Transforming Enterprise Decision Making 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. (Source)
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 big data 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.
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 chances of success.
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 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 plays the key role in bringing valuable insights. It depends on your vision and the plan you have to execute by using the insights.
Turn Information into Insights
The goal is to turn data into information, and information into insight.”
– Carly Fiorina
Being the heart of the Data Analytics process, at this stage, all the information turns into insights that could be implemented in respective plans. Insight simply means the decoded information, understandable relation derived from the Big Data Analytics. Calculated and thoughtful execution gives you measurable and actionable insights that will bring great success to your business. By implementing algorithms and reasoning on the data derived from the modeling and tools, you can receive the valued insights. Insight generation is highly based on organizing and curating data. The more accurate your insights are, easier it will be for you to identify and predict the results as well as future challenges and deal with them efficiently.
The last and important stage is executing the derived insights into your business strategies to get the best out of your data analytics. Accurate insights implemented at the right time, in the right model of strategy is important at which many organization fail.
Challenges organizations tend to face frequently
Despite being a technological invention, Big Data Analytics is an art that handled correctly can drive your business to success. Although it could be the most preferable and reliable way of taking important decisions there are challenges such as cultural barrier. When major strategical business decisions are taken on their understanding of the businesses, experience, it is difficult to convince them to depend on data analytics, which is objective, and data driven process where one embraces power of data and technology. Yet, aligning Big Data with traditional decision-making process to create an ecosystem will allow you to create accurate insight and execute efficiently in your current business model.
You can harness the power of Big Data and accelerate growth of your organization. Cygnet can help you grow your business rapidly with intelligent solutions, effective insights using data science. For more information on how you can utilize Big Data technology to take informed business decisions write to us at email@example.com
Cygnet Infotech is a CMMi level 3 and is ISO 27001:2013 and ISO 9001:2008 certified, 18 year old global technology provider enabling ISVs & enterprises through emerging technology, software engineering, technology consulting, SAP Implementation, advisory & maintenance services. It’s proven 18-year track record boasts of successfully delivered 1400+ valuable software solutions to its diverse clientele across the globe using a global delivery model.View All Posts