Creating a Real-Time Enterprise to Unleash the Full Potential of AI

CIOs and CDOs like me are excited about Artificial Intelligence (AI), but beneath the surface of AI's transformative potential lies two foundational elements critical to leveraging its full capabilities: Digitization of operations and Real-time processing of data across the value chain. If you heard the phrase "we are still running AS/400 or Windows 95 in our facilities", you are not alone. Without digitization of operations and modernization of systems, there are three fundamental gaps companies create in their AI journey: Firstly, they don't connect high volumes of quality data into their data pipeline which is critical to train and operate AI/ML models. Secondly, they limit benefits of automation as digitization is a key step to create a software-led company. The automation with AI can unleash significant productivity and growth. Thirdly, real-time data can significantly reduce cycle times and offer substantial agility when combined with AI models, transitioning from traditional monthly/quarterly decision cycles to a real-time data-driven decision cycle.

What is a Real-Time Enterprise?

A real-time enterprise is a company which is able to connect data from consumers, customers, sales & marketing, supply chain, manufacturing, and sourcing in real-time to respond to changes with agility. Tesla is a famous example for using real-time visibility and decision making during the covid pandemic. While most automotive companies were struggling with their demand vs supply imbalances, Tesla as the first of its kind real-time enterprise in automotive, emerged as a leader. Besides other factors such as vertical integration, the real-time software system Tesla engineered to collect data from cars, customers, stores, its supply chain operations, and suppliers created a significant competitive advantage.

But, what is real-time? The concept of real-time is often nuanced across different companies and industries. In the software world, it typically refers to a latency of few milliseconds (1sec = 1000 milliseconds). In other cases, people use a less demanding standard because making all of your data real-time is costly. Therefore, in the enterprise world you come across the concept of near real-time, which might be a few seconds or even a few minutes latency from when data is created until it becomes available to systems consuming the data. While building a data pipeline, you might use a variety of techniques such as streaming, batch, or micro batch for different data integration needs. Often my rule of thumb is to make everything real-time unless costs are unaffordable and we don't have a line of sight to any tangible value potential.

Besides the low latency of data in a real-time enterprise, it is also important for data to follow the FAIRE model below.

Often data sets in large non-tech companies are siloed and not available readily to consume. This creates two issues: Firstly, building data intensive applications takes time and 50-80% of the time to build a new application is spent on data readiness. Secondly, the value of the combined data set is significantly greater than the sum of the value of individual data sets. Companies with a strong data foundation, with quality data readily accessible using a set of data services are the ones to learn with and scale new data applications the fastest.

What are the Benefits of Operating in Real-Time?

• Enhanced Decision Making: Real-time data allows companies to make informed decisions swiftly, responding to market changes, customer feedback, and operational issues as they occur. This immediacy can be the difference between capitalizing on an opportunity and missing it entirely.
• Improved Customer Experience and Innovation: Real-time insights into customer behavior and preferences enable personalized experiences, fostering loyalty and satisfaction. Immediate response to customer feedback or issues elevates service quality and brand perception. Real-time customer insights feed research and development efforts to prioritize the right innovations and gain market share.
• Operational Efficiency: Identifying and addressing inefficiencies becomes easier with real-time monitoring of operational data. This can lead to significant cost savings, higher productivity, and streamlined processes.
• Risk Management: Real-time data helps companies quickly detect and mitigate risks, from financial discrepancies to cybersecurity threats, reducing potential impacts on the business.

Challenges and Solutions in Becoming a Real-Time Enterprise

While the transition to a real-time data ecosystem offers numerous benefits, companies face several challenges in this journey:

• Data Integration: Many organizations struggle with integrating data from various sources into a cohesive, real-time data pipeline. Solution: Implementation of robust data integration tools and platforms that support real-time processing can bridge this gap, ensuring a seamless flow of information across the organization.
• Manuel Work Processes and Legacy Systems: Manual operations don't allow for capturing data. Outdated systems often cannot support the demands of real-time data processing. Solution: Gradual modernization of legacy systems, digitization of work processes, coupled with the adoption of cloud-based services, can facilitate the shift to a more dynamic data infrastructure.
• Data Quality and Consistency: Ensuring the accuracy and consistency of real-time data is paramount but challenging. Solution: Investing in advanced data management and quality assurance tools, alongside establishing stringent data governance policies, can maintain high data integrity.
• Skills Gap: The lack of expertise in real-time data analysis and management can hinder effective implementation. Solution: Companies should focus on upskilling their workforce and, hiring new talent with the requisite skills in data analytics and management.
• Security and Privacy: Real-time data processing raises significant security and privacy concerns. Solution: Adopting state-of-the-art cybersecurity measures and ensuring compliance with data protection regulations are critical steps in safeguarding sensitive information.

A traditional value vs effort matrix such as below can help create the right roadmap across these initiatives. It is important to follow a business-value-back approach as often value of data not used decreases over time and long technical projects without early wins tend to fail in higher rates.

In conclusion, while the excitement around AI is well-founded, companies must not overlook the foundational role of real-time data and digitization of operations in unlocking its full potential. By addressing the challenges associated with real-time data integration and management, organizations can harness the power of immediate insights to drive decision-making, improve cycle time, enhance customer experiences, and create a competitive edge in the digital AI age. Becoming a real-time enterprise is a multi-year journey, but the rewards are well worth the effort.