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The Future Is Intelligent Apps | @ThingsExpo #IoT #M2M #BigData #Analytics
Developments by industry leaders over the past few weeks have started to add some substance to this intelligent apps trend
Mar. 17, 2017 10:00 AM
I have seen the future! Of course, I seem to say that every other month (maybe that’s because the future keeps changing?), but this is a good one. The future is a collision between big data (and data science) and application development that will yield a world of “intelligent apps.” These “intelligent apps” combine customer, product and operational insights (uncovered with predictive and prescriptive analytics) with modern application development tools and user-centric design to create a more compelling, more prescriptive user experience. These intelligent apps not only know how to support or enable key user decisions, but they continually learn from the user interactions to become even more relevant and valuable to those users.
Several developments and posts by industry leaders over the past few weeks have started to add some substance to this intelligent apps trend, including:
- Cisco’s hijacking of the AppDynamics IPO. AppDynamics creates an “innovative, enterprise-grade application intelligence software platform that is uniquely positioned to enable enterprises to accelerate their digital transformation by actively monitoring, analyzing and optimizing complex application environments at scale.” Sort of wordy (marketing, huh…) but it does articulate the goal of intelligent apps.
- An MIT Sloan Management Review article titled “Digital Today, Cognitive Tomorrow” has some great quotes including:
- “Within five years, I believe all major business decisions will be enhanced by cognitive (analytic) technologies.”
- “If it’s digital today, it will be cognitive (analytic) tomorrow — and not a distant tomorrow.”
In particular, I like the IDC research that states “by 2018, more than half of the teams developing apps will embed some kind of cognitive services in them, up from 1% in 2015.” That is an unprecedented growth and transformation rate.
- Another article titled “Internet Of Things Market To Reach $267B By 2020” brings forth some key predictions that have ramifications on “intelligent apps” including:
- Spending on Internet of Things (IoT) applications is predicted to generate €60B ($64.1B) by 2020.
- IoT Analytics spending is predicted to generate €20B ($21.4B) by 2020.
The chart below summarizes the market opportunity with the integration of IOT apps and IOT analytics to create intelligent IOT apps (see Figure 1).
Figure 1: Growing Market for IOT Intelligent Applications
All of this research and market activity is consistent with the conversations that I am having with customers, who are trying to determine how best to deliver their customer, product and operational analytics in an actionable way that can optimize key business processes, uncover new monetization opportunities, and create a more compelling customer and partner engagement.
The Journey To Intelligent Applications
The path to building intelligent applications starts by understanding the decisions that key business constituents need to make in support their business and operational objectives. Decisions are key because it is around these decisions that we will leverage the ever-growing wealth of internal and publicly available data (e.g., transactional, social, web, mobile, wearables, sensors, embedded, bionic) with data science (e.g., predictive analytics, machine learning, data mining, cognitive computing) to optimize those decisions. That is an approach that we embrace in guiding our clients through their digital business transformation with our Big Data Vision Workshop, Proof of Value and Operationalize offerings (see Figure 2).
Figure 2: Big Data Business Model Transformation Process (The “How”)
To create intelligent applications, organizations will need to embrace key application technology and architecture capabilities including:
Data Lake that supports:
- Rapid data ingest, index and cataloging
- Improved data access across the organization (ecosystem)
- Data management services (e.g., data governance, metadata management, security, privacy)
- Flexible Data Management and Delivery
Data as a Service that supports:
- User self-service
- Faster and simpler data discovery and exploration
- Accelerated data provisioning management, tracking and monitoring
- Integrated data alignment, transformation and enrichment services
Analytics as a Service (Enterprise Analytics) that supports:
- Better, shareable, and embeddable analytics and visualization
- Reusable for faster time-to-insight / time-to-value
- Integrated for broader perspective across varied business functions
- Actively maintained and curated for smarter decisions across the enterprise
Intelligent Applications (App Development) that supports:
- Developed in Agile fashion with modern PaaS and DevOps techniques
- Provide analytic insights at the point of decision
- For everyone, updated for constant relevance
- Delivered in whatever way is best including mobile, web, IOT systems, etc.
Figure 3 summarizes the key capabilities and functionalities necessary to build intelligent applications (special thanks to Clark Christensen for Figure 3).
Figure 3: Intelligent Applications Stack
Intelligent Applications Summary
Let’s be honest, it only makes logical sense to integrate analytics with application development to create intelligent apps that not only deliver a more compelling user experience, but also learn from the user engagement to become more relevant and important to those users. I mean, why collect all this transactional, social, mobile, wearable and IOT data if you aren’t going to do something with it. And the most logical way to drive action (optimized operational decisions) from the data and analytics is via intelligent applications.
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