Enterprise Data & Information Management

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“We help Companies orchestrate towards an improved customer experience and increased revenue”

Enterprise Data & Information Management

Analytics & BI Enablement Primer – CFECTIV-008B23

The Converged Data Architecture ® – (Enterprise Digital Transformation Enablement)

Leveraging : RPA, Automated Data Quality Assessment & Monitoring and Data Virtualization

(OIL & GAS / ENERGY)

“Oil & Gas / Energy companies can leverage the Converged Data Architecture® to maximize yields & reduce risk throughout the supply chain”

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The Solution

 An Enterprise Data and Information Management architecture that supports all data and information management patterns within any organization. This solution enable organization across all verticals to Ingest, