Data & Analytics

Monetise your data and perform advanced analytics on it.

Data and Analytics Solutions:

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Enterprise Data Management:

We help companies create, manage, and disseminate data for all applications and processes. We ensure that our data management complies with regulatory framework. Our services also include master and metadata management.

Data Integration, Reporting, and Visualization:

With these services, companies can integrate data from various resources. This ensures that no information is missed out and companies get a holistic view of data. With advanced tools, we also help in converting data into easy-to-interpret visual reports and dashboards.

Analytics, Insights, and Intelligence:

We provide generic or customized services involving Big Data, predictive, and prescriptive analytics. Such services help understand customer datasets to spot trends, and identify opportunities for growth.

Benefits:

# Provide single source of truth to base decisions on
# Ensure consistency and quality of data being used for analytics
# Eliminate data retrieval problems
# Make decisions more efficiently with highly visual representation of data
# Use social media analytics such as media and behavioral nalytics
# Provide personalization to connect with viewers
# Track quality of content and viewership and get targeted advertising
# Help uncover new revenue streams

Data Platform and Architecture

Characteristics of a modern data platform:

Given the sheer scale and complexity of data today, it’s no longer enough for a modern data platform to process and store data.

# Enable self-service for a diverse range of users

One key aspect of a modern data platform is that it can be used intuitively by a wide range of users. This means that the platform should make it possible for all users to…
>>>Easily discover and analyze data within the platform
>>>Understand the context associated with data, such as column descriptions, history and lineage
>>>Derive insights from data with minimal dependencies on the data or IT team

# Enable “agile” data management

One of the major challenges in legacy data platforms is their complexity. Just getting access to data usually required setting up time-consuming ETL jobs. Modern data platforms aim to change that. With a well-built modern platform, data-driven decision-making should be able to move at the speed of business.
The two fundamental principles that govern modern data platforms are availability and elasticity:
>>>Availability: Data is already available in a data lake or warehouse. Modern data lakes and warehouses separate storage and compute.
>>>Elasticity: Compute is based on a cloud platform, which allows for elasticity and auto-scalability.

# Flexible, fast set-up, and pay-as-you-go

Modern data platforms are built in a cloud-first, cloud-native world, which means that they can be set up in hours, not years.
>>>Easy to set up — no lengthy sales process, demo calls, and implementation cycles. Just login, pay via credit card, and go!
>>>Pay as you go — no up-front payments and million dollar licensing fees. The “modern” stack is all about putting power in the hands of the consumer, i.e. paying only for what you use
>>>Plug and play — the modern data stack will continue to evolve and innovate, and are instead built on open standards & APIs allowing easy integration with the rest of the stack.

Data Governance and Management

Put the right data governance and management around your data.

Getting Started with Data Governance and Management

# What is the opportunity cost of not getting data governance right in terms of missed upside, extensive time lost in manually cleaning data, or incorrect and suboptimal business decisions?
# Who is leading governance efforts today, and what would it look like to elevate the conversation to the C-suite? Who should be involved?
# Where is governance most important? What domains and parts of domains does the organization most need right now?
# What governance archetype best fits the organization, and are current efforts aligned to that level of need?
# How can governance be accelerated by adjusting its focus and injecting iterative working concepts?
# Do you have the in-house capabilities to manage such a shift?