Don't know where your customers and assets are? Then you don't know where your business is going.

Business decision-makers take note: location-based context is critical for the future of application experiences and customer engagement. Understanding the location of customers and assets lets companies get closer to their customers and assets to drive better decisions (just to name a few: choosing the a site for a brick-and-mortar location, picking what offer to send to a customer, guiding passengers through airports, deciding where to focus your next marketing campaign, or pushing a greeting and sign-in to a patient as soon as she enters a hospital). The flood of spatial data generated by ubiquitous mobile and proliferating IoT devices offers developers new opportunities for user engagement, but choosing the right tools and services to ingest, analyze, and act upon this information can be vexing.

From comprehensive spatial analysis systems to decomposed location services solving specific problems, the landscape of spatial analysis vendors and offerings can be overwhelming and confusing. In our new report, Vendor Landscape: Spatial Analysis And Location Intelligence, Q2 2016, Forrester breaks down the market into six cohorts to help application developers, analysts, and business decision-makers choose the right technologies for their enterprise. Check out the full report at Forrester.com for more details and to view capabilities of dozens of vendors in these cohorts:

  1. Dedicated spatial analysis software platform providers
  2. Advanced analytics platform vendors
  3. BI vendors with spatial capabilities
  4. Spatial infrastructure and software vendors
  5. Location intelligence services providers
  6. Horizontal and vertical spatial solutions providers
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It’s Elementary, My Dear Watson: Developers Will Build Cognitive Experiences Bit-By-Bit

It seems like nearly everyone is ready (or at least willing) to add intelligence to their applications. Despite the enthusiasm, developers building cognitive applications have encountered some real growing pains. The way we're going about things, it's almost begun to feel like the promise of cognitive computing would collapse like the AI hype in the 1980s or the first robotics hype in the 1960s and 70s. Thankfully, instead cognitive breaking down, we're breaking down cognitive.

Intelligent software is being taken down to the the atomic level so that developers can easily embed cognitive capabilities into applications. Instead of being totally overwhelmed by the breadth of cognitive possibilities, developers can instead use cloud-based API services to pick from among a menu of cognitive services. Services for image recognition, facial recognition, dialog, sentiment analysis, recommendations and more are callable via APIs no fuss no muss - pass the right parameters and the APIs will do the rest. The market landscape of these services is beginning to burst and bloom, much faster than expected. Developers can now build up cognitive applications with IBM's Watson Developer Cloud, HP is augmenting intelligence with Haven OnDemand, Microsoft has recently introduced Cognitive Services, and Google has begun to build the foundations with CloudML.

Are you building applications using these platforms to add more intelligence to your application experiences? What do you think about their potential to help realize the promise of cognitive computing? Let us know in the comments, I'm excited to see what the future holds.

Search Can Build The Foundation For Cognitive Experiences In The Enterprise

Knowledge is power. And in a time where insights drive business differentiation, knowledge is also the origin of power. In our daily routines as consumers, search is probably the most common application we use to find knowledge, and it forms the basis of our personal systems of insight. But at long last, search in the enterprise is catching up. A new wave of search-based applications and search-driven experiences are now being delivered by companies who understand the need to empower their employees and customers with immediate, contextual knowledge in an easily-consumable format.

These applications are not for mere search and results, but also for knowledge discovery. And increasingly, they are a foundational component of cognitive application experiences. Building cognitive experiences can seem arcane and mysterious, but by taking advantage of familiar search technologies at the foundation, enterprise developers can start on the cognitive journey.

In our new research, Mike Gualtieri and I look at how the emerging landscape of cognitive search experiences are incorporating advanced analytics, natural language processing (NLP), and machine learning to enable organizations to see across wide arrays of enterprise data and stitch together insights hidden among them.

Cognitive Search Is Ready To Rev Up Your Enterprise's IQ

Streaming Analytics Will Transform The Internet Of Things Into The Internet Of Analytics

The challenges of how to manage, ingest, store, analyze, and act upon data in the IoT are beginning to bear down on enterprises. The honeymoon talk of ‘billions and billions of devices’ is over and it’s time to get down to the dirt of how to generate value from all these connected devices. Streaming analytics platforms, already architected to handle IoT data as it streams into the data center, are being extended to deploy out to gateway devices (such as wireless access points) and even out to edge devices (such as manufacturing equipment) to extend the intelligence out to where data is generated and actions occur.

Forrester clients can read the full details of our analysis here and start the process of turning slow processes and weekly analytical batches into the immediate insights needed to support today’s dynamic business environment.

Digital Experience Personalization Is Hollow Without Predictive

There’s no other way to slice it: competition for digital audiences is brutal. Intolerance for poor performance and disengaging experiences drives customers to competitor’s sites more quickly and more permanently than any time in history. Users increasingly demand digital experiences that personalize to their immediate needs and adapt to the current context, not treat them as a market or demographic segment.

In recently published research, we found that even as expectations soar, enterprises are personalizing with methods that are too unsophisticated, too opaque, or too convoluted to meet the complexity and mutability needed to serve individuals.  Persona-based segmentation is too simplistic to meet current, much less future, customer expectations. Some solutions provide predictive analytics capabilities but are limited to a few algorithms or black-box methods (e.g. neural networks) are not easily adaptable to new data or scenarios. Those that rely heavily on rules have become morasses, some customers needing to manage and maintain hundreds or thousands of rules to guide digital experiences.

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