Big Data Trends 2024

By TechDogs Editorial Team

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Overview

Do you remember the movie 2002 sci-fi flick Minority Report? Yes, the one starring Tom Cruise as the lead officer of a special police unit. What was so special about his unit, you ask?

Well, they could predict crimes before they happen to prevent them and make the world a safer place! If that sounds incredulous, we’re with you – but we did say it is science fiction!

However, if we were to (hypothetically) make such a system to take inputs for a gazillion data points and accurately predict an outcome, we would need a highly futuristic data processing technology. Wait, don’t we already have that in the form of Big Data?

Yet, with the increase in the pace of data being generated, businesses need to keep up with the latest happening in the world of Big Data. That’s where we come in – read on to find the top 5 Big Data Trends of 2024 and ace the data game!

Read on!

In the vast business realm, data-driven decisions are quickly becoming the only constant. No matter the industry, data is the lifeblood of modern businesses. However, it is in a state of constant evolution. As business professionals navigate this shifting digital landscape, staying ahead of the curve is not just an advantage; it's a necessity. #QuoteUsOnThat

So, where does this journey of “staying ahead” begin? Right here – as we unveil the Top 5 Big Data Trends of 2024 that will redefine the foundations of decision-making and business strategies. Last year, we uncovered Big Data trends such as retail and pharma integration, data governance, Big Data for autonomous driving and actionable insights. So, what’s changed this year in the world of Big Data?

Join us on this voyage where data insights and business trends collide, leading into the data-driven future of Big Data. It's a voyage no business professional should miss!

Trend 1: Businesses Will Boost Big Data Compliance With Data Masking

TechDogs-"Trend 1: Businesses Will Boost Big Data Compliance Strategies With Data Masking"-"A Meme About Data Masking"

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Last year we mentioned how businesses leveraging Big Data need to be more aware of compliance and regulations around data privacy. The trend remains strong this year. However, in 2024, a new technology will come to the rescue of businesses and their Big Data!

The name? Data masking, which refers to a process that modifies data to create pseudonymized datasets. Essentially, within these new datasets, any personal data or PII (personally identifiable information) is replaced with artificial identifiers. This makes it challenging to identify data about individuals in the datasets, which will protect their privacy. By using data masking for Big Data, businesses will enhance the protection of customers and employee data, while also reducing the potential risk in case of data breaches.

“We view data masking as an important additional security layer in protecting that sensitive data, so just imagine all of those copies that are made, applying masking to it. If a hacker were to breach and get into those copies, they’d essentially be stealing fictional data,” says Steve Pomroy, chief technologist for a cyber security software and services leader.

Healthcare business Tesseract Health, for example, has a vast repository of medical records, including sensitive information such as names, addresses, medical conditions, etc. To comply with privacy regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the United States and GDPR in Europe, the healthcare business implements data masking techniques for sensitive data used in Big Data analytics. Hence, the trend of masking Big Data will extend beyond healthcare in 2024!

TechDogs’ Takeaway: Most businesses have acquired massive datasets that may contain private data. Hence, they must invest in data management tools, such as K2View, with compliance qualifications and comprehensive features, including redaction, tokenization, de-identification, pseudonymization and data obfuscation. They must also review the Big Data repository to ensure it is masked according to the business's security and compliance requirements. Most important of all, businesses need to classify Big Data datasets to identify which ones need data masking, based on the sensitivity of the content.

Trend 2: Stream Processing Will Drive Real-time Big Data Insights

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We bet you already know that Big Data constantly streams information from various sources, including IoT devices, industrial sensors, smart home gadgets, smartphones, etc. However, most businesses are unable to process such massive amounts of data quickly enough. In 2024, however, real-time stream processing will make real-time Big Data insights possible!

Stream processing refers to a data management technique that enables real-time analytics, filtering and transformation of Big Data. This enables businesses to process data right away instead of storing it away to be analyzed later in batches. An insightful example is that of BNP Paribas which incorporated stream processing to analyze real-time customer interactions at its ATMs. Based on customer behavior, the ATM offered a low line of credit to specific users, allowing the bank to increase its total loans by 400%!

Back in 2022, Gartner predicted that over 50% of new business intelligence systems would incorporate real-time Big Data analytics, or “continuous intelligence.” CB Insights also predicted that the stream processing market will be valued at $52 billion in 2027, compared to less than $20 billion in 2023. Mike Barlow, an award-winning journalist and tech consultant explains, “It’s about combining and analyzing data so you can take the right action, at the right time, and at the right place.”

As stream processing includes various Big Data formats, including structured, semi-structured and unstructured data, businesses will achieve faster decision-making, modeling and predictive abilities. This will drive the adoption of stream processing for real-time Big Data insights in 2024.

TechDogs’ Takeaway: To leverage stream processing for real-time Big Data insights, it is critical to understand a few prerequisites. First, you must identify a stream processing framework, such as Apache Kafka or Apache Flink, that aligns with your existing data management systems. Next, you need to identify specific business use cases where real-time stream processing can add value, such as fraud detection, personalization, or real-time analytics. Finally, you must ensure data quality and governance to maintain the consistency of incoming data streams, leading to top-notch Big Data insights.

Trend 3: Businesses Will Invest In Data Lakehouse Infrastructure For Big Data

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Most data-driven businesses want to run in-depth analyses of their Big Data. To do this, they invest in data warehouses (a unified repository that stores large amounts of information from multiple organizational sources) or data lakes (a flexible and centralized repository for structured and unstructured data). However, in 2024, businesses will invest in a “data lakehouse” to combine the best features of both data warehouses and data lakes.

