14 Data Analytics Trends for 2024: What You Can’t Ignore
In SaaS, the top data analytics trends can either be a revolution or just fluff.
So, what are the trends in the data analytics landscape that are actually important for product management?
In this article, we’ll explore the data analytics trends that are not just novel or talked about a lot but those that are changing the way we see and utilize data.
What is data analytics for SaaS?
Data analytics is about collecting and organizing data to transform it into actionable insights, focusing on understanding customer behavior, enhancing product features, and driving strategic decisions. In SaaS, where user experience and service personalization are essential, data analytics helps identify user engagement patterns and enables businesses to make informed decisions about product improvements and market positioning.
Plus, analytics allows companies to unlock new growth opportunities, enhance user satisfaction, and ultimately, increase revenue.
Product analysis with Userpilot.
Top 14 data analytics trends
As we mentioned, analytics is more than an advantage, it is a necessity. And you need to focus on what matters if you want to make the most out of it.
As we go through these top data analytics trends, you’ll notice that these are not novel ideas that you’ve never heard of. Instead, they’re mostly emerging technologies that are actively changing the way we perform analytics and that you should jump into before your competition does.
That said, let’s explore each of these data and analytics trends to understand how they can be leveraged in your company.
1. Smarter analytics with AI
We’re all aware of the rapid development of artificial intelligence, and it’s no surprise that the integration of AI in data analytics is making the analysis process faster, more scalable, and cost-effective.
So when you learn how to use AI in product management, you’ll inevitably achieve more accurate results from your data and analytics efforts.
You can leverage AI to parse vast datasets, predict user behavior, and personalize content at scale. Which, in turn, allows you to understand user behavior and optimize product features so they meet customer needs—at a much faster pace.
2. Natural language processing
Natural language processing (NLP) is a technology that allows software to understand and process human language.
That said, NLP is revolutionizing the way you perform customer sentiment analysis. It can help you collect and process massive amounts of qualitative data, such as survey responses, support tickets, or social media comments, and transform it into valuable data insights you can use to follow a more customer-centric strategy.
It can also improve both internal and external communication, make your content easier to discover through search and translate text easily without investing too many resources.
3. Generative AI
While NLP is about processing language, generative AI is what allows you to produce any type of media, such as text, imagery, audio, and even synthetic data.
As you’ve heard about ChatGPT, it can already assist you in creating an actual product strategy if you provide the right data—going as far as creating predictive models, simulating customer interactions, or brainstorming ideas for product development.
So, given the right data, generative AI platforms like ChatGPT can not only impact the way you conduct data analytics but also significantly enhance content marketing strategies, customer service, product prototypes, and much more.
4. Data visualization
Since the data analytics industry will always require human interpretation, advanced data visualization techniques are forever going to be an important trend.
The reason is simple, it can make complex data understandable and actionable (especially with AI). It can generate graphs, real-time data streams, charts, dashboards, and videos and shape them in any way you prefer.
This is huge, as it democratizes data access within organizations, enabling non-technical users to make informed decisions based on complex data sets—without hiring trained professionals.
5. Data sharing
Just like visualization, data democratization makes data easier to access and understand for non-tech users across an organization.
This trend is about making data available to a wider array of decision-makers, enhancing communication and decision-making across departments and external stakeholders. In SaaS, this can mean enabling secure, controlled access to data across departments, facilitating cross-functional projects, and enhancing customer experiences through shared insights.
That said, more than presenting data with visuals, data democratization’s goal is to cultivate a culture where data-driven decision-making is valued and promoted.
6. Cloud computing
Cloud computing lets you outsource data storage so you can export and manage your data, either with private or hybrid clouds.
Not only that, it offers better accessibility and safer data storage due to its centralized nature, making it a cost-efficient and flexible solution for most companies.
That said, this trend is the backbone for scalable, flexible data analytics. It allows you to store and process large volumes of data without significant infrastructure investments, and without compromising on security or accessibility.
7. Edge computing
Edge computing distributes servers across multiple locations, so users are more likely to be physically closer to them.
This way, data can be processed and analyzed right where it’s generated—making it far faster.
With faster data analysis and real-time analytics, there are less network traffic and connectivity costs. This means faster insights, reduced latency, improved performance, and more ROI—making it especially useful for services requiring real-time data analysis, such as IoT devices or mobile apps.
