AI Takes Center Stage

Data Management in The New Year

Annette Hagood

AI Takes Center Stage

Artificial Intelligence (AI) is an area of computer science that studies the possibility of ‘thinking’ machines and computers.? According to the National Artificial Intelligence Act of 2020, the term ‘artificial intelligence’ means a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.” ?

No longer the experimental darling of prior years, machine learning (ML) will take center stage in the data management space in 2022.? It is expected to be the most important technology adopted, transforming key processes and business models to take advantage of ML.? With the necessary algorithms, compute power, hardware, and skillsets, AI can become the fundamental driver for revenue and growth. Here are a few new AI use cases in the data management space that will flourish in 2022.??

Green AI?

The UN Climate Change Conference, COP26, in Nov’21, continued to emphasize outcomes that directly impact climate action and the Sustainable Development Goals. How can AI help with this endeavor? The airline industry has a call to action, and they are beginning to respond. For example,?Alaska Airlines is using an AI platform and Flyways, to produce the best ways to route aircraft; by analyzing volumes of weather data and suggesting paths that avoid turbulence to provide smoother flights or shorter paths. With just-in-time data analytics, operators are given suggestions on how and where to fly planes, with the platform getting better since it is a machine learning and a dynamically updating ecosystem. Predictive analytics not only support weather, air traffic, and other aspects impacting the flight it could choose to delay a flight to help avoid a weather system or gridlock in the landing pattern thus ultimately reducing flight time, fuel burn, and emissions – ultimately reducing the carbon footprint.??

Customer Experience - AI in the Call Center??

Customer service data management is key to driving a better customer experience.? Executive Orders 12862, 13571,13707,13985, dating back to 1993, set the foundation for improving customer experience and service delivery – putting people at the center of everything the Government does. The use of AI to unlock data that is kept in the millions of daily customer-service conversations between the government and the public will yield a treasure trove of information for predictive analytics. This “speech mining?” provides new CRM use cases with the application of AI tools and techniques.???

Lean Data Management?

Driven by remote work and learning, data, compute, and workloads will move to the edge where it is more efficient to consume and manipulate.? Some industry analysts predict that 2022 will be the year that 75% of enterprise-generated data is processed outside the traditional data center. Data management must therefore follow.? Current edge computing platforms were not designed to handle vast amounts of data cost-effectively. However, if the data is cleaned, and only the relevant blocks pertinent to analytics is retained then this “lean data management” concept can produce useful models for ML algorithms.??

Enterprise Data Fabric (EDF)?

A data fabric allows organizations to model and integrate data at whatever level of granularity desired. It anticipates the need to connect data across the enterprise at speed and scale for dynamic decision-making. To this end, it uses multiple technologies that enable metadata-driven implementation and augmented orchestration design. EDF will help organizations make decisions faster and can create Cognitive Zones? that also ultimately assist citizen data scientists.??

Graph Data Bases?

A graph database is a data model that uses two basic building blocks: nodes, containing all the properties of the entity, and vertices. It utilizes the simple idea of using vertices to establish relationships between pairs of nodes.? It is, therefore, better suited for rapid decision making and visualization of relationships.??

When retrieving data is more important than storing it, the data is interconnected and there is a need for complex relationship analysis, graph databases will see much use. Examples of some use cases are fraud detection, anti-money laundering, population dynamics, virus spread during a pandemic, crime prevention, air traffic management, etc.?

The above discussion provides only a teaser post for my thoughts on Data Management in 2022, and some of the technologies that could impact this space.? If this has piqued your interest stay tuned for more articles in the future.???