Today’s marketplace is no longer a steady and stable one! As the market transforms from product-focused to consumer-focused, businesses need to respond fast to ever-changing customer demands. Agility and Innovation are the demands of the ever-evolving market. This has made it imperative to strategize in entirety so that various functions within the organization work cohesively for the attainment of a common goal. And, it is important, now more than ever, to take decisions that are data-based so that they are proactive rather than reactive. As per McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, 6 times more likely to retain them resulting in the probability of being 19 times more profitable!
Data-based decisions provide an edge to businesses over their competitors. Data can be used to enhance the performance of each and every department of the organization.
We are heading towards a future where business analytics will no longer be a choice but a necessity. This is substantiated by Forrester Insights-Driven Businesses Set the Pace for Global Growth Report. This report states that insights driven businesses are growing at an average of over 30% annually and by 2021 they are expected to take $1.8 trillion from their less informed peers!
Fortunately, we are living in the information age and accessing data is not a difficult task. Although data is easily available, it is vital that an organization filters relevant information that addresses the issues it is facing. The data should aid decision making and it should be accurate.
This is where data analytics becomes useful! It helps in identifying and leveraging the right kind of data.
Business Intelligence enables an organization to analyze the current performance of its team members, departments and processes based on historical data.
Stream Analytics is used for manipulating the real-time data. It conducts statistical analysis on data while moving with the data stream.
Historical and real-time data, machine learning algorithms are used to predict future trends with the help of predictive analysis.
In this age of unpredictable consumer behavior, predictive analysis comes as a ray of hope to businesses! They are able to predict consumer reactions to marketing messages and get an idea of their purchasing decisions. This empowers the business to optimize the performance of its assets. This positively impacts the performance of the business enhancing its revenue!
Step 1: Define your objectives
When initiating a data analytics process, you will need to define what you propose to achieve by analyzing the data. This will give direction to the data analysis process.
Step 2: Data Discovery
In order to start the process of data analysis, data is required. Data is collated from different sources and silos for the purpose of analysis. The data is integrated and transformed into a common format and uploaded into a data analytics system such as a data warehouse. A data warehouse serves as a storage for data. Data can be extracted for analysis as and when required from the data warehouse.
Step 3: Data Mining
Data Warehouse contains large volumes of data. However, not all the data is relevant for analytics. Therefore, data mining is done to extract useful data. Data mining helps transform the raw data sources into a consistent schema which simplifies data analysis.
Step 3: Ensuring Data Quality
After data integration, it is essential to take necessary measures to ensure that the data is free of quality issues as this will impact the accuracy of the data analytics. To avoid errors due to duplicate data entry or any other error, data profiling and data cleansing is conducted.
Step 4: Data Governance
The guidelines for manipulation and organizing of data is done by data governance. Data Governance entails establishing a set of procedures and plan for the execution of the procedures. Data Governance manages the availability, usability, integrity, and security of data.
Step 5: Building and Testing Analytical Models
The next step is data analysis by data scientists with the help of systems, algorithms, etc. to extract insights from data. For this, analytical models are built with the help of analytics software or programming languages. The accuracy of the model is tested by running partial data. More revisions of testing the model are carried out so as to “train” the model to function as intended. After training is complete, the model is run on full production mode.
Step 6: Data Visualization
Data is displayed in a user-friendly manner for the decision makers. Data visualization is done with the help of infographics or visual display of Business Intelligence dashboards. Additionally, reports are generated.
Step 7: Update the System with the Decision
Based on the results of the data analysis, the decision makers check if the results serve the original purpose of the analysis. If they do, they are applied to the process and the outcome of this decision measured. If it fulfills the set objectives, it is implemented else the cycle is repeated.
These are the stages through which raw data travels in order to become useful.
Data is necessary for decision making but it is growing at such a fast pace that it will soon become unmanageable. As per IDC Data Age 2025, the total amount of digital data created worldwide will rise to 163 zettabytes ballooned by a growing number of devices and sensors!
Businesses will have to think beyond conventional data management techniques. Businesses are looking towards Big Data Analytics as the solution for handling large data volumes.
Big data analytics is an emerging trend in the field of data analytics. The scope of Big Data is unlimited offering businesses an endless source of insights creating new opportunities for the business. Big Data offers scalability and agility to the business. Big data analytics is being adopted across various verticals globally. In fact, as per IDC's Worldwide Semiannual Big Data and Analytics Spending Guide survey, expected Global Big Data and Business Analytics Revenue in 2019 was a whopping 189.1 billion USD!
Data Analytics is an indispensable part of business operation in the current market scenario. Data Analytics employs qualitative and quantitative techniques to enable organizations to improve their productivity and enhance profits. However, as data grows in volumes, data analytics has its limitations. Big data analytics is the way ahead! It offers tools to manipulate large volumes of data to provide meaningful insights to businesses.
Big data analytics is being used extensively in various industries such as healthcare, banking, energy, technology, and many more!
However, leveraging Big Data Analytics requires the help of a professional who has expertise in the field.
If you are looking to transform your business with the help of insight-based decisions, it is time to connect with the Big Data Solutions Team of V2Soft!
Let’s Talk!Contact Us to have one of our experts reach out and discover how we can help you with your challenges.