Center for Continuous Intelligence

Continuous intelligence (CI) relies on platforms, architectures, and software that lets organizations use real-time and historical data to enable critical decisions and actions to be taken in milliseconds to minutes.

What Is Continuous Intelligence?

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I feel the need to stream: the impact of continuous intelligence

Witness CI in action across global industries and tap into ideas that can successfully integrate CI into yours. Learn how with IBM Cloud Pak for Data.

Operationalize AI in Real Time with Streaming Analytics

The AI Ladder, along with CRISP-DM, provides a strategic approach to creating CI applications on real time data.

eBook: Successful Continuous Intelligence in Various Industries

At the heart of all continuous intelligence (CI) applications is the ability to perform analytics on real-time data streams.

Continuous Intelligence in the Insurance Industry

IBM’s Cathy Reese explains how an insurance company is analyzing streaming telematics data to provide personalized service and pricing. Learn how Cloud Pak for Data, IBM’s leading data and AI platform, supports CI.

Report: CI vs. Traditional Streaming Analytics

Learn why companies need to transition from simply ingesting streaming data to developing insights that help them understand events in real time.

Continuous Intelligence in Healthcare

IBM’s Mike Beddow describes experiences with clients using streaming data to improve patient care. Learn how Cloud Pak for Data, IBM’s leading data and AI platform, supports CI.

The Forrester Wave™: Streaming Analytics, Q3 2019

Real-time analytics have increasingly become a competitive advantage for the modern enterprise.

AI/ML
Given the track record of the national labs in developing HPC technology that was quickly commercialized, it is a safe bet that businesses will reap the benefits of the DOE’s work in AI.
Real-Time Analytics
Augmented analytics is starting to be used in applications that help users do something they cannot do on their own.
Hybrid Cloud
Continuous intelligence applications need a flexible infrastructure that accommodates the dynamic and variable nature of streaming data.
AI/ML
Using machine learning to accurately predict and improve the health and life of a battery will enable manufacturers to embed this software straight into their battery devices and improve the in-life service for the consumer.
Hybrid Cloud
Hybrid cloud is ideally suited to meet computing requirements for real-time analytics of streaming data. It offers advantages that make deployment easier and less costly.

Additional Resources

AI/ML

5 Places to Take Online AI Courses While You Ride Out COVID-19

Here is a sampling of five online learning organizationswith courses on AI and data science you might want to check out while we ride out COVID-19.

Data Ingestion

Considerations for Successful Continuous Data Ingestion and Analysis

Given the many ways CI applications work with data, organizations need flexible, high-performance, highly scalable data ingestion and analysis solutions.

Best Practices

Successful Business Adoption of AI Requires Soft Skills

MBAs educated today using methods from the past may not be prepared for jobs of the future.

Best Practices

Energy Industry Changes Require Continuous Intelligence and AI

IoT, stream processing, and analytics based on CI and AI allow energy companies to boost efficiencies and better meet customer and regulatory demands.

AI/ML

Artificial Intelligence Predicts Corn Yield Rates

Precision agriculture, through the use of artificial intelligence and analytics, could be a $12 billion industry by 2025.

Use Cases

Self-Deprecating Robots are Better Conversationalists

In any group setting, one way to establish and promote better social engagement is through vulnerable expressions.

Best Practices

Empowering Digital Twins with Streaming Analytics

Combining intelligent streaming analytics with real-time digital twins for aggregate analysis offers several benefits in a variety of real-world applications.

Use Cases

Tech Partners Team Up for IoT-Enabled Smart Hotel Room

The Fiesta Americana Viaducto Aeropuerto Hotel in Mexico City now features a room full of Enseo’s smart hotel room technology.

AI/ML

Can Continuous Intelligence and AI Predict the Spread of Contagious Diseases?

Scientists around the world are turning to sophisticated analytics to predict the spread of the coronavirus and other contagious diseases.

Best Practices

Overcoming the Barriers to Successfully Scaling AI

AI is becoming a technological norm to advance and modernize existing digital infrastructure, now the real question is, “what’s standing in my way?”.

Use Cases

Robotic Process Automation Implementation Choices

When implementing RPA, the biggest challenges involve development and infrastructural issues.

Industry Applications

Hardware Acceleration Drives Continuous Intelligence

AI hardware accelerators have massively parallel architectures that economically deliver the needed compute performance for Continuous Intelligence applications.

AI/ML

BI Providers Integrate Machine Learning into Business Processes

Innovative tools and data annotation service companies help produce much higher quality data to train AI models in much faster time frames.

