Imagine a world without data! Quite impossible to think of such an idea, isn’t it? With the value advantages of analytics, it is possible to derive value from data existing all over the world. How? Let us find the answer by understanding the basics of data analytics.
Data is the most potent asset in the modern world of technology. It is practically impossible to get anything done without data nowadays. Interestingly, Statista estimates suggest that the total volume of data created, captured, and consumed worldwide amounted to 79 zettabytes in 2021. As the volume of data generated all over the world continues to increase exponentially with every passing day, it is reasonable to wonder about the best ways to derive value from such massive volumes of data. Therefore, the significance of “what is data analytics” is far more than you may have expected. Rather than serving a new technology, data analytics presents a new paradigm for extracting value from data.
What exactly goes on with data analytics? What are the potential reasons for introducing data analytics? Does data analytics deliver the insights required by enterprises? Or, do you have to depend on generalized observations from data? Let us find the answers to all these questions with a beginner’s guide on data analytics and data science alongside their value advantages.
What is Data Analytics?
Have you ever wondered how you come across advertisements of brands you have interacted with recently? For example, you may receive ads from Amazon to purchase a new computer table after you have purchased a desktop computer. Why? How does Amazon know that you need a table for your computer? The answer is quite obvious. Amazon has the data about you purchasing a computer and uses it to infer that you may need a table. How does Amazon send its ads specifically to you? Well, this is where you would understand how big data can be a revolutionary concept.
In simple words, data analytics is basically evaluating raw data for finding any trends and patterns. It helps organizations in data analysis to identify patterns and derive insights, which can inform and guide decisions. Data analytics relies on different data types, including real-time, qualitative, historical, structured and unstructured data. Many of the modern data and analytics solutions provide support for end-to-end data analytics capabilities. These systems take care of everything, starting from accessing and preparing data to its analysis, followed by the operationalization of analytics. In addition, data analytics systems also take care of monitoring the results. Reach out to credible data analytics service providers such as Visvero and learn more about data analytics now.
Why Did Data Analytics Emerge?
Considering the significance of data analytics in changing how we view and use data, it is important to know where it emerged. What were the reasons behind the introduction of data analytics? You can find the answer to this question only by reflecting on the history of data analytics.
The earliest use of data analytics for business purposes goes back to the 19th century. Frederick Winslow Taylor showed one of the first examples of data analytics with his time management exercises. In addition, Henry Ford also utilized analytics for measuring the speed of assembly lines. Traditionally, analytics has served many useful applications in governance, for example, the collection of census data to identify useful insights and data for governance decisions. Analyzing population growth by different regions could offer plausible insights into the development of new public utility projects.
History of Data Analytics
How was it possible to achieve such uses of data and analytics without the technology available today? As a matter of fact, data analytics has been through a long history with some prominent milestones. Let us take a look at some of the biggest moments in the history of data analytics.
The arrival of computers in the 1880s transformed the data analytics process by dramatic margins.
Relational Database Management Systems, or RDBMs arrived in the 1970s and gained profound popularity in the 1980s. RDBMs and SQL offer the flexibility for on-demand data analytics with support for maintaining detailed records with accuracy.
Data warehouses evolved in the late 1980s to cope with the significant growth in the amount of data collected. The introduction of data warehouses supported the transformation of data from operational systems into decision-making support systems.
NoSQL databases emerged in the 1990s as the internet started gaining popularity. The NoSQL databases could ensure flexible data translation by leveraging different languages and formats.
Data mining also made its impact in data science in the 1990s, with the growing focus on larger data sets. Many experts acknowledge the evolution of database technologies and data warehouses as motivators for data mining.
The year 2005 served as a major highlight in the course of the history of big data and analytics, with some exceptional advancement. People came across the term ‘big data for the first time and popular analytics tools such as Hadoop.
How Does Data Analytics Deliver the Desired Functionalities?
The fundamentals of data analytics and its background showcase how the field has developed with technological advancements. However, one would definitely wonder about the practical application of data analytics in healthcare industry and many other sectors. This is where you need to take a look at the features and the practical methods for implementing data analytics.
