Over the years, data has morphed into a transformative force that significantly shapes day-to-day business decisions and processes. As more business owners and leaders discover the tremendous value lying hidden in their data, we are likely to witness the emergence of more advanced data analytics techniques and solutions. This article covers the top 8 trends that will shape the outlook of data analytics in 2024 and beyond.
How Much Data Is Created Every Day in 2024?
With the recent accelerated growth of the Internet of Things, increased reliance on search engines for purchasing decisions, and the tremendous rise in the global Internet penetration rate, the volume of data created daily has been on a consistent upward trajectory. According to the latest estimates, the amount of data generated, captured, copied, and consumed daily will rise to 147 zettabytes in 2024, up from 120 zettabytes last year.
Source: Statista
Here’s where the problem comes in — the more data generated, the more challenging it becomes to collect, store, sort, and analyze. To survive in this Big Data era, you must continuously monitor data analytics trends and improve your business intelligence strategies accordingly.
The Top 8 Data Analytics Trends to Watch Out for in 2024
As organizations look for better ways to glean and govern their data, below are 8 trends that will most likely dominate the data analytics scene in 2024:
- The rise of artificial intelligence in data analytics
The synergy of AI and data analytics isn’t just a buzzword. Instead, it’s a transformative strategy that helps organizations analyze large datasets, draw meaningful insights in real-time, predict future trends, and automate decision-making processes. Currently, about 21% of employers rely on AI for data analysis. With AI’s market share estimated to increase at a compound annual growth rate (CAGR) of 37.3% until 2030, you can expect more businesses to integrate AI (especially Machine Learning) into their data analytics systems.
Unlike traditional analytics that require vast IT teams to comb through extensive datasets, theorize and test potential insights, and generate manual reports on their findings, AI/ML models can help you automate your entire data analytics process. They can track data, identify trends and anomalies, and generate meaningful insights without human input. Besides saving cost and time, this automation enhances accuracy by eliminating potential human error.
Does this sound like something your business can benefit from? Of course, yes. That’s why over 80% of executives, especially in the retail and consumer industry, are seriously considering implementing AI by 2025.
- Increased adoption of self-service business intelligence (self-service BI)
Source: Pixabay
Initially, organizations hired dedicated BI and IT specialists to create and maintain data warehouses, develop BI applications, run queries, and generate simple dashboards and reports for other non-technical staff. This time-consuming approach typically involves a lot of bureaucracy — when users make analytical queries, the tech team must first initiate a requirements-gathering process, obtain approval for the project, extract and prepare the data from source systems, crate queries, and design dashboards before generating a report. Sometimes, this process may take weeks, making it impossible for users to leverage data as soon as it’s generated.
Comparatively, self-service business intelligence allows executives and other staff members to initiate queries and generate dashboards on their own. It involves creating smaller data marts to hold subsets of business data for specific departments and deploying intuitive analytics software with easy-to-navigate user interfaces. This approach promotes faster data analysis and decision-making and creates a single source of truth by ensuring every business unit draws its insights from a vetted, central source. With 62% of business executives saying self-service BI is an essential investment, we expect more organizations to adopt this model in 2024.
- Edge computing and real-time stream analytics to gain more traction
Traditional data analytics involves first extracting data from different source systems and importing it into in-house warehouses or data lakes. However, because of the proliferation of smart devices and the tremendous surge in the amount of data generated, it’s virtually impossible to rely on in-house data storage systems. As a result, several organizations have transitioned to cloud storage. Unfortunately, even cloud storage systems do not have the full capacity to handle the continual influx of business data. Most of them are prone to bandwidth limitations and network disruptions that can derail your data analysis processes.
Edge computing comes in as a perfect solution to these challenges. It enables businesses to conduct real-time data analysis at the source, eliminating latency issues and preventing potential data corruption that may occur during transit. Also, it can help derive insights faster and take advantage of emerging market opportunities ahead of your competitors.
According to a recent IDC study, the global edge-computing budget will reach an all-time high of $208 billion by the end of 2024, representing a 131% increase from last year. Similar research by Gartner shows that over 50% of corporate data will be generated and processed in edge computing environments (outside conventional in-house and cloud storage facilities) by 2025. Even the US Postal Service has already deployed edge computing to monitor parcel data and track missing packages in real time.