8. Augmented analytics
Augmented analytics uses AI and machine learning to automate data processing tasks such as data preparation, insights generation, and explanations—making data analysis more accessible.
Its goal is to “augment” how users approach and navigate business intelligence, making analytics not only more accessible for non-experts but also more efficient for actual professionals. This is because it allows you to automate time-consuming tasks such as AI deployment or data management and focus on processing the data that matters.
9. Predictive analytics
Predictive analytics, as the name suggests, predicts customer behavior, sales, future revenue, potential risks, and more.
While not always 100% accurate, this trend is crucial for strategic planning. It can provide anticipatory services, tailor customer experiences, and optimize operations to meet future demands more effectively.
Plus, predictive analytics is constantly evolving, becoming even more accurate at forecasting market trends, operational problems, and customer actions.
10. Decision intelligence
Decision intelligence merges data science, social science, and decision theory to create a system that can make data-driven decisions.
In SaaS, this trend has the potential to streamline product development, marketing strategies, and customer service protocols based on a comprehensive analysis of available data.
It works best when combined with human expertise, as it allows you to judge a situation with a wider lens and make decisions that are many steps ahead of the present.
11. Synthetic data
Synthetic data, as the name suggests, is data that’s been artificially generated when real-world data is insufficient or non-existent. And it’s used either to perform validation tests or even train AIs.
That said, depending on the available data, you can either have fully synthetic data (which has no connection to real data whatsoever) or partially synthetic data (which takes real information except for sensitive data).
It represents a revolution in cybersecurity, as it causes fewer problems with data privacy and makes data more available. And in SaaS, this means more opportunities to innovate and improve AI products without compromising user privacy or relying on hard-to-source datasets.
Its impact is so big, that Gartner predicts synthetic data will surpass real data (in AI models) by 2030.
12. Data security and privacy
With increasing data collection and sharing, concerns around data privacy and security are more pressing than ever.
We’re talking about data breaches from big companies, as well as cyber crimes such as identity theft, advanced phishing, and digital extortion.
That said, your company must prioritize strong data governance and adhere to data protection regulations to maintain trust and compliance. This can involve implementing proper safety protocols and damage control methods and hiring specialized cybersecurity services.
13. Data contracts
With more data in our hands than we can work with, data contracts allow different parties to exchange, structure, and process data sets under a formal agreement—ensuring proper data integrity and compliance.
As working with data becomes more complex, this trend is particularly relevant for organizations dealing with large data ecosystems—which often require seamless integration, data exchange between multiple parties, and more accountable data management.
14. Data literacy
It doesn’t matter how accessible data science becomes; it won’t serve for much without an experienced and trained staff.
What’s worse, according to research, there’s still a gap of 39% between leaders who think they’ve provided enough data skills and actual employees who confirm it to be true—the need for data literacy is real.
This can involve upskilling all employees (not just data specialists) to learn how to process and interpret data to make their job more effective, as well as investing in the right tools so everyone has easy access to the data.
For most companies, this trend is essential to drive more innovation, make better decisions, and cultivate a culture of continuous improvement.
Data analytics with Userpilot
Although you can code your way into these trends, using a specialized tool is always going to cost less money and time.
That’s why we recommend Userpilot—despite our obvious bias—since it’s a cost-efficient product management tool that can help you collect and interpret user data without coding.
- Get a deep user experience understanding with analytics charts such as funnels (to spot friction points), trends (to understand what brings value across different plans), and paths (to draw the product journey of your business users).
- Send a great variety of in-app surveys such as CSAT, CES, and NPS surveys. Use advanced survey analytics to tag and filter user responses based on recurrent themes and keywords and save time analyzing responses.
- Use feature-tagging and event-tracking to analyze user behavior and identify friction points.
- Segment your users based on their data and personalize the customer experience.
- Analyze feature usage with feature heatmaps to understand product adoption and which areas of your product bring value to your users.
- A/B test different in-app flows to see what kind of content generates more engagement.
- Create in-app messages with generative AI and NLP so you can communicate with users in a more personalized, engaging way in order to improve the user experience.
Conclusion
These data analytics trends are shaping the way we work with data.
Whether through smarter AI-driven analytics, advanced data visualization, or leveraging generative AI for better communication, integrating technologies in ways that add real value for users will always provide better business outcomes.
So if you’re looking to leverage data analysis for product management, why not book a Userpilot demo to see how you can start making data-driven decisions?
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