Transforming Enterprise Decision-Making With Big Data Analytics
Transforming Enterprise Decision-Making With Big Data Analytics
Aligning big data with traditional decision-making processes to create an ecosystem will allow you to create accurate insights and execute efficiently in your current business model.
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A survey conducted by NVP revealed that the increased usage of big data analytics to make 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 a glowing result for businesses. This post will enlighten you on how big data analytics is changing the way businesses make informed decisions. In addition, you'll understand why companies are using big data and elaborated processes 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 made 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.
Five exabytes of information were created between the dawn of civilization through 2003, which has tremendously increased to the 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 making important decisions. Data analytics surely leads to making the most accurate decisions and getting highly predictable results. According to Forbes, 53% of companies are using data analytics today, up from 17% in 2015. It ensures the prediction of future trends, the success of the marketing strategies, positive customer response, and an 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 make informed decisions but also has helped them with decoding, identifying, and understanding information, patterns, analytics, calculations, statistics, and logistics. Utilizing it to your advantage is as much art as it is science. Let's 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 to 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.
Understanding the Importance of the 3 Vs
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, and medium).
- Look at the data from the customer perspective. That will help you with details such as how much time to take and how to respond within your customer's expected response times.
- You must identify data accuracy. Capturing valuable data is important. 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 it into right categories, and selecting it. The goal to turn vision into reality is dependent on how well you have prepared your data. Ill-prepared data will not only take you nowhere but also no value will be derived from it.
Two key focus 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 collection, cleaning, and preparation the data, statistical and analytical methods are applied here to get the best insights. Out of many tools, data scientists require using 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 give 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 valuable insights. Insight generation is highly based on organizing and curating data. The more accurate your insights are, the 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 most 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 making important decisions, there are challenges (such as cultural barriers). When major strategical business decisions are taken on their understanding of the businesses and experience, it is difficult to convince them to depend on data analytics, which is objective, and data-driven process where one embraces the power of data and technology. Yet, aligning big data with traditional decision-making processes to create an ecosystem will allow you to create accurate insights and execute efficiently in your current business model.
Published at DZone with permission of Hemang Rindani . See the original article here.
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