Data Surge and Big Data Trends

How data has harnessed Cloud-based Platforms to Surge Growth

Digital data is growing at an explosive pace, thanks to larger, faster, and more affordable storage solutions. Businesses have harnessed cloud-based platforms to collect massive amounts of data since Google and Facebook began doing so in the early 2000s. And data generation can only grow even more enormously as the world’s population gains internet access. Global data is predicted to exceed 180 zettabytes by 2025, up from 64.2 zettabytes in 2020. This figure results from the daily creation of 402.74 million terabytes or 403 trillion megabytes of various sorts of data—generated, collected, and copied.

Source: https://www.statista.com/statistics/871513/worldwide-data-created/

Rapid data growth has also forever changed the way companies manage data. Formerly just a byproduct of business operations, these diverse, high-volume, and growing datasets—called big data—are crucial for solving problems and decision-making.

Below is a timeline of data management tool milestones from the 1990s to the present listed by management consultant and AI readiness coach Achim Lelle.

Source: https://www.linkedin.com/pulse/charting-data-landscape-tale-technological-triumphs-trials-lelle-2lxse

So where do we go from here? This article will discuss the trends shaping the data landscape.

6 Big Data Trends for 2024 and Beyond

Speed, scalability, governance, and cost-effectiveness contribute to relevant, actionable, and impactful data insights.

Source: https://www.atscale.com/blog/defining-the-modern-data-landscape/

Here are six ways in which these four factors will cause big data to evolve in the near and long term:

1.     IoT Integration with Big Data and IoT Network Growth

The Internet of Things (IoT) refers to the network of physical devices (computers, home appliances, and sophisticated monitoring machines at an establishment or location) connected via the internet. They can collect, receive, and send data through the software and sensors they contain.

A Statista forecast shows the number of IoT devices worldwide growing to 32.1 billion in 2030, more than double the 15.9 billion recorded in 2023.

Source: https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/

Big data tools can process vast raw information streams to extract real-time patterns and trends about machine performance, user behaviour, and environmental conditions. These insights can then help companies improve operational efficiency or services and predict trends. As a result:

• Manufacturers can predict equipment maintenance needs.
• Hospitals can provide better patient care and detect diseases early.
• Retailers can personalise the customer experience.
• Farmers can enhance crop yields and reduce waste.
• Cities can enhance traffic management, public safety, and energy use.

According to IoT Analytics, IoT continues to be among the corporate investment priorities, ranking second or third behind AI. The top three technologies making up almost 80% of all 2023 IoT connections were Wi-Fi, Bluetooth, and cellular IoT (2G to 5G, NB-IoT, and LTE-M). Meanwhile, 51% of enterprises involved in IoT Analytics’ Summer 2024 report said they plan to raise their IoT budgets in 2024. The increase is up to 10%+ for the 22% of the companies with such plans.

IoT is predicted to be worth $153.2 billion by 2029—up from $64.8 billion in 2024—with an 18.8% CAGR.

2.     Industry-Specific, AI-Driven Insights in Real Time

While big data analysis tools address specific industry needs, all sectors benefit from improved decision-making, customer experience, operations, and budgeting.

Source: https://blog.emb.global/big-data-analysis-tools/

Meanwhile, here are some specific ways big data and its analysis impact industries.

Sources: https://bytehouse.cloud/blog/relationship-bigdata-and-iot,https://datafloq.com/read/the-future-of-big-data-trends-and-predictions-in-2023/, https://blog.emb.global/big-data-analysis-tools/, https://eyer.ai/blog/the-use-cases-for-anomaly-detection/, https://www.simplilearn.com/tutorials/big-data-tutorial/big-data-applications

3.     Diversified Data Storage Options

Data silos are a challenge faced by 60.9% of organisations. As an industry response, two models have emerged to break them down and improve storage and access—the centralised data fabric architecture and the decentralised data mesh. However, the two frameworks aren’t mutually exclusive. Many companies typically use a hybrid model to leverage each system’s strengths.