Hybrid cloud

How Kubernetes and Containers Enable Highly Scalable CI Applications

Building continuous intelligence applications on cloud-native architectures using containers and Kubernetes makes it easier to deploy, maintain, and update those applications to meet changing business requirements.

Data Ingestion

New Tool Offers Help with Data Annotation

Innovative tools and data annotation service companies help produce much higher quality data to train AI models in much faster time frames.

Best Practices

What Is Cloud-Native and Why Does it Matter for CI?

Cloud-native is the architecture of choice to build and deploy AI-embedded CI applications because it offers benefits to both the business and developers.

Video

Continuous Intelligence in the Chemical Industry

IBM’s Cathy Reese discusses how a chemical company is transforming customer service by analyzing streaming data.

Industry Applications

Industry 4.0 Progress Slow, But Progress Nonetheless

Seven in 10 executives believe that long-term business success requires the integration of Industry 4.0 technologies into their operations.

IoT/Edge

2020 Will Be the Year of Continuous Intelligence

A confluence of factors is creating the perfect storm of the adoption and mainstream use of continuous intelligence.

Best Practices

How AutoAI Accelerates AI Adoption

With AutoAI, businesses can build and deploy a machine learning model with sophisticated training features and no coding.

Hybrid Cloud

Hybrid Cloud Optimal For Businesses

Hybrid cloud has seen a sharp rise in interest over the past few years, due to the limitations and ‘lock-in’ effect of having a single cloud provider.

Real-Tme Analytics

Will Predictive Analytics Boom Pave the Way for CI?

Future adoption of CI will require expertise in predictive analytics and AI. The anticipate explosive growth in the adoption of predictive analytics will give businesses the experience with such sophisticated analytics methods.

AI/ML

AI Advances in 2020 to Come Slow But Steady

Organizations must determine precisely which types of processes will benefit most from an injection of machine and deep learning algorithms.

Industry Applications

Continuous Intelligence Delivers Benefits Across Industries

Continuous intelligence, which relies on real-time analysis of streaming events data, is being widely adopted across many industries, for many applications.

AI/ML

IBM Watson AI XPRIZE Enters Final Stage of Competition

Three finalists will be announced for the competition that provides a $3 million Grand Prize, $1 million 2nd place prize, and $500k 3rd place prize.

Video

Continuous Intelligence in the Transportation, Utility, and Retail Industries

IBM’s Cathy Reese discusses the benefits and inhibitors her clients are experiencing as they implement continuous intelligence strategies.

Video

Experience continuous intelligence with Streams on IBM Cloud Pak for Data

Enable your business to gather, analyze and act upon data easier than ever.

Best Practices

AI Transparency and Regulatory Issues Loom Large in 2020

There is no questioning the booming success of artificial intelligence (AI) and its increasing adoption by businesses in all fields. A is critical in the growing number of critical applications that make use of continuous intelligence (CI).

AI/ML

AI Job Skills In Big Demand

Many businesses believe they need AI, but one of the biggest challenges is finding talent.

Industry Applications

Using CI and AI to Tackle Top Financial Services Regulatory Challenges

Continuous intelligence offers benefits that can help companies better optimize, predict, and automate processes for greater business value.

AI/ML

Forget Machine Learning, Constraint Solvers are What the Enterprise Needs

Constraint solvers take a set of hard and soft constraints in an organization and formulate the most effective plan, taking into account real-time problems.

Best Practices

Will the Consumerization of AI Set Unrealistic Expectations?

Corporate AI offerings must be as robust and feature rich as the consumer AI services workers use at home. Otherwise, users will not embrace them.

Industry Applications

Continuous Intelligence in Action: Lessons from 3 Use Cases

Continuous intelligence offers benefits that can help companies better optimize, predict, and automate processes for greater business value.

Industry Applications

How AI Will Make Industry 4.0 Profitable

The ability to move to Industry 4.0 depends on an organization’s ability to scale its proofs of concept to a more industrialized level.

AI/ML

Continuous Intelligence and AI to Dominate 2020 Analytics

Perhaps the underlying reason AI, CI, and other technologies will be disruptive factors in 2020 is digital transformation.

Best Practices

4 Things to Know About Using Kubernetes for AI

Many businesses use container-based microservices architectures to develop AI. The container orchestrator Kubernetes can help with management and scaling.

