Have you defined a Data, Cloud, and Artificial Intelligence/Machine Learning (AI/ML) Strategy in 2024?
We are almost a month into the new year 2024 and here is my opinion on some key emerging trends and challenges in 2024 and beyond.
Emerging trends:
Data strategy:
1. Data Labeling Challenges: This will remain a critical hurdle. While automated solutions like Snorkel AI and Label Box are promising, human involvement is still crucial for accuracy and ethical considerations. We might see increased collaboration between organizations to share labeled data sets and best practices.
2. Increased Data Sharing: Adequacy decisions and privacy laws are pushing for smoother data flow across borders. However, concerns about data sovereignty and security will persist. Open-source collaboration on data governance frameworks and standardized security protocols could facilitate safe and responsible data sharing.
3. US and global Data Privacy Regulation: Uncertainty prevails, but state-by-state laws will likely take precedence until a federal law emerges. The onus will be on organizations to adapt to a patchwork of regulations, creating operational complexities. Cloud providers offering compliance-aware solutions could play a significant role.
4. Data Darkness for Advertisers: The trend towards user control over data for personalized advertising will continue. Businesses will need to shift towards more contextual and privacy-preserving targeting strategies, focusing on value proposition and user experience. The rise of alternative revenue models beyond advertising is likely.
5. Data Quality and Governance continues to be center stage: Ensuring the quality, accuracy, and reliability of data remains a significant challenge. Establishing robust data governance frameworks is crucial for maintaining data integrity. AIML is inherently dependent on superior data profiling, cleansing, and preprocessing before analysis and decision-making.
6. Legacy system integration: We are all privy to key systems that are central to the organizational functions that are legacy and transforming these solutions and integrating them with cloud and AI is a formidable challenge. This will need meticulous planning and commitment (financial and resource) to complete with a multi-year strategy.
7. Improve system Interoperability and reduce data duplication: Integrating and ensuring interoperability between diverse data sources and platforms is a complex challenge, especially in large enterprises with legacy systems. Many organizations do not have a proper data purging strategy in place and duplicate data as a matter of “convenience” rather than enabling optimal data integration using, for example, APIs or microservices.
8. Data Democratization demands mindset change: We all have heard the cliché: Data is an asset, but do we truly treat it as an asset? Do organizations know what is the cost of storage and use at a data element level? If so, then the organization can truly assess the “Cost of data” and change the mindset on responsible storage and consumption. Efforts to make data accessible to a wider audience within organizations, ensuring that decision-makers at all levels can access and understand the data relevant to their roles.
Cloud platform strategy:
9. Advancements into Multi-Cloud: Multi-cloud will become the norm, driven by cost optimization, feature access, and vendor independence. Managing data across multiple clouds will require robust data integration and governance frameworks. Standardization and interoperability between cloud platforms will be crucial.
10. Edge Computing: The increasing use of edge computing to process data closer to the source, reducing latency and enabling real-time decision-making in applications like autonomous vehicles and smart cities. This will improve privacy and will impact data pipeline design and architecture.
11. Data Mesh: Decentralized data ownership, virtualization, and management will gain traction, empowering business units while requiring strong coordination and data governance frameworks.
Analytics and AI/ML strategy:
12. AIML Integration including IPV6 integration: Greater integration of AI/ML into business processes. Organizations are leveraging advanced analytics and machine learning models to derive actionable insights and enhance decision-making. Also, Robotic Process Automation (RPA) will be increasingly used to trigger actions based on analytical triggers to further automate processes. These may accentuate the concern of AI eliminating jobs, but the workforce must evolve and adapt. Also, AI/ML integration of AI-enabled devices connected to IPV6 will result in an explosion of data from IoT enablement and automation.
