The Future of Data Analytics: 10 Trends to Watch in 2023
Data analytics is a field that’s constantly evolving, so staying up to date is essential if you want to succeed. In this article, we’ll cover a few trends in data analytics that will likely influence the industry over the next decade.
We’ll be looking at what’s next for big data technology, how it will affect business practices, and how it can benefit business practices.
To do this, we will look at some of the biggest trends that have emerged in recent years and see where they’re headed.
1. Artificial Intelligence
Data analytics is highly influenced by artificial intelligence (AI) and is one of the most talked-about trends in data analytics today. It’s also one of the most important: If you want to understand what’s happening with your company, your industry, or any other business sector, AI can help you do it.
AI can analyze information from many sources and then predict what that data means for you and your business.
It’s not just about predicting sales numbers or consumer trends; AI can help you understand how to use your data to make decisions that will improve your bottom line, reduce costs, and increase revenue.
2. Edge Computing
Edge computing is the new buzzword of the moment. The term refers to handling data at the edge of a network rather than in a centralized data center. This can happen at either the source (the first point where data enters) or the destination (the last point where data leaves).
The reason for this shift toward edge computing is simple: it’s faster and more reliable than cloud-based solutions. It’s also cheaper since there’s less reliance on expensive servers and storage space.
Several companies are jumping into edge computing, such as Amazon Web Services (AWS), Microsoft Azure, IBM Cloud, and Google Cloud Platform.
3. Augmented Analytics
Augmented analytics is a new method of analyzing data that combines artificial intelligence and human intelligence to create a more accurate picture of your business and the world around you. When augmented analytics is used with machine learning, it can produce even more precise results than alone.
Using augmented analytics, businesses will be able to find patterns in their data that they wouldn’t have seen before — and those patterns could lead them to new insights about their customers or markets that they didn’t even know existed until now.
4. Data Democratization
The number of companies that can use data analytics to transform their business is growing, and so are the number of people who can use it. Data democratization is the trend toward making more data available to more people to help them make more informed decisions. As a result, companies are beginning to release more data than ever, opening up new opportunities for existing employees and new hires.
Data democratization is also happening at the personal level, with individuals accessing more and better data about themselves than ever before. So whether you’re looking for a job or trying to understand yourself better, you can find plenty of online tools to help you get there.
5. Natural Language Processing
One of the most exciting areas of data analytics is natural language processing or NLP. NLP is the ability of computers to understand human speech and language. It’s not just about transcribing the text into another format — it’s also about understanding what words mean in context and even inferring meaning from them.
This is an area that will continue to grow as technology advances, but there are already some great applications that are making use of NLP today. For example, Google Assistant can understand user queries and give them relevant answers without being explicitly trained for a single person or question type. The same goes for Amazon Alexa, which can answer questions about the weather or sports scores using natural language processing technology rather than having to be programmed explicitly with all possible answers beforehand.
6. Blockchain Technology
Blockchain technology has been in the news a lot lately. It’s a decentralized, digital ledger that records transactions between parties. The technology enables secure online transactions and storing data, but it can also be used to track assets, identify digital currencies, and make payments. Blockchain technology has many advantages over traditional databases: it is more secure because the data is distributed across multiple computers; it is more efficient because it eliminates intermediaries; it takes less time to complete transactions, and it is easier for everyone involved to see what’s happening with their information at all times.
Blockchain technology isn’t just for cryptocurrencies anymore! Companies are using blockchain technology in industries ranging from healthcare to food safety, manufacturing to supply chain management, and even music streaming services like Spotify (which recently partnered with Mediachain Labs).
7. Data Analytics Automation
In the past, data analytics was a time-consuming and labor-intensive process. While it’s still essential to have a human overseeing the process, technology has enabled humans to automate parts of the analysis. Therefore, you will likely see more data scientists using machine learning algorithms and automated processes to help them make sense of your data.
You’ll also see an increase in the automation of the reporting process. For example, instead of creating custom reports for each client or company, you’ll be able to use templates that allow you to save time by automating some aspects of the reporting process.
8. IoT and Analytics
The Internet of Things (IoT) consists of a network of connected electronic devices. This IoT network can include intelligent refrigerators, cars, and fitness trackers. The data collected from these connected devices can be used for predictive analytics to make decisions about future behavior.
These IoT devices constantly collect data from the surrounding environment or their users. For example, a thermostat might gather information about room temperature and humidity patterns over time. A fitness tracker might collect information about your walking patterns over time to determine if you are trying to lose or maintain your current weight. A smart car might gather information about how many miles you drive each day and how much fuel you use each week to determine if it needs maintenance before its next trip. All of this data can be combined with other datasets, such as weather reports or traffic patterns, so that predictive models can be created based on what we already know will happen in the future based on past events.
9. Cloud-based Data Analytics
The cloud is the future of self-service data analytics. As the adoption of cloud computing increases, it is apparent that cloud-based solutions will be the most popular for users and administrators.
Cloud-based self-service data analytics provide many benefits to their users. First, they are more cost-effective than on-premises solutions, as they do not require expensive hardware or software licenses. Cloud providers also offer a wide range of tools that allow users to create their dashboards, reports, and visualizations without any coding experience required.
Cloud providers also provide clients access to multiple applications that can be integrated into one platform, which means they can manage all aspects of their business from one place. For example, Dropbox has partnered with Tableau Software to provide its clients with an enhanced version of Tableau’s analytics tool that offers more features than the standard version (such as data visualization). This allows companies who have already invested in Tableau software to use Dropbox’s features without spending additional money on other software licenses.
10. Predictive Analytics
Predictive analytics is the art of using data to make predictions, and it’s growing more common in all industries.
Predictive analytics has been around since the 1950s when computer scientist Arthur Samuel first began developing algorithms to analyze past data and then predict future outcomes based on those analyses. In recent years, though, we’ve seen an explosion in the uses of predictive analytics — from predicting customer behavior to predicting crime patterns for law enforcement agencies.
The reason for this explosion? Machine learning! It is a form of predictive analytics that is widely used. It uses algorithms to “learn” from past data and then extrapolate lessons from that data to predict future events or outcomes based on what happened before them.
Machine learning is a powerful tool because it allows us to use computers instead of humans to do massive amounts of analysis quickly and accurately — something humans can’t do without lots of help!
Conclusion
In conclusion, we’ve examined data analytics’s past, present, and future. We’ve seen how far the field has come since its inception, how quickly it’s growing, and what’s to come.
There is no doubt that data analytics will continue to be a hot topic in the years to come. With more and more businesses using data analytics to better understand their customers and their operations, it’s no wonder why this field will only continue to grow in popularity.