- The growing importance of data-as-a-service (DaaS)
Another trend that will likely dominate the data analytics scene is data-as-a-service (DaaS). And reasonably so — the more data we generate, the more challenging it’s becoming for organizations to collect, store, sort, and analyze all the data they need to gain business insights. Unless you have the same capacity as tech giants like Google or Facebook, you can barely access 1% of the global data. That’s where DaaS comes in. By outsourcing your data analytics to DaaS companies, you can access all the data and insights you need at a reasonable fee — saving you time and effort.
One of the major players in the DaaS industry — Snowflake — is already making waves. While the company was initially only popular for its data warehousing services, it recently started offering DaaS. Its platform has over 600 active datasets, allowing clients to centrally access and analyze large volumes of data from multiple sources. It also hosts vendors offering DaaS services to different businesses.
DaaS is pretty much like software-as-a-service (SaaS). The only difference is that it’s still a new concept that just started gaining popularity recently. As more businesses embrace cloud-first computing, it will definitely gain more traction. According to a recent study by Technavio, the DaaS market share will record steady growth for the next couple of years and reach an all-time high of $56.85 billion by 2027.
- More focus on data democratization
Several modern-day businesses still use data silos to store isolated datasets for different departments. While this arrangement may seem perfect, especially if your departments operate independently and have varying goals and budgets, it also makes it challenging for different units to share intel and collaborate on data analysis projects. That’s because siloed data typically exist in standalone repositories detached from the entire organization’s storage system — preventing other users from accessing them.
In a world where 80% of business executives believe easy data access is crucial for decision-making, we’ll inevitably see more organizations transitioning to democratizing data systems. Unlike siloing, democratization involves eliminating data gatekeepers and simplifying data stacks to make information available to everyone, regardless of their departments or technical expertise. Doing so enhances agility by enabling all teams to access and analyze data in real-time, ensuring they don’t miss any opportunities. It can also improve collaboration by allowing different business units to share insights seamlessly and align their strategies accordingly. According to the Harvard Business Review, 97% of business leaders believe that democratization can significantly enhance business success.
- Data privacy and security will take center stage
Today’s consumers are very skeptical about how businesses collect, process, and use their data. A 2020 McKinsey survey showed that over 71% of customers would cut ties with an organization for sharing their sensitive data without their knowledge or authorization. Unlike a few years ago, even the younger generations take data privacy seriously — almost 75% of millennials are wary about how social media and tech firms collect and use their sensitive data.
With the increasing data privacy and security concerns, businesses will most likely invest more in data governance and cybersecurity compliance. As a result, they will either hire dedicated cybersecurity and data governance experts or prioritize data analysts with these expertise.
According to Gartner, global cybersecurity spending will increase to $215 billion in 2024, representing a 14.3% rise compared to 2023. As organizations look for better ways to collect and analyze more data, they’ll also be forced to invest more in safeguarding their data systems.
| Segment | 2022 Spending | 2022Growth (%) | 2023 Spending | 2023Growth (%) | 2024 Spending | 2024 Growth (%) |
| Application Security | 5,047.6 | 10.9 | 5,765.2 | 14.2 | 6,670.3 | 15.7 |
| Cloud Security | 4,487.4 | 24.0 | 5,616.7 | 25.2 | 7,002.6 | 24.7 |
| Data Privacy | 1,129.2 | 9.9 | 1,338.7 | 18.5 | 1,667.3 | 24.6 |
| Data Security | 3,072.9 | 21.4 | 3,692.1 | 20.1 | 4,333.3 | 17.4 |
| Identity Access Management | 13,944.1 | 13.6 | 16,169.1 | 16.0 | 18,556.5 | 14.8 |
| Infrastructure Protection | 24,089.0 | 19.9 | 28,359.6 | 17.7 | 33,319.6 | 17.5 |
| Integrated Risk Management | 5,157.3 | 9.6 | 5,687.1 | 10.3 | 6,277.7 | 10.4 |
| Network Security Equipment | 18,932.5 | 11.9 | 21,383.6 | 12.9 | 24,360.1 | 13.9 |
| Security Services | 73,394.7 | 3.9 | 80,835.7 | 10.1 | 89,996.7 | 11.3 |
| Consumer Security Software | 7,443.4 | 2.9 | 7,901.7 | 6.2 | 8,406.7 | 6.4 |
| Others | 8,029.8 | 50.1 | 11,365.4 | 41.5 | 14,362.8 | 26.4 |
| Total | 164,728.0 | 10.6 | 188,114.8 | 14.2 | 214,953.7 | 14.3 |
- Improved data efficiency via DataOps
DataOps is another trend that’s gaining popularity because of the inability of traditional data management techniques to cope with the recent explosion of the amount and inherent value of data. Like DevOps, which derives its name from, it involves setting up agile and iterative structures and protocols to ensure every user accesses high-quality data pipelines and analytics as efficiently and reliably as possible. It combines technology, cultural philosophies, and business processes to automate data workflows and reduce the time teams take to extract data and insights from source systems.
