With the role of technology becoming multifold in every industry, a large volume of information is important in yielding valuable insights and determining the next steps. In addition, data collection goes hand in hand with analysis to be useful for decision making. This, in turn, has led to the boom of big data analytics to use data in making crucial decisions.
Today’s rise in technological advancements has influenced how we use data sources for analytics. There is information coming from your website traffic, mobile devices, social media, and the Internet of Things (IoT).
What is Big Data Analytics?
Big data analytics refers to the process of using techniques to work on diverse big data sets, including structured, semi-structured, and unstructured data. These data sets might originate from a number of platforms, including web, mobile devices, social media, and smart gadgets.
Below are the differences among the data formats:
- Structured data, such as databases and spreadsheets;
- Semi-structured data like XML files and websites; and
- Unstructured data like photos and audio.
In all, this business intelligence solution describes the procedures, equipment, and software used to gather, analyze, and draw conclusions from a wide range of high-volume, high-speed data sets.
Related Article: Everything You Need to Know About Data Science
Importance of Big Data Analytics
Analyzing data provides significant value, since the concept of big data has been around for years. It helps organizations to utilize data sets to identify new opportunities, predict and prevent business risks, and optimize business operations. Moreover, the process of analyzing large volumes of data provides businesses and industries a head start on their next process or strategy toward growth and development.
The information provided by big data analytics not only increases operational efficiency; it also improves customer experience and reduces risks by identifying discrepancies. Thus, big data analytics empowers businesses to make room for innovation. No matter what industry you are in, analysis of data sets helps companies to become agile in creating business plans.
Today, businesses can collect and analyze data in real time to make well-informed decisions. Aside from decision-making, it’s also vital for improving a business’s speed, efficiency, agility, and competitiveness. Consequently, technologies such as business intelligence, cloud computing, and artificial intelligence are among today’s core business solutions in this changing business world.
Since you already have an idea of what big data analytics is along with its importance, let’s take a look at the analytics process.
How Does Big Data Analytics Work?
Data can be collected from anywhere and any part of our daily lives in more ways than we can think of. As an example, you can capture data from online platforms that we use daily, such as Facebook and Spotify. And the number of digital data we are creating is growing exponentially.
According to an article by SAS, since the early 2000s, big data has become popular when information systems made it possible for organizations to manage large volumes of unstructured data. New technologies, including smartphones, have contributed to uncovering large amounts of data to help with data-informed decisions. The process involves the collection, processing, cleaning, and analysis of large datasets to manage the big data or organizations.
Big data analytics plays a vital role in producing insights, which can lead to better decisions and strategic business moves.
For an overview of how it works, let’s dive into the process.
1. Data Collection
Nearly limitless sources of information on a wide range of subjects are available on the internet. That said, the most important step in the cycle is data collection. Then, data is placed into databases for storage or further processing.
Many companies rely on data collection and analysis. Through this, decision makers can have accurate insights on the enterprise’s operations. Aside from online sources of data, conventional businesses also have access to a variety of external data sources that they may integrate with transactional data.
2. Data Processing
For the analysis to yield correct answers, data must be appropriately organized once it has been gathered and stored, especially if the data is big and unstructured. Data processing is becoming more difficult for corporations as the amount of data available increases dramatically. Additionally, big data processing is frequently carried out in sizable clusters.
3. Data Cleaning
The process of data cleansing is an important part of data analytics since this is where data must be segmented and corrected. Plus, inaccurate and corrupted data must be removed. The quality of data is important in enabling quality decisions. Thorough data cleansing ensures data quality.
4. Data Analysis
You can complete data analysis using various tools and methods, such as data mining, predictive analytics, and deep learning. It finds patterns in the most complex and abstract data with proper tools and technologies.
Benefits of Big Data Analytics
Big data analytics technologies and software may help organizations make data-driven decisions that can enhance the results of their business operations. Benefits might include enhanced consumer personalization, streamlined operational efficiency, and more effective marketing. You can have these advantages over competitors with a strong data-driven strategy.
Types of Big Data Analytics
Big data provides better insights with the correct analytics since it uses data from several sources and transactions to reveal hidden patterns. Big data analytics is divided into the following four subcategories:
1. Descriptive Analytics
Descriptive analytics offers insights into historical events and the trends to identify information. This involves creating reports on a company’s earnings, sales, and other metrics.
2. Diagnostic Analytics
You use diagnostic analytics to find out what happened or to establish what caused a situation or problem. As a result, businesses can better make decisions about how to address issues and boost their level of performance.
3. Predictive Analytics
As implied by the name, predictive analytics focuses on predicting future instances, which include market trends and consumer trends. It records previous actions and makes predictions about potential future actions based on data.
4. Prescriptive Analytics
Whereas predictive analytics aims to predict potential outcomes, prescriptive analytics on the other hand seeks to discover the optimum solution given a number of options. Both prescriptive and predictive analytics provide business intelligence insights based on what has occurred.
Play a Pivotal Role in Decision Making by Understanding Big Data Analytics
As the adoption of data analytics grows, mastering the process of big data analytics and business intelligence has increasingly become a necessity. So, why not build job-ready skills in the field of business information systems, data mining, big data systems, data structures and algorithms, and other related areas of study by pursuing a Bachelor of Science in Information Systems (BSIS) with CIIT?
At CIIT, you’ll not only hone your technical and analytical skills in business process management, evaluation of business performance, quantitative methods, and machine learning. But also study a curriculum that’s been validated by industry leads and you’ll train under the guidance of top experts in the field.
If you’re prepared to begin your career as a data analyst or business analyst, then mark your future and tap into your potential with us.
CIIT Philippines is an innovative industry-based academic institution and home for creators and innovators. We focus on providing quality arts and technology courses, which fully equip our students to achieve entrepreneurial success. Reimagine the future with us as we prepare you for a career shaped by digital technology. Bring insights to companies and enable them to identify analytics opportunities within their organization. Join us today!
