StreamScape Technologies
Turning Real-Time Insights into Intelligent Actions

It’s no secret that good information drives better decisions. To gain operational efficiency and a competitive advantage, enterprises invest heavily in analytics to extract powerful insights from heaps of existing data for faster and more accurate decision-making. But there’s a catch! While business decisions have to be made on the fly, stale, incomplete and missing data undermine the ability to obtain useful and timely answers, hindering advancements in business intelligence. StreamScape Technologies is uniquely positioned to confront the challenge of timely decision support with a robust combination of Real-Time Data Integration and Business Intelligence.

StreamScape offers powerful next-generation tools for real-time data integration and analysis—a Data Fabric—that captures data changes at the source and turns insights into intelligent actions by combining traditional BI with Stream Analytics and Machine Learning techniques. Powered by change capture technology the Data Fabric ingests information from disparate sources, filters, classifies and identifies data groups allowing users to create business rules for analyzing critical changes. Databases, files, messaging, big data sources and cloud storage data can now be analyzed in real-time providing decision makers with timely information where and when they need it.

“Any kind of data integration technology is predominantly trying to aggregate information from different sources to give organizations a 360-degree view for top-down decision making. This process is typically initiated after a fault or critical condition is detected and involves a set of manual analysis steps that delve into the data sources to figure out what changed, why it changed, and what happened,” explains Dmitry Lelchuk, President, and CEO of StreamScape.

The StreamScape approach takes this paradigm and flips it on its head, using the fabric’s change capture capabilities to identify what went wrong in real-time without human intervention. An automated, bottom-up approach to data-driven decision making eliminates data latency and offers real-time insight instead of hindsight analysis. “Our solution let users triggers business logic or machine learning algorithms to analyze and classify information in response to data changes, automating the decision-making process.

Real-Time Decisionware offers our customers a unique ability to see and analyze data as it moves through an organization allowing them to act on new insights as they become available

Real-Time Decisionware is a new type of technology that automates root-cause analysis and affects programmable, situational awareness,” says Lelchuk. The technology lets companies integrate, store, and analyze big data in-flight as it’s being created and changed. “We offer a unique ability to see and analyze data as it moves throughout an organization, for real-time decision-making.” he adds.

As a unique value proposition in data analytics, Lelchuk offers an example of a financial firm’s anti-money laundering (AML) initiative for real-time fraud analysis. To solve the problem and provide timely insight into potential violations, financial data moving between applications and warehouses is turned into streams using the fabric’s data connectors and change capture features. Data on the move is stored in special snapshots for probabilistic computation, allowing users to develop targeted models for real-time analysis. This makes it easy to identify fraud incidents, reduce the number of false positives and re-use models across implementations or even industries. The fabric’s real-time visualization tools also make it easy to see and work with critical in-flight data.

StreamScape’s long term focus is on expanding their reach into Healthcare, Manufacturing and IoT. The team is looking forward to bringing their technology to a broader audience. “Our technology footprint and ease-of-use makes it ideal for hybrid cloud or on-premise initiatives, yet it’s compact enough to run on some of the smallest devices. It is quite possible that a single data integration and analytics platform can bridge the gap between portables, cloud and the IoT space,” concludes Lelchuk.