analytical methods are used here to get the best insight. Out of many tools, data scientists require to use the most relevant statistical and algorithm development tools for their goals. Choosing the right model is a thought-provoking process as the model plays the key role in bringing valuable insight. It depends on your vision and the plan you have to execute using insight.

Turn information into insight

“The goal is to turn data into information and information into insight.”

– Carly Fiorina

Being at the heart of the Data Analytics process, at this stage, all information becomes insight that could be implemented in respective plans. Insight simply means the decoded information, understandable relationship that comes from Big Data Analytics. Thoughtful and thoughtful execution gives you measurable and actionable insights that will give your business great success. By implementing algorithms and reasoning on the data that comes from the modeling and tools, you can receive the valuable insights. Insight generation is heavily based on organizing and curating data. The more accurate your insights, the easier it is for you to identify and predict the results as well as future challenges and deal with them effectively.

Execution of insight

The final and important phase is to execute the derived insights into your business strategies to get the most out of your data analysis. Precise insights implemented at the right time in the right strategy model are important where many organizations fail.

Challenges organizations tend to face frequently

Despite being a technological invention, Big Data Analytics is an art that handled properly can lead your business to success. While it may be the most preferred and reliable way to make important decisions, there are challenges such as cultural barriers. When making major strategic business decisions about their understanding of the company and the experience, it is difficult to convince them to rely on data analytics, which is objective and data-driven, embracing the power of data and technology. Yet, aligning Big Data with traditional decision-making to create an ecosystem allows you to create accurate insights and execute effectively in your current business model.

According to Gartner Global’s business intelligence (BI) and analytics software revenue, it is expected to reach $ 18.3 billion in 2017, up 7.3 percent from 2016. This is a large number and you would like to invest in a intelligent solution.