The following are the top big data trends for 2023.
1. Big Data is moving to the mainstream
Along with artificial intelligence, big data has been one of the most talked-about buzzwords in IT for over 10 years. Now, that hype is turning into results as more and more businesses start to see real-world benefits. As Daniel Newman points out, big data is now starting to drive real business decisions, rather than just assessing past performance.
Tom Davenport from Deloitte says that big data is now starting to merge with traditional business analytics. It is now an essential part of many companies, and it’s getting harder to distinguish it from other business analytics.
Also, Google searches for “Big Data” have actually declined since 2010. This shows that people are starting to understand what the term means, and don’t need to look up its meaning anymore.
Data analytics is being used in many industries, from helping to diagnose patients in healthcare, to setting insurance premiums in financial services. It’s no longer just the big players and risk-takers that are taking big data seriously. Everyone’s getting in on the action.
Things like Cloud technologies and access to powerful analytics APIs from the likes of Google and Amazon are really making big data easy. If you’re not taking advantage already, you need to.
2. Leaders are asking for a reality check
Now that executives are starting to realize the benefits that big data and analytics can bring their companies, they are also starting to become aware of the potential pitfalls. In particular, they are looking at problems like:
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- Overinvesting in projects that don’t have clear goals
- Overestimating the insights Big Data can deliver
- Relying too heavily on specific data sources
- Not having the right data for the job
- Analyzing poor-quality data
- Thinking that Big Data can be a solution to everything
Many companies have been burned by one of these problems in the past. As this technology moves into the mainstream, a lot of CEOs, CIOs, and data scientists will need to make sure their companies, budgets, and expectations are in touch with reality. Decision-makers are now asking exactly where the returns are going to come from big projects, and when.
3. Moving to the cloud
Companies all over the world are shifting their IT out of their data centers, and into the cloud. It makes perfect sense that big data and analytics would follow this trend.
Companies of all sizes now demand reduced costs, greater flexibility, and less hassle from their IT departments. And that’s exactly what cloud technology delivers. No wonder this is one of the most important big data trends at the moment.
Newer companies are also emerging that are ‘Cloud Native’. They are built from the ground up to take full advantage of cloud technology.
Data projects can scale up and down easily as these companies grow and demand fluctuates, giving them a huge competitive advantage. Even older companies are moving some of their legacy IT systems to the cloud.
However, this is opening up some challenges, especially for larger enterprises. Moving data from legacy in-house data centers to the cloud is costly and tricky.
Also, there is the issue of data privacy when using a public cloud. These enterprises are having to completely rethink the processes they have been doing for decades.
As applications and data move to the cloud, data analytics will inevitably follow.
4. Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) go hand-in-hand with big data. To make really intelligent machines, you need huge amounts of data for them to learn from. Similarly, to understand huge amounts of data, you need the help of intelligent machines.
Machine learning has come a long way in the last few years. Anyone can access powerful ML algorithms and services from vendors such as Amazon, IBM, and Google. Combine this with the scalable infrastructure that the cloud provides, and big data is now very approachable.
Randy Bean explains that “Although many AI technologies have been in existence for several decades, only now are they able to take advantage of datasets of sufficient size to provide meaningful learning and results”.
Breakthroughs in deep learning are helping make sense of the huge amounts of unstructured data – called ‘dark data’ – being collected worldwide.
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Ovum predicts that machine learning will be the major disrupting force for big data in 2023. Predictive analytics, recommendation engines, customer insight and personalization, fraud, and threat detection will all benefit from the convergence of machine learning and the latest big data trends.
5. More creative startups finding ways to make use of data
Not only is data piling up at a rate that is incomprehensible, but there are new types and varieties popping up every day.
New devices, apps, formats, and ‘things’ are all generating data, and nobody can keep up. Figuring out how to actually use all this to improve business decisions requires experimentation, agility, and thinking outside the box. All the things that startups are so great at.
The biggest challenges for these startups are proving to be in getting data analytics to work in real time. This is where many see the real power of this technology.
2023 will see some great new projects underway helping businesses collect, visualize and use big data. Some of the technologies they are using didn’t even exist a couple of years ago.
Check out this list of the 10 Coolest Big Data Startups and you will see just what is in store for 2023 regarding the latest big data trends.
6. Data security is more important than ever
The public is becoming acutely aware of just how much of their data is being collected. Companies that show they don’t take this responsibility seriously will lose the trust of the public. And these days, that can be a death sentence.
Investors know this better than anyone and will be more likely to invest in companies that prioritize privacy and data governance.
Regulators are also getting better at understanding the importance of data. If you’re found liable for breaching any rule or regulations regarding data security, you can expect big fines and penalties too.
The rise of the Internet of Things (IoT) makes this data analytics trend even more important. IoT data breaches are going to cause havoc over the next few years.
7. Big Data-as-a-Service
Businesses are getting used to things “as-a-service”. Some of the most popular right now are:
- Infrastructure as a service
- Platform as a Service
- Software as a Service
- Machine learning as a service
It makes sense too. With these options, you don’t have to hire expensive specialists, buy any equipment, or deal with any of the technical problems that go with those things. You simply figure out what service you need, and pay for it as a subscription.
Data-as-a-service (DaaS), or ‘self-service-big-data’ will tie in very closely with many of the services I mentioned above. Cloud services allow businesses to only access the infrastructure they need when they need it.
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Similarly, DaaS will allow businesses to tap into only the data they want and when they want it. As Daniel Newman puts it: “This essentially eliminates the need for in-house commitment to data and allows businesses to perform with greater agility, because they can seamlessly and effortlessly get the exact data they need”.
This type of self-service analytics will become expected from service providers. They will have to provide great tools that allow clients to access and use data easily, rather than being the middleman.
Companies want insights from data and they want them now – and that’s something that the upcoming big data trends will allow for!
What does this mean for your business?
All these big data trends have a common theme. That theme is that they are affecting all businesses, not just those at the cutting edge.
If you’re surprised by any of the things that I’ve mentioned, you are probably already falling behind. Don’t worry, it’s not too late yet, but it will be soon.
It’s time now that every business starts to look seriously into what services are available, and which ones they can take advantage of.
There are many ways to do that, from researching and implementing them yourself, to hiring dedicated staff, to enlisting the services of a company that can help you get started.
If you need developers or dev teams then contact DevteamSpace via this link and one of our account managers will show you how we can help.
Frequently Asked Questions
Big data trends are movements or shifts that see the utilization of new directions and technology in the field of big data. An example of this was a recent trend that saw big data combined with machine learning to improve search results or the accuracy of chatbots.
Machine learning remains the primary focus of the latest big data developments. For more information, read this article.
K Means clustering, Mean-Shift Clustering, and DBSCAN can be used with big data. Other types of clustering models are not suited to the sheer scale of data that big data involves. Use one of these approaches to analyze big data.