Video

Continuous Intelligence in the Public Sector

Mike Beddow, IBM Cloud and Analytics Sales Leader, discusses continuous intelligence technology adoption trends in the public sector.

Use Cases

AI So Expansive You Can Drive A Truck Through It

With full autonomy, operating costs would decline by about 45 percent, saving the US for-hire trucking industry between $85 billion and $125 billion.

IoT/Edge

Predictive Maintenance: The Continuous Intelligence Killer App

CI based on analysis of sensor and IoT data can help spot state changes in devices and make predictions about an asset’s failire probability.

Best Practices

Act Now to Prevent Regulatory Derailment of the AI Boom

If businesses do not adopt their own best practices in addressing AI transparency and bias issues, they may not have a choice in the future.

Best Practices

AI’s Empathy Problem

AI analysis may identify certain emotional traits, without providing a more holistic perspective on the person exhibiting those traits.

AI/ML

Using the AI Ladder to Prevent the New Digital Divide

Businesses that work up the AI ladder will eventually be able to reach the top rung and have pervasive AI throughout their business processes.

Use Cases

Continuous Intelligence Empowers Smart Mirrors

Some of the most innovative uses of smart mirrors with artificial intelligence can be found in the retail and automotive industries.

Best Practices

What’s the Expiration Date of Your Data Insights?

Today, there is a need to transition from simply analyzing streaming data to developing insights that understand events in real-time.

Fundamentals

What Do You Need to Stream?

Getting the data is sometimes the hardest part of continuous intelligence.

AI/ML

Not Sure What Your AI Is Doing? Google Will Explain

Google Explainable AI is a new set of tools and resources to help developers better understand model behavior and detect and resolve bias, drift, and other gaps in their models.

Data-Analytics Platform

Data Architecture Elements for Continuous Intelligence

By some estimates, collecting, curating, and tagging data accounts for about 80% of the effort in modern AI projects.

Hybrid Cloud

Continuous Intelligence Apps Benefit from Cloud-Native Architectures

A cloud-native architecture provides a way for businesses to build new CI applications that incorporate modern analytics.

Data-Analytics Platform

IBM Extends Cloud Pak for Data Strategy to Postgres

The goal of the strategy is to make it simpler for IT organizations that are building stateful applications to mix and match databases as they best see fit.

Industry Applications

Mutually Complementary: Continuous Intelligence, IIoT, and Digital Twins

Infrastructure spending specifically to train machine learning models has grown more than 50 percent year-to-year over the past two years.

Video

Watson Health for Diabetes Management

IBM Distinguished Engineer Pam Nesbitt discusses the advances in diabetic care using AI and predictive analytics.

AI/ML

CI and Machine Learning Growth Drive Infrastructure Upgrades

Infrastructure spending specifically to train machine learning models has grown more than 50 percent year-to-year over the past two years.

Use Cases

MIT Develops Autonomous Sensing System, Using Shadows

The new system offers an improvement of a second over current Lidar systems deployed in almost all autonomous vehicles.

Data Ingestion

Continuous Intelligence Data Considerations

Businesses must move to a data and analytics architecture approach that incorporates diverse data and delivers analytics everywhere.

Use Cases

Recruitment Chatbots Better Engage Today’s Workforce

Use of recruitment chatbots is on the rise because text messaging is a much more effective method than email or calls to engage job candidates.

AI/ML

Can AI Improve Disaster Response?

Machine learning programs are being utilized to analyze data from previous disasters and provide forecasts.

Best Practices

How to Speed AI Deployment to Achieve CI Benefits Faster

Companies need to build up AI skills and overcome cultural challenges to its use. The key to accomplishing these objectives is to do many projects quickly.

Real-Time Analytics

Universities Use Real-Time Analytics to Keep Students Safe

Sensor data, surveillance video, and continuous analysis give colleges the time-critical information to take actions to improve physical campus security.

Fundamentals

The Case for Continuous Intelligence

It’s time to move beyond simple streaming analytic applications to truly cognitive applications that incorporate the latest in machine learning and deep learning.

Industry Applications

CI Enables New Healthcare Diagnostic Capabilities

Continuous intelligence (CI) is enabling innovative approaches to healthcare, giving clinicians fast, more accurate diagnosis capabilities.

Use Cases

Enhanced CI-Based Customer Engagement Drives Streaming Analytics Market Growth

Streaming analytics shifts the focus from systems of record to real-time data for actionable insights.

AI/ML

IBM Confronts AI Resistance

The AI challenges organizations are wrestling with span everything from the integrity of the data being employed to drive AI models to a lack of skills.