13. Moving the needle on Analytical maturity: Organizations must constantly evaluate their analytical maturity and assess where they stand:
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Here’s the journey ahead:
Reactive -> Proactive -> Predictive -> Prescriptive -> Cognitive/Adaptive -> Collaborative (AI) -> XAI and Transparent AI -> AutoML/RPA
14. Explainable AI (XAI) and responsible AI: The demand for transparent and interpretable AI models is growing. Explainable AI is crucial, especially in regulated industries, to understand how AI algorithms make decisions. Integrating ethical considerations throughout the AI lifecycle, from algorithm design to deployment and monitoring. Ensuring transparency and mitigating bias in AI models to build trust and ethical data usage.
15. Generative and multimodal AI: Creating high-quality text, images, and code, with applications in content creation, product design, and personalized experiences. Combining different modalities like text, image, and audio to improve accuracy and understanding in tasks like machine translation and sentiment analysis. Organizations should move beyond BOTs and further harness these capabilities in other use cases.
16. Automated Machine Learning (AutoML): Tools and platforms that automate the end-to-end process of applying machine learning to real-world problems, making ML more accessible to organizations with limited data science expertise.
17. Blockchain in Data Security: Exploring the use of blockchain technology to enhance data security and privacy, ensuring the integrity and traceability of data throughout its lifecycle.
Challenges (listed in no order):
Data and Cloud:
Data Quality, Governance, Privacy, and Security: Ensuring data privacy, security, and compliance in increasingly complex data ecosystems will be crucial. Also, ensuring the quality, accuracy, and reliability of data remains a significant challenge. Establishing robust data governance frameworks is crucial for maintaining data integrity. The increasing focus on data privacy regulations (such as GDPR, and CCPA) poses challenges in handling and protecting sensitive information. Organizations need to comply with evolving regulatory landscapes.
Cost Management: Deploying and maintaining AI/ML systems can be expensive, especially, with computing resources. Organizations need to carefully manage costs associated with data storage, processing, and model training. Organizations should avoid surprise costs by having a progressive 3-year roadmap on data storage and consumption.
Talent Gap: Filling the need for skilled data scientists, engineers, and architects with expertise in AI/ML.
Employee mindset: Encouraging a data-driven culture within organizations and fostering collaboration between IT and business units remains a challenge. Overcoming resistance to change and ensuring everyone values and uses data effectively is essential.
Artificial Intelligence Explainability and Fairness: As AI models become more complex, ensuring transparency, and mitigating bias will be critical for building trust and ethical data usage. Increasingly, AI explainability will be key to tame hallucinations that are inherent to LLMs that are integrated with various BOT applications.
AI Legal and Ethical Challenges: As an example, we all have heard about AI libraries that are producing songs using the songs of famous dead singers which are very realistic. It is just a matter of time before one of the songs is a billboard top hit that will trigger legal battles on ownership, and royalties with artist families, record labels, and song creators. And, we have already seen deep fakes, whether it is election disinformation actors influencing geo-political issues, or plain defamation of celebrities. In a nutshell, AI will further blur the goalpost of truth or fact.
Organizations struggle with practical value-add use cases of AI: Typically, large, and midcap firms are technology laggards and struggle with practical value-add (revenue generating) use cases that leverage AI. Like any other definition of a minimal viable product (MVP), they must assess if they have a problem statement that they AI to solve or need AI to work on the data to find the problem statement (mostly unsupervised). Without this clarity, organizations will end up claiming glory for dabbling with AI rather than a revenue-generating application.
Analytical maturity: Many organizations are still stuck anywhere between the Reactive -> Proactive stages as they still rely heavily on manual Excel spreadsheets for analysis!
In Conclusion:
2024 will be a year of navigating ongoing data challenges and embracing new opportunities. A successful Data, Cloud, and AIML Strategy needs to be flexible, adaptable, and focused on harnessing the power of data while respecting user privacy and ethical considerations. Enterprise Data and AI Architects will play a key role in designing and implementing robust data architectures that enable data-driven decision-making while staying compliant and secure.
This is my perspective, but I would love to hear from Enterprise Data Strategists to learn more.
Bridging Tradition, Reimagining Success & Championing Leadership Co-Founder & CRO at RE Partners
11moBhanu Kuchibhotla What key elements define your Data, Cloud, and AI/ML strategy for 2024?