Gramener
Making insights obvious
Gramener Visualisation is a
fast, flexible product that lets
you tell the story of your
business, in pictures.
NON-INTRUSTIVE
INSIGHTFUL
SCALABLE
Handle terabytes of data
Gramener visualises
your data quickly
and non-intrusively
Pull in data from any source,
without IT intervention
Use the right visualisation to
tell the story of your analysis
Unlimited data. Near real-time.
With no manual intervention.
via non-traditional analytics
visualised in real-time
Gramener transforms your data into concise dashboards that
make your business problem & solution visually obvious. Our
designs, based on cognitive research, help your eye find
important messages quickly. Our experience guides your
Visualisations towards actionable decisions.
SYSTEM REQUIREMENTS
HARDWARE
2 core processor
1 GHz
4 GB RAM
500 GB hard disk
SOFTWARE
Windows XP / Windows
Server 2003 / Windows
Vista / Windows 7
DATA SECURITY
o Product does not hold any
data for storage
o Input data needs to be fed
explicitly - does not scan
sources or ports
o Processing does not need
remote or network access
o All outputs are saved to the
identified location only
Easily complies with any
organizational security policy
TECHNOLOGY STACK
ARCHITECTURE
Python 2.7 is the underlying language
Raw data is passed
to the server as a
plain text file
Numpy 1.6.1 for numerical computations
SciPy 0.10 for statistical transformations
Tornado 2.0 as the templating engine
Tabular 0.0.8 for loading tabular data
RSA 3.0.1 for license key validation
Cygwin for text file processing
Template
engine
… and transformed
via custom-built
templates.
Analytics
engine
Visualisation library
The output of the
process is a static
PDF or XHTML file
… which is shared
manually with endusers
VISUALISATION LIBRARY
Our library has an exhaustive set of custom visualisation charts and
templates across industries and functions :
• Telecom
• Sales & Marketing
• Manufacturing
• Finance
• Contract Farming
• Production
• Retail
• Procurement
SOME BUILT-IN STATISTICAL MODELS..
VISUALISATION MODELS
PREDICTIVE ANALYTICS
Pairwise correlation: Determine most / Linear regression: Determine if output
least influences amongst variables
increases in proportion to the input
Hierarchical clustering: Group
variables into related clusters
Discriminant analysis: Create a variable
that classifies input into distinct groups
Segmented averages: Categorize
Bayesian classification: Model to
metrics for most significant influencers probabilistically classify input into groups
Comparison of means: Determine
numerical significance of impact
Support vector machines: Perform nonlinear transformations for classification
ANOVA: Determine statistical
significance of impact of a variable
Auto regression: Predict future values of
variables based on past correlations
Principal component analysis: Identify Decision trees: Identify simple but
variables most influencing the output optimal rules for classification into groups
Spectral analysis: Identify seasonal
patterns in time-series data
Neural networks: Predict outcome by
modeling neural structure of the brain
Prioritised metrics: Depict
performance weighted by a priority
Markov chains: Predict a series by
combining chains of patterns in the input
contact@gramener.com
gramener.com
+91 40 6454 5009
blog.gramener.com
Hyderabad
Plot 9/2, Survey No 64
HUDA Techno Enclave
Phase 2, Madhapur
Andhra Pradesh 500 081
India.
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