The Flow Analytics framework is the single most powerful data analysis framework available today.
Flow allows you to design workflows which can solve and automate any data analysis challenge irrespective of scale or complexity.
Flow facilitates the end-to-end data analytics pipeline. Flow can access and consolidate data from any required sources, cleanse and prepare data faster than any other technology, perform advanced analytics, and provides a structured framework for the communication of analysis results to decision makers.
Flow is capable of:
Most technologies for performing data analytics (such as R or Python) were not built with business in mind. Flow provides a computational engine equivalent to anything that can be done with code, but is also a framework developed specifically for bridging the gap between computational data analysis power and the delivery of actionable results to business decision makers.
Flow focuses on analysis workflows producing results. The construct of results in the system ensures that data analysis workflows are structured to have clear output. Flow provides infrastructure for communicating analysis results to decision makers and for automating the delivery of data and insights.
Flows business-oriented analysis framework allows you to:
Flow's computational environment is more powerful and flexible than code. Code is often the go-to approach for solving truly tough data analysis problems. For complex data analysis scenarios, most companies turn to custom coded solutions using technologies such as R or Python.
Flow provides a better approach to the development, deployment, and management of data analysis solutions than these types of technologies. Flow's computational framework is what is called "Turing-complete". In computing theory, this essentially means that anything that can be done by writing code using a traditional programming language - Flow is capable of doing as well.
Flow's Cloud Connect development environment supports the rapid design and deployment of highly custom analytics solutions through an entirely different approach to computing. The development lifecycle of a data analysis workflow in Flow is on average 100x faster than attempting to architect an equivalent solution with code.
Flow is also vastly superior to custom-coded solutions when it comes to ongoing management of workflows and adapting your solutions as business requirements change. Flow can automate a solution to any data analysis problem. If you don't believe it we will prove it. Contact us for a demo here.
High performance data analysis workflows require being able to quickly aggregate and join data from different sources. Flow supports a multitude of data denormalization and set based operations which allow data to easily be linked and joined for analysis.
Flow allows users to perform any type of join, append, or lookup rapidly accelerating the time required to bring together data sets for analysis. Flow's generic computing engine can scale to process datasets of any size.
More than 90% of the work in data analytics is in the preparation and cleansing of data. Flow's data-point-in data-point-out generic expression evaluation engine drastically reduces the time required to prepare data.
Any data quality rules, data point transformation, or required feature extraction can be done with Flow. Flow provides a vast library of mathematical expressions, logical operations, time series calculations, text based functions, probability operations and more which can compute any required data point on the fly.
Flow provides a number of data quality and data validation summary functions, analysis of blanks, and descriptive statistic functionality which allow you to quickly detect outliers and anomalies and gain detailed understanding of the landscape of your data.
Flow accelerates data cleansing and data preparation tasks by being upwards of 50x faster than competing script-based technologies like R or Python.
Flow's data transformation capabilities allow you to:
At the core of Flow is a revolutionary hypercube computational engine. This native hypercube engine allows data to be linked and transformed into optimized objects for multidimensional analysis. Hypercube analytics allow you to generate any report or view on-demand and to evaluate any computational expression or statistical calculation in a multidimensional context against your data.
Flow's multidimensional hypercube objects also open vast new possibilities for advanced artificial intelligence and machine learning applications. Hypercube computation and analysis actions are embedded into workflows and can be executed in an autonomous context. Flow agents are capable of computing, mantaining, and delivering hypercubes to the cloud to provide continuously updated reports, views and dashboards.
Hypercube computation and Flow's high performance multidimensional statistical engine drastically reduces the time required to summarize data and deliver multidimensional reports. Flow is capable of doing all of this without requiring any speciailized IT resources or data warehouse infrastructure.
Flow's hypercube analytics engine allows you to:
Flow provides direct interfaces to the full IBM Watson and MS Cortana cognitive computing suites. Cognitive computation and artificial intelligence functions can be embedded into workflows in order to evaluate powerful cognitive analytics. Flow's cognitive interfaces allow for analysis such as sentiment analysis, keyword extractions, topic detections, image recognition, facial recognition and more to be used in your data analysis workflows.
Flow bridges the gap between business data access and these high powered artificial intelligence engines. Flow allows your business to immediately leverage these types of cognitive functions against it's data without having to worry about technical configuration or code.
Flow's plug-and-play capability with these AI engines makes artificial intelligence practical for everyone. Flow completely eliminates the bottlenecks associated with feeding data to these types of AI systems and allows your business to focus on AI implementation and results.
Cognitive analysis can be used in the context of autonomous workflows to create intelligent logic for processes running continuously on agents. These AI functions can derive new cognitive data points from existing data which can then be leveraged to create next-level autonomous decision structures and to drive more accurate predictive modeling techniques.
Flow's cognitive analysis and AI engine allows you to:
Flow provides machine learning and predictive modeling workflow actions. Flow's expanding machine learning library allows you to train neural networks, learn decision trees, bayesian networks, logistic classifiers and more. Flow provides operations for clustering and pattern discovery.
Learned predictive structures can be embedded into workflows and invoked in an autonomous context. This allows you to develop intelligent learned logic for your workflows. Machine learning models can be called upon on demand to produce up to date predictions and classify new data.
Flow's machine learning functions allow you to:
Flow's distributed agent architecture and Tesseract File System allow analytics workflows to be scaled across multiple environments. Clusters of machines can easily be linked up using Flow agents to support multi-machine parallel computing. Agents can orchestrate tasks and communicate information through the cloud to synchronize their data processing efforts.
Flow's parallel agent computing paradigm allows you to scale your analytics workflows to process data of any size. Flow's managed Agent Cloud environments allow you to deploy data intensive workflows without having to worry about setup or complex technical configuration.
Flow is the only distributed computing framework that requires absolutely no code. Flow completely eliminates technical bottlenecks associated with scaling data analytics tasks and orchestrating computation across distributed environments.
Flow emphasizes the delivery of results. In Flow, workflows produce results. Flow provides a clear framework for delivery and communication of analysis results to decision makers. Analysis results are easily grouped together into presentation-ready dashboards and reports.
Data analytics is not all about computational power, it is just as much about delivering and communicating analysis output in such a way that it can be easily interpreted and used to drive informed decision making.
Flow provides a user-friendly cloud portal and dashboard design enviornment which allows data analysts to efficiently organize and communicate their analysis results. Flow's dashboards and analysis results support a sharing and collaboration engine which makes it easy to distribute dashboards and reports across your organization.
Every data analysis workflow developed in Flow can be deployed to execute autonomously on agents. Automating data analytics with Flow allows your business to schedule critical analysis processes, reduce costs, and deliver continuous and up to date information to decision makers.