Master connected intelligence in 8 key steps

When it comes to emerging technologies, there are promises and there are realities. In some cases, the promise is just so high that technology itself can never achieve it. In other cases, new technology is quietly finding its way into the market, both through growing demand and through practical applications.

The connected intelligence is somewhere in the middle. His vision is to use AI, where people and machines connect in a digital environment, share knowledge and form an experience for exponential business growth. It removes business and technical silos, opening AI to switch locations, activities and solutions. Connected intelligence is not a brand new concept, but it is becoming more common, with two-thirds of businesses adopting AI and almost half adopting peripheral computing, according to Forrester. The vision of connected intelligence finds its way into several real-world applications in the following ways:

• Retail is adapting to market disruptions. The big stores, disrupted by the pandemic-induced buying trends, linked supply chain data to inventory, e-commerce and customer experience systems to set the right expectation of when critical items and orders will be shipped.
• Medicine becomes personal. Healthcare providers can now connect with patients through telehealth visits, receive vital data from medical and personal devices, and connect with pharmacy and insurance information to ensure that care plans are followed and remain effective.
• Cars deliver new experiences. Car companies have rethought the driver experience, with electric vehicles presenting the car, learning the driver and passenger experience, and connecting to emerging networks of charging stations with the amenities customers want.

These examples show the real global potential for connected intelligence. So the question arises: How can organizations move from their current data strategy to a more integrated intelligence approach? With the associated intelligence, the linear and point deployment of AI models provides a way to display AI in distributed and complex streams of raw data, events and outputs of the model in real time. This raises the preconditions for corporate data of organizations.

Assess the organizational strength and readiness around data-related intelligencetechnology architecture and delivery leaders will need to master the following eight competencies:

1. Find a source to present your business in data. Data collection should be iterative and continuous for creating, training and optimizing models. New types of data and data (text, voice, image, audio, video) need to increase and improve machine learning (ML) models as data becomes more representative of the environment in which AI is used. Markets and exchanges can provide a reliable source of data through the self-service of data scientists.
2. Capture and receive quality and relevance data. Fresh data is a prerequisite for productive AI. Data scientists need representative data that moves into their sandboxes and learning environments. In an integrated intelligence model, data capture occurs through ephemeral data processing streams (e.g., time series). Data fabrics are bent to match and shape data streams to keep data and insights up to date with the digital experience and results.
3. Select and model data for a better context. Forrester found that 62% of global data and decision makers receive external data. This requires constant classification, labeling and certification of data in order to understand and manage self-service data. AutoML on structured data, computational vision, and behavioral ML in data use can be combined to scale and accelerate data processing and modeling to meet the demands of data scientists and related intelligence solutions.
4. Transform and prepare data for increased relevance. ML data generation is fraught with business logic, security, confidentiality and regulatory considerations. Data scientists, data engineers and data managers collaborate and share transformations and preparatory steps to streamline data flow. DataOps and data management tools use ML to set data standards, schemas and controls, while ensuring transparency and traceability of the data flow for impact analysis and root causes.
5. Test and train to build trust. Make AI testing holistic in data services, data models, business logic, data management and service levels, metadata and ML models and solutions. DataOps, ModelOps, and DevOps can play a critical role in comprehensive and detailed testing and tracking of data flows affecting business logic and routing, as well as the ML model itself.
6. Deliver and deploy to scale. Consistent use of processes and practices for continuous integration and delivery (CI / CD) keeps DataOps, ModelOps and DevOps connected and collaborative. Connected intelligence is built component by component and delivered as a product by data engineers, machine learning engineers and software engineers. Feature storage platforms, data tissue, cloud and peripheral computing systems create the backbone for deploying each component quickly, easily, on a scale and properly managed.
7. Perform and act dynamically to achieve results. Data and ML models need to remain responsive and constantly adapt to business conditions and solutions. MLOps, data management and continuous line analysis ensure that related intelligence complies with service level agreements. CI / CD allows dynamic versions of data and model components to optimize the system, and monitoring and alerts provide an early warning system for potential deterioration.
8. Monitor and evaluate for improvement and ongoing management. Detection of anomalies by DataOps helps to preventively identify and quarantine capture data to mitigate AI deterioration upstream or risk. A global financial firm has a unified detection of data anomalies and ML to effectively increase the risks and threats of known and unknown behaviors of bad participants. Data monitoring tools and MLOps give context to data anomalies and performance impacts to mitigate AI management and responsible AI risk conditions.

Ultimately, by understanding these eight key aspects of the connected intelligence model, technology architecture leaders will be able to move both to AI thinking and use AI to build the new, transformative experiences that business leaders anticipate and which customers require.