By taking a brief look at the history of data analytics, as well as examining the current practices in this field, we can get a glimpse of how this discipline has evolved, as well as gain insights into where data analytics is expected to go in the coming years.
What is Data Analytics?
Data is everywhere; it comes in different sizes and from different sources. There’s data in every second of recorded security camera footage, each open-ended survey question response, and even in the quarterly grades of all the students in a given school district. However, until this data is sorted and analyzed, none of it is actionable. Each day, an estimated 2.5 quintillion bytes of data are created. The past two years alone accounted for 90% of this data creation. But what to do with it all?
This is where data analytics comes in. Data analytics is the set of techniques used to analyze raw data (unprocessed data) to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.
A Brief History of Data Analytics
Big data is not a new concept. As early as the 1950s, before the use of the term “big data, ” companies and organizations used basic analytics such as manually examining numbers on a spreadsheet, to identify trends and key insights.
As the field of data analytics evolved, so did the speed and efficiency with which data was examined and useful insights applied to immediate decision making. The faster an organization could extract and use information from data, the greater advantage it would have over competitors. Today, a variety of techniques and methods exist for analyzing data, which depend on the aim of analysis and the industry in which the insights will be applied.
Data Analytics in 2021
In 2021, data analytics remains a popular method for cutting down business costs, charting new strategic directions, and mitigating risk. The many benefits data analytics provides to an organization help them not just stay afloat, but thrive, even in a tumultuous economic environment.
Here are a few of the most important current trends in data analytics that are being used in 2021 by companies and organizations to more efficiently and effectively manage their data needs:
- Blockchain analytics: This process involves identifying, analyzing, and clustering data on a cryptographic distributed ledger, where it is then accessible to all. Data can be modeled and visually depicted so that important insights about users and their transactions can be extracted.
- AIOps: This term pertains to applying AI to IT environments. This tool allows teams to work quickly and efficiently, and detect issues with digital service early and implement measures to resolve the problem before it can affect business operations. AIOps is especially useful in hybrid environments, as well as those that are dynamic, distributed, and componentized.
- Embedded analytics: This form of analytics integrates analytic capabilities and content within applications. Its use helps those working with data to incorporate timely or relevant data and analytics to find solutions to complex business problems, which allows employees to work more efficiently, since these capabilities are inside the applications that are used on a daily basis. This approach enables users to receive important notifications directly rather than having to log in to an analytics platform and pick through the information to isolate what is needed.
- Self-service data analytics: This form of data analytics enables end users, even those who don’t come from a technical background, to access data and generate or customize analyses or reports.
- Data integrity tools: This term pertains to the variety of tools currently available that help users maintain data integrity and the referential structure of a database such as data constraints or defaults.
- Smarter AI: New AI technologies such as AI chatbots, allow companies to devise smarter ways to use their data. AI can be used for extracting value from large datasets and spotting patterns or trends that would be difficult or impossible for a human to notice. Currently, nearly half of all companies rely on AI for handling data quality. This powerful tool can be used to quickly predict investment outcomes and to devise strategies or establish long-term goals.
Several of these tools such as AI and AIOps, are not only changing the current data analytics landscape but also provide snapshots of the future of this expanding field.
The Future of Data Analytics
A major transformation is underway in the field of big data analytics. Many of the current trends spring from the convergence of a variety of transformative technologies such as machine learning, AI, natural language processing, IoT, and cloud-based data sources.
Here are a few predictions for what’s to come for the rapidly evolving data analytics field:
- Explainable AI: This type of AI can explain the benefits and drawbacks to a given model, how it is expected to perform in a given situation, and the potential for bias. This tool allows organizations to pinpoint instances in which decisions are based on bad information, and can provide an understanding of the path that was taken by a system in order for it to arrive at a specific decision.
- Continuous Intelligence (CI): This new technology incorporates real-time analytics into business operations and data processing. It evaluates new information against historical patterns to recommend actions. The real-time insights help with strategic planning initiatives.
- Machine learning: One of the most powerful forms of machine learning is deep learning, which helps an individual or team create a neural network, or complex mathematical structure that’s built on massive data stores. It is able to learn from a data structure, which allows it to detect anomalies and offer predictions.
- IoT: Internet of Things (IoT) data analytics enables users to analyze large volumes of data that are generated by connected devices. It affords organizations many benefits such as engaging additional customers, empowering employees, and optimizing operations.
- Data visualizations: Traditional dashboards are being replaced with self-service business intelligence tools, which have improved capabilities that help end-users create stories from data. This movement of transforming dense reports into engaging visuals helps viewers to focus on outcomes, thus impacting product sales and revenue. The future of data visualization is likely to incorporate more graphs, charts, and heatmaps, all of which can help to frame insights in a manner that can connect with the audience’s emotions.
- Augmented analytics: This process involves automating insights using machine learning and natural language processing. It provides solutions that can help organizations handle complex datasets at scale, provide more universal access to insights, and engage workers at all levels with the data.
Looking back on the past seventy-plus years of how humans have worked with data, it’s evident that the field of data analytics is in a state of perpetual transformation. New advances with AI and machine learning, among other cutting-edge technologies, continue to propel this field forward, into the future.
Hands-On Data Analytics Classes
Do you want to learn more about Data Analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.
In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $27,500. Students can study from the comfort of their own home or office space and still receive industry-relevant data analytics training.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.