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Striim – Top 27 Predictions for 2017

Striim_logoAccording to Markets & Markets, the streaming analytics market is estimated to grow from USD 3.08 billion in 2016 to 13.7 billion by 2021. Businesses are quickly beginning to realize that they need real-time data integration and streaming analytics to gain deeper insights, secure their data and remain competitive. Within milliseconds of data being generated, companies can:

  • Filter out irrelevant data
  • Perform aggregations and group-by’s
  • Correlate information across streams
  • Combine metadata, reference and historical data with streaming data for context
  • Detect anomalies/outliers in real time

Steve Wilkes, co-founder and CTO at Striim, Inc. the real-time data integration and streaming analytics platform, offers his top predictions for 2017 on how real-time data integration and streaming analytics will impact the many areas it touches, including Cloud, IoT, Integration, Analytics, Big Data and Security.

General

  • The lines that demarcate where Enterprise data resides and is processed will blur rapidly during 2017 as more and more organizations extend their data stores to the Cloud and embrace IoT. This will enable new opportunities and bear fruit in the form of scalability, agility and cost savings, but will be fraught with challenges concerning security, integration and analytics.
  • CIOs will continue to face mandates to do more with less, making it essential to evaluate and apply next-generation technologies beyond their traditional use cases to increase productivity and decrease costs without compromising customer experience and SLA compliance.

Cloud

  • The Hybrid cloud model will become predominant with Enterprises moving data to the Cloud for scalable storage and analytics. More business systems will be moved to the Cloud through database migrations.
  • Regulated industries, previously more cautious in adopting Cloud technologies, will become more aggressive in their strategies utilizing real-time encryption, obfuscation and tokenization techniques to protect sensitive Cloud-bound data.
  • It will become normal for Enterprises to use multiple Cloud vendors, with Cloud-to-Cloud integration and migration becoming essential. Analytics applications will emerge for things like multi-Cloud monitoring and spot-price-based instantiation.

IoT

  • Low power mesh networks will begin to replace single-point-of-failure access points and enable disparate IoT devices to interact within a mesh environment.
  • IoT Platforms will grow in strength and capability incorporating device registration, management and communication features as well as integration, analytics and machine learning.
  • Organizations adopting IoT will be forced towards event-driven streaming architectures to handle the processing and analytics of huge volumes of rapidly produced (fast and vast) data. This shift will enable these organizations to apply streaming technologies to other integration and analytics use-cases outside of IoT.
  • IoT applications historically thwarted by compliance and security concerns will turn to in-memory computing technologies to enable the required protections before data lands on disk.
  • Simple IoT use cases such as the real-time tracking of the movement of people or packages via geolocation and time windowing will become prevalent across healthcare, transportation, manufacturing and logistics.

Integration

  • As memory and CPU continue to increase in power and decrease in cost, the delineation between real-time streaming and batch integration will fade, with streaming integration taking on more and more legacy batch ETL workloads.
  • Streaming data platforms will become more powerful and unified, merging enterprise database and log file data with IoT, cloud, SaaS and application information. These platforms will become distributed through enterprise, cloud and gateways, providing robust nervous systems through which all data flows.
  • Integration and analytics will become increasingly reliant on one another, as aspects of machine learning, artificial intelligence and spatial and temporal correlation are leveraged to solve integration problems.

Analytics

  • As with complex event processing in 2016, analytics platforms will incorporate more and more machine learning and artificial intelligence capabilities in 2017.
  • Log correlation will be increasingly managed in-memory as both the requirements for real-time analysis and the cost of licenses for traditional technologies explode.
  • Neural network and genetic algorithms will facilitate greater unsupervised learning across a wide spectrum of use cases, providing insights that may not have been considered by human data scientists.
  • Rapid response, reliability and security concerns will push real-time analytics to edge locations for IoT. This will become evident through connected cars, home IoT hubs, retail store-based gateways and other localized technology. Anonymized data will be pushed to the cloud for deeper analytics.
  • Customers will become accustomed to more personalized care and streamlined experiences as analytics, artificial intelligence and automation are applied to all customer interactions.

Big Data

  • On-premise Data Lakes powered by the big three Big Data vendors will give way through commoditization to cloud-based Big Data storage and analytics utilizing vanilla open-source Hadoop and Spark.
  • Security concerns will force Enterprises to take a second look at their Data Lake initiatives. Current practices that dump raw log files with unknown and potentially sensitive information into Hadoop will be replaced by systematic data classification, encryption and obfuscation of all long-term data storage.
  • Streaming data preparation will become paramount as fast, critical Enterprise applications increasingly require that data be filtered, transformed, aggregated and enriched before landing in an underlying data store.

Security

  • Perimeter security techniques and reactive post-mortems will be augmented with a myriad of technologies focused on data protection. Real-time monitoring and correlation across multiple security silos, coupled with analytics and AI-based security models, will lead to the successful prevention of breaches and fraud.
  • As Home and Enterprise IoT goes into overdrive, dumb routers will give way to smart gateways laden with analytics that can detect and prevent remote access, data theft, ransom-ware and other malicious attacks.
  • At least one internet-connected toaster will be infected with ransomware that randomly threatens to burn toast and catch fire unless a payment via bitcoin is made.

Industry Impact

  • Healthcare will emerge as a surprising leading beneficiary of the advances in IoT as real-time, sensor-based data is combined with geolocation, time series, patient profiles, and security-driven rules and algorithms to increase efficiencies and compliance, and speed care to patients.
  • In-memory processing, correlation and analysis will further speed the ability of real-time health systems to draw accurate conclusions and make sound recommendations to health care professionals, improving patient experience and outcomes.
  • The opportunity for extreme costs savings and productivity in the Agriculture industry make Agtech a likely candidate to leapfrog current technologies and catapult forward through the use of IoT, Cloud and in-memory computing strategies.

 

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