Data Lakehouse is a storage architecture that offers “store now, analyze later” capabilities, given its scalable size. This allows businesses to simplify Big Data management, improve data security and speed up data analytics by combining the best of both worlds: the scalability and flexibility of data lakes with the robust management features of data warehouses. An example of this trend is the data cloud provider Snowflake, offering data lakehouse infrastructure to businesses allowing them to optimize Big Data analytics at scale. Snowflake ended 2023 Q3 with a 70% year-over-year product revenue growth and is expected to continue on the same trajectory in 2024.

Bill Schmarzo, chief technology officer at Dell EMC, said in 2019, “We’re going to see company after company who have already jumped into the lake. The data lake is going to be a great enabler. It’s going through its overhyped status right now, but a year from now we’re going to see a lot of different organizations that have implemented the data lake and are running not only their analytics on top of that but, in some cases, have actually moved some of their data warehouse capabilities of that as well.” This precise prediction is what we will see in 2024 – a merger of data lakes and data warehouses!

TechDogs’ Takeaway: While most Big Data businesses have adopted data lakes on some level, data lakehouses can be a game-changer for industries that face regulatory or technical limitations. For instance, heavily regulated industries like healthcare, finance, etc., should adopt an on-premises data lakehouse for improved data management. Moreover, data governance is vital to the success of this storage architecture or it may turn into an unusable “data swamp” over time. Finally, a data lakehouse will store much of the organization’s valuable information, so protecting it through encryption to give only authorized users access is a critical aspect. Happy lakehousing!

Trend 4: AI And ML Will Be Great Enablers For Big Data Automation

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If there’s one emerging trend that’s affected every industry equally, it is undoubtedly AI adoption! In 2024, Artificial Intelligence and Machine Learning will unsurprisingly become more viable for Big Data analytics and predictive capabilities. Organizations will adopt AI/ML solutions to process Big Data and create actionable insights faster and more accurately.

Think of the ride-hailing industry giant, Uber, which utilizes Big Data successfully to the tune of several hundred petabytes of data daily. They achieve this by integrating automated AI/ML solutions with Big Data to help make decisions such as estimating large-scale demand, matching closest drivers with riders, setting fares, etc.

What’s more, over 60% of IT leaders say they’re planning to increase spending on artificial intelligence and machine learning (AI/ML) solutions – and for good reason. AI solutions can automate almost 70% of data processing work and 64% of data collection tasks, which makes the brunt of the work in Big Data. Furthermore, machine learning enables the automated identification of patterns in Big Data to help make accurate predictions and create decision-making algorithms. In a nutshell, AI/ML will soon automate most of the time-consuming tasks in Big Data, leading to improved analytics and predictions.

TechDogs’ Takeaway: We bet you’re already using AI/ML in your day-to-day tasks. However, to use AI/ML to supercharge your Big Data strategies, you must prioritize data quality as accurate, well-structured data is the primary foundation for effective Big Data applications. Secondly, focus on the continuous learning and upskilling of the workforce to enable AI/ML tools to be leveraged across touchpoints. Lastly, consider the scalability, ease of integration, flexibility and cost-effectiveness of each AI/ML tool before adopting them for your Big Data automation strategy.

Trend 5: DataOps Will Make Way For Simpler Big Data Adoption

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Have you come across the term DataOps yet? It refers to a collaborative approach to data management by improving the communication, integration and automation of data flows between data stakeholders in an organization. If not, you’ll be hearing about it a lot more in 2024, as the adoption of DataOps is an emerging trend for businesses dipping their toes into Big Data.

DataOps can streamline the Big Data pipeline – from data collection, preparation to analytics and delivery! This enables organizations to extract actionable insights from Big Data more efficiently and with faster time-to-value. In a DataOps approach, instead of thinking of data in the traditional fashion, where individual teams handle data collection, generation, storage, transformation, processing, analytics and management, organizations make the data lifecycle more streamlined.

For instance, DataOps software provider Zaloni aims to solve challenges with traditional data management approaches with their DataOps platform, Arena, by consolidating enterprise data solutions through transparent data governance at every step of the data supply chain. Zaloni CEO, Susan Cook, validates the DataOps trend by explaining the issue, “With data responsibilities existing across multiple departments, the companies we work with have often created, over time, a decentralized hodgepodge of vendors to ingest, store, compute, catalog, secure, prepare, classify, and analyze data, which results in an uncoordinated data sprawl. This fragmentation impedes their ability to collaborate, control, and consume data quickly and efficiently.” With DataOps, businesses can optimize the Big Data pipeline from a bird’s eye view, leading to more effective use of resources.

TechDogs’ Takeaway: To replace the traditional Big Data approach with DataOps, focus on adopting automation tools that reduce manual intervention, regulate data quality and accelerate data processing. It is also essential to establish a data governance practice that detects anomalies to ensure the data quality remains sustainable for Big Data operations. Most important of all, to truly adopt a DataOps approach, you must foster a collaborative business environment that allows data engineers, analysts, data scientists and other stakeholders to streamline communication and collective decision-making.

Conclusion

In 2024, the Big Data landscape is poised for transformative changes. Perhaps not as transformative as John Anderton’s life in the movie Minority Report – but as the data revolution chugs along, we may someday be able to use Big Data to predict crime before it occurs! #FingersCrossed

Till then, focus on trends such as AI/ML automation, stream processing, data masking and adoption of data masking and DataOps practices. These Big Data trends signal a future where accurate, secure and real-time insights will be the norm. Till then, stay ahead in this dynamic environment and unlock the full potential of your data with these top 5 Big Data trends!

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Tags:

Big Data Data Management Data Masking Data Streaming Data Lakes Data Lakehouse Artificial Intelligence (AI) Machine Learning (ML) DataOps Big Data Automation Data Processing Data Fabric Augmented Data Management

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