Data-Analytics Platform

Continuous Intelligence Requires an Event-Driven Architecture

An event-driven architecture supports the analysis of event notifications to make decisions based on situational awareness.

Use Cases

Credible Chatbots Outperform Humans

5G will greatly increase the amount of data that can be sent at one time and the speed at which it can be sent.

AI/ML

Researchers Surpass Speech Recognition Methods With Help From Honey Bees

Improved speech recognition is achieved using an artificial neural network that use opposition-based learning and bee colony behavior algorithms.

Industry Applications

Government Interest in Continuous Intelligence and AI on the Rise

AI research at US agencies includes work on autonomous systems, predictive maintenance, smart cities technology, and more.

Data-Analytics Platform

Infrastructure Architecture Requirements for Continuous Intelligence

Continuous intelligence analytics solutions are platforms that ingest streaming data, perform analytics, and embed code, machine learning models, and rules to enable the real-time enterprise.

Industry Applications

Energy Sector Realizes Untapped Potential for Real-Time Analytics

The toolkit uses machine learning algorithms to help organizations validate how their AI models are constructed.

Data-Analysis Platform

Will 5G Have a Role in Providing CI Apps with IoT Data?

5G will greatly increase the amount of data that can be sent at one time and the speed at which it can be sent.

Real-Time Analytics

Continuous Intelligence to Benefit from Streaming Analytics Boom

Revenues for the global streaming analytics market are expected to increase at a compound annual growth rate of 28.2 percent for the next five years.

Use Cases

Using Continuous Intelligence for Decision Support and Automation

CI-based decision support and decision automation systems have the potential to deliver significant benefits to organizations that need to react in the moment to dynamic situations.

Use Cases

AI Brings Real-Time to Fraud Detection and Prevention

Advanced analytics and biometrics are becoming central to enterprise anti-fraud programs. AI adoption and plans are up 300%.

Fundamentals

8 Digital Transformations Coming in 2020

It’s never too early to start planning for tomorrow.

AI/ML

Gartner: 77% Organizations Aim To Deploy AI, Staff Skill Holds Adoption Back

For business leaders, the motivators for AI are improving customer experience.

IoT/Edge

Getting the Edge with Data About Data

As IoT data becomes a more important part of enterprise business operations, the ability to reduce latency in data analytics and processing can make a difference.

Industry Applications

Can the Smart Factory Get Even Smarter?

While industrial process change can be a slow process, transformational digital technologies are making inroads in the new smart factory.

Use Cases

Deloitte Report Details Scope of Data Modernization Challenge

Survey finds many organizations are still approaching data modernization as a tactical project rather than as a strategic initiative.

AI/ML

IBM Extends Scope of DataOps Portfolio

New IBM tools advance the state of DataOps by automating manual tasks that would have to be performed by a data engineer.

Data-Analytics Platform

Real-Time Insights with IBM Streams on IBM Cloud Pak for Data

IBM Streams brings continuous intelligence and team collaboration to businesses on the Cloud Pak for Data platform (formerly IBM Cloud Private for Data) by combining the power of streaming and stored data with AI.

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eBooks & Reports

What Is Continuous Intelligence?

Continuous intelligence (CI) gives organizations the information needed to react to events as they are happening. It is a form of real-time analytics used to support decisions when actions must be taken in milliseconds to minutes.

Continuous intelligence relies on platforms, architectures, and software that allows organizations to collect, organize, and analyze data to enable fast actions in response to real-time events. Unlike traditional analytics, it relies on both the analysis of historical data and real-time data from a variety of sources. That data can include email messages, clickstreams, social media, data logs, and information from sensors and Internet of Things (IoT) devices.

In most cases, CI makes use of artificial intelligence (AI) and machine learning (ML) to complement traditional analytics used to investigate these datasets. Businesses can realize significant benefits from CI when their real-time analytics capabilities are integrated into their operations. In this way, CI can prescribe actions in response to business moments and other events.

What makes CI different?

CI enables smarter business decisions using real-time data streams and advanced analytics. It differs from traditional analysis in that it is always on for situational awareness, prescribes actions, and allows businesses to be proactive.

In contrast, conventional operational decisions are made by doing real-time calculations on historical data or data captured at one moment in time. A simple example that illustrates the difference is an app that calculates the distance users have walked each month using global positioning system (GPS) location data. The traditional approach would make one calculation using all the data transmitted from the user’s mobile device and stored over the past month. In contrast, CI using stream processing performs continuous analytics to keep running totals that are updated moment by moment as the GPS data is refreshed.

