Why Big Data Needs to Become Smart Data: An AI/ML Perspective

Big data has been a hot topic for over a decade now, as organizations rushed to collect massive amounts of data with the promise that it would provide game-changing insights. However, the reality has not always lived up to the hype. Many companies have struggled to extract real value from their big data investments due to challenges with the volume, variety, and quality of data.

This is where the concept of "smart data" comes in. Smart data refers to data that has been preprocessed and optimized to be "analysis-ready" – structured, accurate, and relevant for the intended use case. It represents the natural evolution of big data to make it more usable and valuable, especially for artificial intelligence (AI) and machine learning (ML) applications.

In this article, we‘ll take a deep dive into why big data needs to become smart data, looking at the challenges with big data, the benefits of smart data, key enabling technologies, and real-world use cases. We‘ll also examine some potential concerns with smart data and take a peek into the future of intelligent data.

The Big Data Boom and Disillusionment

First, let‘s look at some statistics that highlight the astonishing growth of big data:

• The amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020 and 180 zettabytes in 2025. (Source)
• The big data and business analytics market is expected to grow to $274.3 billion by 2022, with a five-year CAGR of 13.2%. (Source)
• Approximately 80% of the data collected by organizations is unstructured. (Source)

However, many organizations have failed to capitalize on their big data as much as hoped:

• Only 14.5% of firms have deployed AI capabilities into widespread production. The biggest challenge is the lack of data to train AI. (Source)
• Through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or teams. (Source)
• 60-73% of all enterprise data goes unused for analytics. (Source)

Clearly there is still a large gap between the promise and the reality of big data. Companies are collecting more data than ever, but struggling to manage and utilize it effectively to power data-driven decisions and AI/ML applications.

From Big to Smart: The Evolution of Data

The big data landscape is evolving and the emphasis is shifting from quantity to quality. This is where "smart data" comes in. Let‘s look at what defines smart data and how it differs from traditional big data:

The key point is that smart data shifts the focus from collecting the largest possible data set to curating data that is optimized for the intended analysis. It applies AI and ML techniques to automate much of the tedious data wrangling and integration work that has traditionally been a major bottleneck for deriving value from big data.

Let‘s look at some of the key processes and technologies involved in smart data:

• Data ingestion: Automating collection and streaming of data from diverse sources
• Data validation: Automatic detection and handling of inaccurate or missing data
• Data enrichment: Adding relevant contextual metadata, often leveraging knowledge graphs
• Data integration: Combining data from multiple sources and resolving entities
• Data storage: Optimized storage format and platform aligned with analysis use case
• Data access: API-based access enabling real-time processing and self-service analytics

By implementing these data management processes and leveraging AI/ML, organizations can dramatically improve the speed and accuracy of gaining insights from their data.

How Smart Data Enables Intelligent Applications

In addition to making data analysis more efficient, smart data is a key enabler for AI and ML applications. High-quality, relevant data is critical for training accurate models and avoiding the "garbage-in, garbage-out" problem. Some examples of how smart data enables AI/ML:

• Supervised learning requires accurate historical data for training and validation
• Natural language processing (NLP) models leverage knowledge graphs to understand entities and context
• Computer vision models require labeled image data for training
• Predictive maintenance leverages sensor data enriched with contextual metadata
• Recommendation engines rely on integrated customer activity data

With the growing adoption of AI/ML across industries, smart data will only become more important. According to the 2021 IBM Global AI Adoption Index, over a third of companies are using AI, with another 43% exploring AI. Some of the top use cases include business process automation, IT optimization, customer care, and business intelligence – all of which require smart data.

Smart Data in Action: Industry Examples

Let‘s look at some real-world examples of how smart data is being leveraged in different industries:

• Healthcare:
  • Medical devices and wearables pre-process and analyze patient data at the edge for real-time monitoring and alerts
  • Smart data integration enables a unified patient view for improving care and reducing costs
  • AI-powered medical imaging leverages labeled image data for training models to detect diseases
• Manufacturing:
  • Smart sensors on equipment generate structured data optimized for predictive maintenance models
  • Computer vision models analyze video feeds to detect product defects in real time
  • Digital twins leverage enriched equipment data to optimize production processes
• Financial Services:
  • Automated data validation and integration improves efficiency of regulatory reporting
  • AI models for fraud detection analyze structured customer activity data in real time
  • NLP algorithms extract insights from unstructured data like earnings call transcripts
• Retail:
  • Recommendation engines leverage unified customer activity data to personalize offerings
  • Smart inventory data improves demand forecasting and supply chain optimization
  • Computer vision models analyze in-store video feeds to optimize merchandising

These are just a few examples of the vast potential for smart data to enable intelligent applications across sectors. As data volumes continue to grow and AI/ML adoption accelerates, smart data will be a key competitive differentiator.

Considerations and Future Outlook

While the benefits of smart data are clear, there are also some potential concerns to consider:

• Privacy and security: As more data is collected and integrated, strong governance is critical to protect sensitive information and manage access
• Algorithmic bias and transparency: Automated data processing and model training introduces the risk of perpetuating biases. Processes are needed to detect and mitigate bias.
• Organizational change: Becoming a smart data-driven organization requires changes to culture, skills, and processes beyond just adopting new technology

Looking ahead, the future of smart data is tightly linked with AI and ML. As AI/ML capabilities advance, more data processes will be automated and optimized in real time. Techniques like reinforcement learning, transfer learning, and federated learning will enable continuous improvement of models and more decentralized, privacy-preserving data intelligence.

We‘ll also likely see smart data become more accessible to a broader range of users through the rise of augmented analytics and AI-as-a-service offerings. Just as cloud computing democratized access to big data infrastructure, smart data platforms will abstract away complexity to empower domain experts to leverage data.

Conclusion

Data is often touted as "the new oil" – a raw resource that can be refined into valuable insights that power the intelligent applications of the future. However, much like oil, data in its raw form has limited utility. It needs to be processed and refined into a more usable form.

This is the essence of smart data – applying AI and ML to automatically validate, enrich, and optimize data to make it "analysis-ready." Smart data makes data analysis more efficient, while also enabling more advanced AI/ML applications. It will be critical for organizations to be successful in a future where data continues to grow in volume and AI becomes pervasive.

The evolution from big to smart data is already well underway. By developing the right data strategy, architecture, and governance processes, organizations can unlock the full value of their data assets and become intelligent enterprises. The future belongs to those who can tame the chaos of big data and refine it into smart data.