Oil & Gas Digital Slutions

Data Cleansing and Predictive Intellegence Technologies

Building The Next Generation of Intelligent Digital Platforms for Oil and Gas Companies

Functionality at a Glance

Comprehensive predictive analytics capabilities

Maximize equipment utilization
Speed decision making and increase agility

Statistical analysis and visualization

Decision management and intelligence

Predictive modeling and data mining

Model development, test, and automation platforms

Data Sciences machine learning solutions

Prescriptive analytics and intelligence solutions

Mobile access to dashboards, reports, and spreadsheets

Data Cleansing, extraction, and visualization solutions

Rules-driven data correction using algorithms & data rules

The First Genesis Data Cleansing and Predictive Analytics solutions brings together advanced analytics and data maintenance capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text anlytics, optimization, real-time scoring and machine learning.  Our Analytics tools help organizations discover patterns in data and understand what events have occurred or anticipate what events are to occur.  Our Data Cleansing tool extracts the necessary customer data from the source systems with connectors, cleanses postal data, enriches existing records with addtional information, and identifies duplicates with their complex consolidation.

Our Data Cleansing Solution

Mastering and applying the techniques and disciplines of the full lifecycle data maintenance process helps ensure that the value of our customer's IT investments are maximized and that costly delays are avoided.  First Genesis works with its customers to implement holistic data cleansing solutions from integration to post migration, ensuring consistency throughout all database or storage environments.
Our Data Cleansing platform provides an entire ecosystem including processes, platforms, algorithmic applications, and governance, enabling customers to make better business decisions using quality data, more efficiently manage inventories, improve your order tracking, delivery accuracy and ensure your direct marketing and customer relationship activities are effective.  All of this comes from the reliable data achieved by maintaining quality data with Data Scrubbing Tools.
Our Data Cleaning solution features include:

  1. Data Profiling Engine - Provides a data extraction and visualization platform that enables you to understand the structure, semantics, content, anomalies, and outliers present in your data, and derive data rules that will later be used within your data warehouse
  2. Data Correction Engine - Custom data rules applied to your data, and generates digital correction mappings to cleanse and transform your data
  3. Data Governor - Takes your data rules and monitors the queality of subsequent data tools

AI and Machine Learning Customer Scenarios

We deliver Artificial Intelligence and Machine Learning solutions to customers within the Energy/Oil and Gas space surch as ExxonMobil, Schlumberger and Fortune organizations.  For one ofour recent analytics and machine learning engagements that focused on a predictive equipment maintenance scenario, where we integrated data from multiple disparate data sources including sensor readings, timestamp, historical, and real-time analytics and built learning models to predict at the time "t", using the data up to the time, whether the equipment will fail in thenear future.

Another use case involved our ability to learn about user behavior within an educational and safety portal environment so that the company could build predictive and intelligent expriences on demand.  We aggregated data from real-time and fixed interval data sources.  Summarized that data by categoring that data through clustering, and then prioritized the data by weight against all categorized data.  We then assigned a binary sentiment based either negative sentiment or positive sentiment, wihich provided us with the3 ability to classify content based on uncovering latent (unknown) inputs like sentiment based on the clustering of textural data.
For a final use case, customer required learning models  fro a Contract Analysis scenario that leveraged analytics to provide insights and efficiencies to contracting processes, categorizing their content to allow quciker understanding of their terms and comparison between contracts (detailed and summarized), providing search capability basede on topic/context, allowing to build taxonomy and to cluster documents, being able to extract structured information (e.g. geographical data) out of unstructured content and maintaining legal references up-to-date.  We leveraged textural data and interpret it like a human reading a newspaper would interpret it, and used decision tree analysis and data classification methods to summarize or classify read text based on (known) categories.