What's Next for Data Science and Analytics: Key Trends in 2023

Information is captured every second. If you need to differentiate who the winner competes with today, the differentiator will be data, as data is the new gold in this modern era.

An average person curates 1.7 MB of data every sec. Imagine, there are 5 bn people on the internet today. Do the math, and you will know that a lot of data is created daily! The total data size held by the big four members of the NASDAQ, namely Amazon, Google, Microsoft, and Facebook, is already 1.2 million terabytes of data.

Hence, data is widely spread in our present lives, waiting to be transformed and analyzed into valuable insights. The parallel evolution of storage mediums, technology, and processing power makes it easy to process the sheer mass of data.

Therefore it is more than ever essential to move a business model around data in its key activities, where decisions are made based on what we know is right or wrong rather than "gut feelings," which is the factor that curates gaps between winners and the others. Data assists us in the pandemic, wars, and worldwide economic crisis to bring uncertainty in everyone's life. Through data, we can state what is genuine or fake- isn't it?

#1. Data Democratization

One of the most crucial trends will be the continued empowerment of the workforce – rather than data scientists and data engineers – to put analytics to work. This is giving hike to new forms of augmented working, where applications, tools, and devices push intelligent insights into the hands of everybody to allow them to do their jobs more efficiently and effectively.

In 2023, organizations will appreciate that data is the key to understanding consumers, developing better services and products, and streamlining their internal operations to reduce waste and costs. But, it is becoming increasingly clear that this won't completely transpire until the power to act on data-driven insights is accessible to the frontline, shop floor, and non-tech staff, as well as functions such as finance and marketing.

Some great instances of data democracy in practice involve lawyers utilizing NLP tools to scan pages of docs of case law or retail sales assistants utilizing hand terminals that can access consumer buying history in real-time and recommend products to cross-sell and up-sell. A study by McKinsey has found that organizations that make data approachable to the entire workforce are 40 times more likely to say analytics positively impacts revenue.

#2. Cloud and Data-as-a-Service

We've put these two together because the cloud is the platform that allows data-as-a-service technology to work. Companies can access data sources curated and collected by third parties through cloud services subscription-based billing or on a pay-as-you-go model. This decreases the requirement for organizations to build their proprietary data collection, expense, and storage systems for many apps.

As well as raw data, DaaS organizations offer analytics tools as-a-service. Data accessed through DaaS is generally utilized to augment an organization's proprietary data that it collects and processes to create rich and more valuable insights. It plays a crucial part in the democratization of data mentioned formerly, as it offers businesses to work with data without requiring to maintain and set up specialized and expensive data science operations. According to a survey, by 2023, it is estimated that the value of the market for these services will grow to $10.7 bn.

#3. Real-Time Data Analysis

When delving into data in search of insights, it's apt to know what is going on right now – rather than yesterday, last month, or last week. That is why real-time data is increasingly becoming businesses' most valuable source of information.

Working with real-time data often needs more sophisticated data and analytics infra, which means more expense. Still, the benefit is that we can act on info as it happens. This could include analyzing clickstream data from visitors to the website to determine what promotions and offers to put in front of them or financial services; it could mean triggering transactions as they take place worldwide to watch out for warning signs of fraud. Social media (SM) sites such as - Facebook analyze thousands of gigabytes of data per second for numerous use cases, including preventing the spread of fake news and serving up advertising. Hence, as more companies look to data to provide a competitive edge, those with the most advanced data strategies will progressively look towards the most insightful and up-to-mark data. That's the reason why real-time data and analytics will be businesses' most valuable big data tools in 2023.

#4. More Streamlined Tech Stacks

In the beginning, while data science was still gaining momentum, the tech spearhead of the field was a great mess. Researchers were trying to use almost every language and technology stack to find out what works and what doesn't, and it took a lot of work for newcomers to orient themselves in a way that didn't face the risk of obsoletion. Now, it's a completely different story. Numerous languages like Python and R have emerged as industry leaders, and we are already seeing some full stacks stabilizing in the market and enjoying attention from corporations at all levels.

And that's an excellent chance for those who intended in getting involved in the domain because it offers them much more confidence and security during their nascent stage, which is arguably when people require that kind of support the most.

#5. Big data analytics automation

Automation is one of the driving factors that transform the way data is governed in the world today. Industry veterans are exploring possibilities to automate Big Data analytics to gather and evaluate unstructured data throughout their organizations. Moreover, they are also exploring opportunities to scale Analytics Process Automation (APA) by providing actionable insights and predictive capabilities. Enterprises that leverage big data analytics automation enables them to deliver better results and reduce operating costs.

#6. More Attention on Cleaning up and Maintaining Data Sets

The last decade saw an explosion in collating data and storing it for future analysis. One of the advantages of advanced data analytics is that it can work well on historical data, which has elicited some borderline hoarding behavior in some of the big-shot organizations in the market – talking about the ones that can afford the huge data centers required to store all of that info.

But currently, a new trend has started to come into play. Organizations have begun to realize that much of the data they have been piling up for later analysis could end up essentially useless, at least in its current state. Data collection practices could have been more streamlined and diligent in the beginning, meaning that many companies now possess huge sets that require a lot of sanitization work. Unfortunately, that's still something that needs manual labor to a vast extent – and that's where a lot of attention will fall over the next decade. In a nutshell, data science and analytics are moving towards a much more streamlined situation.

Therefore, the data science and analytics space still has room for future growth. In less than two months, 2023 is at our door, and we'll see the trends coming true.

Organizations should try to be of quick adopters to make a clear difference between the competitors to remain the winner! Book a session to learn more about our data science and analytics offerings at Polestar Solutions.