Data Engineering helps in determining the relevant datasets & preparing the data for consumption by AI Solutions to provide valuable insights to business. Data engineering constitutes almost 60% of work in AI Solutions. We, at RandomTrees help get your data AI-ready. Harness evolving data sources by automating & building robust capabilities for data cleaning and harmonization to feed machine learning models.
Organizations have to help businesses by building robust capabilities to deal with the volume, velocity, veracity, and variety of data. They have to make this data available for business users to consume. Data Scientists, with the constraint of handling massive data taken care of, will have the opportunity to work on these comprehensive data sets, derive valuable insights for business teams to implement decisions. This significantly increases the business value that can be realized.
Assist with identifying data sources, verify, cleanse, transform and integrate datasets to make them ready for ML model consumption.
Provide data governance framework that helps organizations to take care of the data they currently have, get more value from that data, and bring high visibility of data to users
Assist in powering new Insights with a high performing Data Lakes.
Suggest and help to setup data platforms and tools to manage data.
RandomTrees Data Engineers will help identifying data sources, verify, cleanse, transform and integrate datasets to make them ready for ML model consumption
RandomTrees data governance framework will help organizations to take care of the data it has, get more value from that data, and make important aspects of that data visible to users
RandomTrees assists in powering new Insights with a high performing Data Lakes
With its unique data strategy, RandomTrees will suggest and help to setup data platforms and tools to manage data
As part of strategy development, We assist with following:
Help to choose between open source vs vendor based platforms
Assist in choosing the right Hosting Strategy (on-premise vs. cloud).
Suggest overall data architecture & Big data ecosystem.
BIG DATA ARCHITECTURE
Build complex Industrial Pipeline (IDP’s) which are Data Factory like framework.
Variety of Data (Types, Sources & Volumes).
Faster data processing and handling repetitive tasks much quicker.
Design & build complex data lakes to continuously deliver
AI-ready data in near real time.
Bring breadth of data sources together to increase the
Power of AI & Analytics manifold.
Help establish a real time automated process
for data ingestion and processing.
Make data clean, compatible, comparable & reliable,
even when it comes from a wide range of unrelated sources.
Ensure data is ready for unsupervised
learning, supervised learning and advanced analytics.
Handle huge volumes of streaming data
(IoT, Social media such as Twitter, Facebook).
Process and analyze in real-time or near real time &
generate valuable insights such as customer sentiments,
Competitor intelligence,Realtime trends.
Help take right approach to improve latency, processing or
ease of operation.
Democratize data with strong yet flexible governance.
Provide data governance framework that helps organizations
to take care of the data they currently have.
Help make sure data is always secure but also
continuously flowing to your business.
Help you build complex Knowledge Graphs using Graph
DB’s which are fundamental to the modern AI systems.
Knowledge Graphs act like a memory to AI Applications
(Example: real-time recommendation & knowledge sharing apps).
KG’s respond with valuable insights by accumulating
contextual knowledge with each conversation using
connected data to understand concepts, infer meaning.