In this way, CI augments the traditional analytics approach by enabling continuous analysis to be modified over time. For example, the streaming data from a user’s phone can be analyzed using machine learning techniques to determine the most popular walking routes or detect deviations from a user’s standard walking patterns.

Another powerful example that highlights the differentiation of CI from tradition real-time analysis is machine maintenance. A traditional approach is to either wait until something breaks and fix the problem, do preventive maintenance by replacing parts on a pre-determined schedule, or conduct periodic manual inspections.

CI enables predictive maintenance. Continuous sensor-based monitoring could be used to identify leading problem indicators and implement a just-in-time, condition-based replacement of parts. The instantaneous information from sensors also can be analyzed in real time to optimize a machine’s performance.

Why is CI going mainstream now?

Organizations need to identify risks and opportunities in high-velocity data—opportunities that often can be detected and acted on only at a moment’s notice. Flows of data from streaming sources (such as market data, the IoT, mobile devices, sensors, clickstreams, and even transactions) remain largely unnavigated. Such data must be unlocked to optimize decision making.

To innovate, organizations must quickly make sense of all this data—from persistent structured data to streaming data from sources such as smart sensors and IoT devices—decisively, consistently, and in real time. Understanding the context of data is key to improving customer relationships, enhancing operational efficiencies, reducing risk, and uncovering new opportunities. CI is the instrument that derives that context.

Leading-edge applications such as mobile device navigation systems, equity trading systems, e-commerce site referral engines, and others have made use of CI for years. For example, mapping apps combine real-time location data from every traveler’s mobile phone to determine the concentration of cars on the roads and how fast they are moving. Some add in updates from Departments of Transportation from across the country to pinpoint accidents. And in the latest iteration, some mapping apps add in crowdsourced information about delays, accidents, and debris in the road provided by drivers and passengers. The information is synthesized to determine estimated time of arrival at a destination and to suggest alternate routes, all in real time.

Now CI is being more widely adopted across many industries and for many applications. The reason: There is a perfect storm of data sources and tools. Businesses have huge amounts of streaming data from IoT devices, smart sensors, and other sources that are ripe for analysis and inclusion in business processes. And the technologies (hardware, software, services, and systems) to make use of that data are now available.

The combination of lots of streaming data and solutions to derive actionable intelligence from that data means CI can deliver significant benefits to businesses of all types and sizes. A financial institution could use CI for real-time fraud prevention by detecting malicious transactions and stopping them before they are executed. An online retailer could use CI to provide an enhanced customer experience and improved service when a customer contacts a call center or moves through the product selection and purchasing process online. First responders and agencies dealing with natural disasters could use CI to deploy resources more efficiently as a dynamic situation unfolds.

CI’s impact is expected to accelerate its adoption. By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions, according to Gartner analyst Roy Schulte.

Key elements to CI success

Several factors make CI more accessible to the masses today. They include:

Hardware: Businesses today have access to the high-performance computing capabilities needed for CI. The price of CPUs, GPUs, and high-performance memory and storage continues to drop, allowing businesses to install systems with processing power that rivals that of supercomputers of five years ago.
Businesses also have the option of using high-performance CPU, GPU, storage, and interconnect instances from the major cloud service providers. Getting a CI initiative started does not require a significant upfront investment with this approach. Additionally, cloud services allow businesses to scale up efforts over time quickly.

Analysis, AI, and ML software: A second factor assisting CI in becoming mainstream is the availability of new analysis algorithms to make sense of streaming data. Today, businesses have access to relatively easy-to-use machine learning, artificial intelligence, stream analytics, and time-series DBMS software.

Cloud and middleware: The growing use of cloud-native applications, microservice architectures, and hybrid cloud makes it easier for businesses to develop, deploy, and run CI across the enterprise. And modern middleware gives businesses the ability to move, host, and access CI applications and data on-premises or in the cloud, taking performance and cost factors into account.

Data platforms and architectures for CI

The heart of any CI effort is the streaming and historical data to be analyzed. Managing the data, preparing the data for analysis, ensuring access to the data, and safeguarding the data can be formidable tasks.

The right data platform and architecture would simplify and unify how businesses collect, organize, and analyze data to accelerate the value of real-time analytics and AI.

