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Advanced Analytics

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Visual Analytics


Discovery & Analysis



Understanding the data is key to asking right questions to gain valuable business insights and hidden patterns.



Easy unhindered exploration through visual interaction enables investigating deeper connections with in the data, especially by business analysts or domain specialists who know the data.



hiddime works with traditional BI Data Warehouses and modern Hybrid BI Data Warehouses and extends the seamless exploration to connected graphs, semi-structured data, etc.

Analytics on-cloud, or on-premise, in the browser by business analyst


Target Users

Frontline Business Managers
Domain Experts & Data Analysts
Data Scientists



Domain Independent and Self service
POINT & CLICK Interface
Automatic Narrative Analytics


Remote collaborative analyst workgroups on-demand

Big Data presents vast amounts of data to be understood and analyzed.

Teams are essential for a comprehensive approach to analytics and to be sure that possible data patterns are not left out

Seamlessly adding analysts to workgroup who can work remotely on data (without moving data) goes a long way in extending the analytics effort and improving the outcomes

head-bg Concierge Analyst Service

Business Analytics

Remote Data Analyst

Data Prep & Ingest

Remote Data Engineer

Predictive Analytics

Remote Data Scientist

Product Updates


At Hiddime we believe in constantly discovering and inventing our product. Our efforts to re-engineer to newer versions is driven by market demand and our findings from our customer engagements. True to our belief in maintaining an easy to use product regardless of the end-user's proficiency in technology, we also try to simplify our product and solutions. In the most recent release, version 1.0.5, we incorporated mathematical functions at a table level, multiple new chart formats and made our flagship feature, the connected graph, a bit more easier to navigate.


Connect Art of Business with Science

Over the last decade the data land scape in the enterprise has rapidly changed along with changing user and market expectations attached to the BI and Analytics off the data that is newly available. The unprecedented and exponential growth of sophisticated internet/web enabled consumer devices (smartphones, etc.) that are capable computers themselves, generating vast amounts of data and powerful computer and communication technologies along with potent software techniques such as graph processing and machine learning has enabled ever-complex processing work-loads generating deeper insights – defining characteristic of Big Data!

In this new scenario, erstwhile tabular and structured data that was mostly relational has been steadily giving way to non-tabular and non-relational data. New data is more complicated and unstructured forcing processing systems to handle new and difficult data types such as non-tabular, text, images, audio etc.
Big Data is characterized by huge amounts of unstructured data that is being generated and collected within the enterprise. This data includes weblogs, equipment logs, sensor logs, email, photos, voice files, videos, images, documents, social data, geo-location, maps etc. in addition to the transactional data.
The new analytics off Big Data world are far more sophisticated than the reports and dash boards of the older BI world as the data to be processed is far more complex and integrated over varied inputs and conclusions drawn are far more involved requiring employing complex techniques such as graph processing, pattern recognition, machine learning, etc.
The resulting analytics won’t necessarily fit into the visualization and interaction schemes of the older BI tools, leaving a market opening and a second major opportunity for new entrants to deliver appropriate visualization and interaction support in their tools.
More recently, adding SEMANTICS to data during the ingest process (process of turning Big Data into Smart Data) has been proven to enhance manageability and governance of data as well as proven to reduce ETL work and frees up data scientists to maximize using their special skills to work on the advanced analytics that the organization wants delivered. Another major benefit of adding SEMANTICS to Big Data is that rich Knowledge Bases that exist in virtually every domain can be integrated and leveraged for deeper and more integrated analytics. Semantic integrated environments also drive up automation levels resulting in further efficiencies within the organization.
Advanced enterprise analytics has become the new go-to source of guidance for operational direction within the modern enterprise to successfully wade through competition to preserve and improve market share.
Staying ahead or leading the pack means companies now more than ever are forced to constantly scrutinize the entire data and processing infrastructure of the organization looking for opportunities to incorporate efficiencies due to ever modern yet proven technologies, techniques and practices.
Hiddime is a new age BI and Analytics tool that has as its product focus the two areas: 1. Ease of Use and exploratory tool for the end consumer of BI and Analytics 2. Visualization and Presentation of newer, higher order analytics – predictions, detecting correlations from manifest connections, etc
Hiddime is an IDEA tool, Interactive Discovery and Exploratory Analytics tool. Hiddime differentiates itself from the usual BI tools with its focus on ease-of-use. Our emphasis on making the user interaction sessions seamless and simple has resulted in least learning curves for the business user. Hiddime is domain and industry agnostic. Hiddime works with different backend storage formats like SQL, NoSQL, Property Graph or RDF Triple Store databases. AllegroGraph from Franz is used as the storage backend for RDF data storage. Cloud deployments are limited (for now) to 100GB of user data, which will work with standalone versions of AllegroGraph on a single Amazon Machine Instance. On-Premise versions would leverage the AllegroGraph cluster, which forms the Smart Data Lake. AllegroGraph also holds ontologies and knowledge bases relevant to the domain of the data being analyzed. (also read:
The capabilities of Hiddime extended by Data Science concepts and enhanced by the power of AllegroGraph help:
1. Derive new knowledge which will be integrated into SEMANTICS layer for future use - 2. Integration of Domain Knowledge in order to make the exercise computationally less expensive 3. High level information retrieval which is not possible with eye balling (system level patterns traversals are hard to glean from raw data) 4. Significance of high level structures in SEMANTICS layer

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