Data trends. Some are fads and pass within a few months, while others stick around for the long haul. But how can your business tell the difference between what data trends are a flash in the pan and which ones are here to stay?
Don’t worry; we’ve got you covered. After analysing numerous trends and technologies in data and analytics, we’ve put together a list of our top data trends for 2023 and beyond.
Technological changes in data management are accelerating the evolution of data analytics, and your business might need to make some fundamental shifts if it wants to keep pace.
These rapidly accelerating advances are helping businesses recognise the value of data and increase data literacy within their teams. The following trends are changing what it means to be “data driven,” and you need to keep a close eye on them if you don’t want to fall behind. Let’s get started. Here are our top data trends for 2023 and beyond.
1. Advancements in data governance
In the coming years, we expect to see a decisive shift from a data governance model of loosely followed guidelines to a form of data governance that ensures organisations make the most of their data and analytics.
For data to truly power the transformation initiatives many businesses strive towards, it must be quickly available, reliable, credible, and relevant. Solid data governance models will ensure that this is the case and help your organisation release the full potential of its data.
Often the issue of data governance is the result of a lack of access to data and insight. Unless everyone, across every team, at every level of the organisation has the opportunity to interact with data in a meaningful way, it’s difficult to recognise the full potential of data governance to create value for the business.
This results in a set of data governance policies that are not widely followed and often only focused on the IT department—rendering them and the data within your organisation ineffective.
Through 2023 and beyond, we will see more organisations combat this issue as a way to save time and create greater value from data. Data processing consumes large chunks of your analytics team’s time, including that of your highly paid data scientists. This limits the actionable insights gained from your data and frustrates your employees.
According to McKinsey’s 2019 Global Data Transformation Survey, 30% of employees’ time at an enterprise level is spent on non-value-added tasks because of poor data quality and availability. That’s a staggering statistic! Combatting this issue will have a huge impact on your organisation’s efficiency and profits, and we expect it to become a higher priority in the future.
2. Big Data in healthcare
The next wave of healthcare innovations will be fuelled by Big Data. Historically, the healthcare sector has faced challenges with health data platforms due to myriad factors, including a lack of patient trust.
Creating health data platforms that patients can trust could help to accelerate the current approach to drug development and revolutionise medical sciences’ understanding of numerous diseases.
Being able to analyse Big Data can help the healthcare sector to transform drug research. Big Data in healthcare could give data researchers the ability to quickly identify patterns and build holistic disease models that make it simpler to develop more specific and scalable drugs that have massive impacts on many people’s well-being.
3. Getting AI to scale
Most organisations recognise that Artificial intelligence (AI) has the potential to completely revolutionise how they work. However, there is a problem in most organisations with implementing AI in a way that can scale.
The challenge with AI-driven projects is that organisations have not figured out how to adapt their current models to meet new technologies.
To capture the full value of AI in 2023 and beyond, businesses must review how they operate. AI needs to be integrated at the core of your business strategy to be successful, you can’t simply plug AI into your current processes and expect it to add value.
As a result of most businesses approaching AI as an afterthought, this reduces the applications that you can put AI into and also makes it much harder and more costly to scale. If AI is not integrated business-wide and with new models, each team will end up reinventing the wheel to get stakeholder buy-in, complete training, and deal with the implementation of data and technology.
4. Machine learning for business
Machine learning has shown huge potential for increasing efficiency in businesses across industries. As we move into 2023, we will see more businesses prioritise machine learning and putting the correct processes in place so that machine learning can work effectively.
Generating real, lasting value from machine learning is more than having access to the latest algorithms. For machine learning to work effectively, a business needs to look at modernising its processes and finding the greatest use cases.
Sadly many businesses get stuck at the first stage. For example, a company might have found a few smaller use cases to test machine learning but then struggled to apply it at scale across their organisation.
In 2023, we predict that we will start to see more businesses take a new approach to implementing machine learning. Rather than finding individual steps to implement machine learning, businesses will design more automated processes from start to finish. This approach will create greater efficiency and scalability.
Creating a data-driven business model for 2023 and beyond
In addition to being aware of the hottest data trends that look set to dominate discussion in 2023, your business should also be thinking about modernising your data analytics capabilities as well.
Businesses looking to disrupt and innovate need a smart, powerful, and equally innovative data analytics solution to help them make those crucial market-leading decisions.
If you’re ready to bring your business to the next level, then why not try Vizlib? Our solutions help businesses to get the most out of their analytics by offering powerful, value-added products for Qlik Sense.
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