Additionally, what’s needed is a hyper-converged architecture that combines storage, computing, and networking software into a single system. Such a solution can simplify and unify how a business manages, governs, and analyzes data. The right solution allows businesses to provision and deploy data services flexibly and rapidly.

Hyper-converged technology makes it possible for businesses to scale and evolve their infrastructures simply and economically as application loads change. An ideal solution would use a flexible microservices software architecture and acceleration hardware designed for the compute needs of stream processing and AI.

The CI paradigm shift

Making use of these technologies for CI gives businesses a way to radically change the way they make decisions.

Traditional analytics and business intelligence (BI) are descriptive, providing information on what happened, and diagnostic, helping to explain how something happened. For example, traditional analysis of merchandise sales would note that a clothing line had a sudden spurt in popularity, and it was due to a social media influencer’s endorsement of the line.

More sophisticated uses of analytics and BI also are often predictive. For example, an analysis might use machine learning to help infer what is likely to happen next. An example would be a machine maintenance application where analysis of historical data identifies that a machine of this model, this age, and operating at this temperature, is likely to have a specific part fail after these many hours of service.

Decisions made using these methods are reactive and are based on historical data. In the case where a machine part is identified as having a high likelihood of failing, that part can be replaced before it fails and disrupts operations. But the problem with this approach is that it does not use situational information to make smarter decisions. A part might be replaced to be on the safe side sooner than it needed to be. That practice results in spending money on new parts while the old are still functioning.

In contrast, CI would use real-time information about the part to find that its performance is degrading at a slower-than-normal rate, so may remain in place for a longer time. This approach saves money by extending the time a part is used, while also ensuring the part will not fail. Such applications get to the heart of CI’s strength.

CI use streaming data to understand what is happening and is embedded within business operations. In this way, CI can derive real-time context data from event streams. Those streams can include:

  • Transaction records such as customer purchases, bank transactions, and more
  • Interactions including clickstreams, emails, customer call logs, and social media posts
  • Related data such as IT network logs, market data, and news feeds
  • Physical events data captured by sensors and IoT devices including machine logs, geolocation, telemetry, and SCADA data

CI based on such event streams enables proactive responses (like supply chain visibility, facility monitoring, etc.) and reactive responses (such as providing a customer with a next-best offer or checking transactions for fraud). It takes decision-making to a new level. In the past, predictive analytics might be used to determine what will happen next. A machine part will fail, for example. CI can complement this predictive capability using prescriptive analytics or rules to determine what to do next. As such, CI can be used for decision support and decision automation.

CI used for decision support helps businesses and people make decisions on how to respond to events that may be rapidly changing. The derived intelligence complements the actions a person would take. So rather than making a decision based on a gut feeling (a change in market conditions, for example), CI would provide the information to back up the decision. General application areas for CI in business include risk assessment, target marketing, sales acceleration, revenue growth, finding opportunities for funding business growth, and increasing operational efficiencies.

CI for decision automation takes things to a higher level. The derived information is used to take actions without a person involved. CI for decision automation has many benefits. It offloads work from people; is a faster, less expensive way to operate than relying on human decisions; ensures more consistent decisions; and can guarantee better compliance by following predetermined policies.

Bringing it all together

CI helps businesses make decisions while events are happening. It brings meaning to fast-moving data streams and helps organizations in a wide variety of industries.

Today, data streams that business have available and should make use of for CI can originate from many sources, including:

  • IoT devices and sensors
  • Text files, spreadsheets, images, video, and audio recordings
  • Email, chat, and instant messaging
  • Web traffic, blogs, and social networking sites
  • Financial transactions, customer service records, telephone usage records, and system and application logs
  • Satellite data, GPS data, smart devices, sensors, network traffic, and messages

Solutions to support CI must include appropriate hardware and real-time analytics and artificial intelligence software. The architecture to support CI must be capable of storing, managing, and safeguarding historical and streaming data. Solutions must be deployable on-premises and in the cloud, and easily moved to the platform that offers the best performance and cost as conditions change.

With the right platform and architecture, a CI solution will be able to:

  • Continuously analyze data in motion across multiple sources to deliver actionable insight.
  • Connect to any data stream to make predictions and discoveries as data arrives to enhance and improve analytic models and cognitive systems.
  • Deploy a complete set of streaming analytics—such as natural language processing, geospatial, predictive, and more—to satisfy unique, industry-specific requirements, and use cases.
  • Speed time to value with open-source technologies using APIs and visualize data easily with drag-and-drop development tools that help support faster time to deployment and effective production management.

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