The data fabric approach integrates data from diverse sources—databases, cloud services, and data lakes—and makes it accessible from one place. A centralised platform simplifies compliance and security. However, data operations can be rigid and block any cultural disruption.

Meanwhile, a data mesh infrastructure treats data as a product owned and managed by individual business departments or “domains” that generate and consume it. The distributed nature of data ownership allows domains to build products tailored to their needs and make decisions with more agility. However, strong coordination is necessary to ensure interoperability and consistency.

Inspired by: https://dualitytech.com/blog/data-mesh-vs-data-fabric/, https://www.precisely.com/blog/datagovernance/unraveling-the-threads-data-fabric-vs-data-mesh-for-modern-enterprises, https://shelf.io/blog/data-mesh-and-data-fabric/

Edge computing has been described as a mesh network of “micro-data centres” that processes and stores data locally or within a network before sending bundled relevant data to the cloud. This setup is ideal when data is physically close to the users who generate and use it. Internet-enabled or IoT devices are edge computing tools, which can lower network latency or duration between data generation and processing. Healthcare and retail are prime candidates for this type of computing because real-time data processing is crucial to their operations.

4.     Quantum Computing

Regular computing uses a binary system, wherein each bit is either a zero or a one. Meanwhile, quantum computing uses quantum bits or “qubits,” which can be both numbers simultaneously. The qubits’ ability to exist in multiple states simultaneously allows them to analyse or perform highly complex calculations involving large datasets at unprecedented speeds.

Such capability has applications in medicinal drug development, simulating the interaction of molecules. Because quantum computing can crack regular codes, it can help efforts to strengthen encryption for improved data security. Moreover, quantum-enhanced machine learning (ML) systems can develop models from analytics to more accurately predict stock market trends, disease outbreaks, and weather patterns. Google, IBM, and Cambridge have begun quantum computing projects to power ML models for real-world applications.

According to QuEra Computing, the majority of its 900+ poll respondents believe the top two drivers of quantum computing’s ROI are its ability to solve previously unsolvable problems and do it faster than classical computing. However, responses were varied based on segments.

Source: https://cdn.prod.website-files.com/643b94c382e84463a9e52264/66add72e0b8354a8451d9305_QuEra%20July%202024%20Survey%20Report.pdf

5.     Data Visualisation

Data visualisation has become a key facet of data democratisation or the granting of data analytics access to non-IT/technical staff. Tools that turn data into charts and other visuals make the information easier to understand as they immediately highlight what matters.

Moreover, AI-enabled tools let users ask questions using natural language (e.g., “What are my three top-selling products in the last quarter?”) for more customised results. As a result, marketing people and others without deep technical knowledge can make informed decisions and identify opportunities.

When presenting data to stakeholders, developing stories around statistics becomes more convenient and appealing through customisable charts, pre-designed templates, or interactive maps. Software can now display these figures in 3D using VR or AR for a more immersive experience.

Also, interactive dashboards with streaming or real-time visualisations may soon become the norm. These tools allow you to explore data in more detail and enable you to perform other functions while viewing graphs. For instance, you can sell a particular stock when it hits a price threshold.

Sources: https://www.ingentis.com/blog/trends-data-visualization/, https://www.kellton.com/kellton-tech-blog/top-emerging-data-visualization-trends

6.     Data Stewardship

Stronger data governance overshadows AI when it comes to long-term business viability moves. Around a third of survey respondents in Immuta’s 2024 State of Data Security Report said that security controls top their organisational initiatives. Data architecture modernisation (22%) and AI integration (20%) came in second and third. Amid the excitement about how AI can improve operations, including data privacy, 56% of data professionals fear that AI prompts used by employees can inadvertently expose sensitive company information.

Moreover, companies continue to come under attack by cyber threats. In 2024, 60% of large companies, 48% of mid-sized firms, and 12% of small businesses experienced cyberattack-related data loss.