In today’s fast-paced business world, DataOps comes in handy to help organizations meet their need for speed. Therefore, it’s only natural that it will move from being merely an industry buzzword to an actionable strategy businesses implement to gain a competitive advantage. As shown in the KBV Research report above, its market share will rise to almost 2.1 billion this year and continue on this trajectory for the better part of this decade.
- Continued adoption of software engineering mindsets in data analysis
Several data analysts have already adopted software development best practices to enable them to deliver data products faster. For example, agile development principles are now widely used in data analysis projects to track continuous feedback, ensure iterative deliveries, and promote incremental improvements. Similarly, several analysts currently use CI/CD pipelines to automate data testing and validation. As the line between software engineering and data analysis gets thinner by the day, businesses will inevitably prioritize analysts with basic coding and software engineering expertise.
Get High-Quality Data & Analytics Engineers From LATAM
As you must have noticed, one common factor cutting across most of the above trends is that they’re designed to address the need for speed. Coincidentally, that’s precisely what nearshoring data and analytics engineers from Latin America can help you achieve.
Unlike popular offshore destinations like Eastern Europe and Ukraine, LATAM enjoys minimal time difference with the rest of the US and Canada. As a result, hiring data specialists from this region allows you to collaborate with them in real time. Also, because of the geographical proximity, LATAM shares several cultural similarities with the surrounding areas, so you don’t have to labor explaining your work ethic and expectations to its data engineers. The best part is that most of the region’s population speaks English as either the first or second language, eliminating the language barrier issue often synonymous with offshore teams.
Latin America offers more than just proximity to Canada and the rest of the US. It also prides itself in a deep bench of IT experts who’ve honed their skills in the region’s tech unicorns and global firms like Google and Microsoft that have established R&D offices here. Above all, it has a long history of exporting tech talent to Canada and the surrounding areas, with Mexico already ranking tech as its main export.
Stay Ahead By Hiring Competent Data & Analytics Engineers Through DevEngine
Source: Pixabay
Data is at the core of every business decision and process. Therefore, you cannot afford to have incompetent engineers behind your data systems. Let DevEngine help you get the best data and analytics engineers from LATAM hassle-free.
Why should you work with us?
- We assess each client individually to understand their unique needs before beginning the hiring process
- All our specialists are pre-vetted to ensure they have the requisite educational qualifications and industry experiences
- We’ve been around since 2019, so we can confidently say we are experts at what we do
- We offer transparent upfront pricing to ensure you know what you’re paying for before we begin
- We serve a limited number of clients at a time to give each of them personalized attention
- We provide more than just hiring — we can also handle onboarding and staff management
As we transition into the cloud-first computing era, the need for speed in business data analytics will increase tremendously. In response, the industry will embrace and abandon several technologies and techniques. If you want to stay ahead of the curve, it’s imperative to monitor these trends and adjust accordingly. Above all, it’s crucial to have competent data and analytics engineers who are current with these changes and are able to embrace emerging patterns as they come.
If you have any questions or need assistance finding data specialists in LATAM, do not hesitate to contact DevEngine. Help is just One Chat Away!