What Are The Essential Features Required in data analytics solutions?
Business Intelligence and Reporting
One of the most common and sought-after capabilities in data management and analytics tools is a business intelligence and reporting. Business owners, leaders and end-users can depend on data analytics solutions for making informed business decisions. For example, business analytics tools can offer reports and dashboards for effective monitoring of business progress, partner status, revenue, outages and many other data points. Explore the data analytics solutions by Visvero with BI capabilities right now.
Data Preparation
Data preparation, also referred to as data wrangling, refers to preparing the raw data for analysis. The data preparation capabilities in data analytics solutions help bring data together from different sources and clean them up for better analysis.
Data Visualization
Data visualization is one of the key highlights of the benefits of big data analytics, as it offers a visual representation of data. Data scientists and analysts could use graphical representation of data to visually explore and identify the data patterns. Furthermore, a data analytics solution with comprehensive data visualization capabilities could speed up and simplify data exploration.
Location Analytics
Another significant functionality of data analytics, which has significant value in the process, is location analytics. The location or geospatial analytics layer in a data analytics system could help in developing precise, relevant insights and connections in the data.
Predictive Analytics
The most significant functionality in data analytics for a modern data analyst would obviously point at predictive analytics. Businesses all over the world rely on data analytics to draw accurate predictions on certain events. Predictive analytics work by taking historical data as inputs in a model tailored for predicting future events. You can rely on credible data analytics solution providers like Visvero to make the most of your data with predictive analytics now.
Machine Learning
The next crucial factor that would boost the value of data science further is machine learning. With the help of machine learning, automated analytics models could support iterative learning from data with better performance optimization. Some of the advanced machine learning functionalities in modern big data analytics solutions include augmented analytics, natural language processing and image analytics.
Streaming Analytics
The final and most important essential functionality in the working of business data analytics is streaming analytics. Streaming analytics basically refers to the collection of data from IoT streaming devices, social media platforms, video sources and audio sources.
Where is Data Analytics Headed in 2022?
The broad range of functionalities in data analytics shows how it can transform the value of data for businesses across various sectors. The applications of big data analytics in healthcareindustry, advertising and business operations have been expanding gradually over the course of time. According to the estimates of IDC analysts, businesses all over the world have spent almost $215 million on data analytics solutions in 2021. By 2022, experts have pointed out that almost 90% of corporations would recognize data as a critical enterprise asset. How will the field of big data and analytics grow in the coming years? Let us take a look at some of the expected trends in the field of data analytics for 2022.
The dominance of Machine Learning and AI
One of the foremost trends affecting how data analytics finds applications in different sectors is artificial intelligence. The advantages of automation and machine learning in data play a huge role in accenting the benefits of data analytics in healthcare and other sectors. AI can elevate business value by augmenting human capabilities. For example, AI can help obtain better demand forecasts that would drive promising growth in sales.
Frontrunners in Data Analytics Adoption
According to the Business Intelligence Market Study, 2021 by Dresner, retail/wholesale, technology and financial services organizations are likely to increase their budgets for business intelligence by more than 50%. Therefore, you can clearly point out the frontrunners in the adoption of data analytics solutions.
Edge Computing Rises
Another formidable trend that would define the big data analytics sphere is the growth of edge computing. Predictions suggest that the edge computing market would grow to almost $87.3 billion in 2026. With the gradual transfer of processing power to the edge, data analytics are also slated to move towards the edge—for example, integration of real-time analytics capabilities for IoT devices.
Final Words
The concept of data analytics has played a critical role in the transformation of many businesses and industries altogether. Data analytics is driving a wide range of valuable benefits from product development to the facility of targeted content. However, you need the right technical expertise and support to make the best of your enterprise’s analytics and big data. Without the facility of proper guidance regarding why you need data analytics and how to use it, you may end up with insufficient results. Therefore, you need a credible digital transformation consulting service provider to navigate your way around the mazes of data analytics. Get in touch with Visvero to make the most of effective data analytics services right now.