Source: https://datahealthcheck.databarracks.com/2024/

At the same time, data governance is necessary to build trust. 73% of UK participants in a survey believe companies collect too much personal or financial data. The same percentage wants stronger protection of customer data once obtained. Britons also demand more transparency and regulation amid fears about unrestricted use of AI with their data.

Trust can make consumers more willing to share data, deepening data pools. However, they’re calling for disclosure over who their data is shared with and vetting of data security providers (77%).

Data use reporting has also become necessary to comply with regulatory frameworks (such as the General Data Protection Regulation in Europe and the California Consumer Privacy Act in the US) and support policy-making (such as the collaboration between the UK’s AI Council and various industries for the national AI strategy). Starting in 2024, the EU requires large and listed companies to report on the environmental and social impact of their activities as part of its sustainability directive.

6 Steps for Adapting to Emerging Trends

Sources: https://www.trinetix.com/insights/building-a-resilient-data-management-strategy-key-steps-and-best-practices, https://www.dataversity.net/data-strategy-trends-in-2024/, https://jake-jorgovan.com/blog/emerging-trends-in-big-data-analytics-what-businesses-need-to-know-for-the-future, https://www.avenga.com/magazine/trends-and-future-forecasts-in-big-data/, https://intelliarts.com/blog/building-big-data-strategy/

With executives under pressure to swiftly adapt to marketplace trends and show tangible impacts of their data planning, having a data strategy is vital to staying competitive and reaping the benefits of big data technology.

Here are six steps to help you achieve such a strategy:

1.     Review your existing tech stack and big data sources.

Sit down with your IT team and assess and document your existing systems and capacities. Identify your big data sources—internal and external ones, as well as web and app-based analytics. Your future big data service provider or development team will need this documentation to assess your current situation.

2.     Set your business objectives.

Know exactly what you want to achieve with your data. In defining your data processing needs, consider:

• The nature of the data you handle
• The volume of data you must process (Is it consistent or subject to seasonal fluctuations?)
• The complexity of your data processing tasks (Do they involve analysis, integration, and transformation?)

3.     Assess and update your data security measures.

Revisit your data governance policies to ensure they comply with industry standards and relevant regulations: access controls, data encryption, proactive monitoring and detection, and backup and recovery. Proper documentation and audit trails should be SOP. At the same time, data protection protocols must promote honesty and accountability in data storage, collection, and use.

Nearly a third of firms worldwide have a chief data officer (CDO), whose top three roles include data governance. In the UK, 57% of businesses with revenues exceeding $50 billion have CDOs.

Source: https://www.statista.com/statistics/1362060/cdo-primary-responsibility-worldwide/

4.     Create a data strategy roadmap.

This roadmap describes how your organisation will access, manage, and store data. It should provide a situational analysis upon which your business plots its data goals. Then it should discuss your tactical plans, timeline for implementing these measures, and monitoring/evaluation process.

5.     Budget for new investments.

Investments should cover both technology and people. Equip your workforce with the skills to be data literate and proficient with the tools to manage and interpret data.

Once you’ve determined the level of support you’ll require from a service provider—whether it’s a data centre or data analytics agency—search, evaluate, and select based on their:

• Data specialisation and expertise
• Track record and reputation
• Data security and compliance
• Tools (check compatibility with your tech stack)
• Scalability
• Communication and collaborative approach

6.     Schedule quality checks/results monitoring.

Conduct regular reviews to check how you’re getting value from your big data solutions. Evaluate the system or software based on performance efficiency, reliability, usability, and security.

Big Data Leadership Starts with a Forward-Looking Strategy

As technologies handling big data will continue to evolve, businesses should anticipate disruptions and keep pace with innovations to meet stakeholder demands. Staying informed of trends—like the points discussed above—can prepare you and your team to navigate the future with a data-driven mindset.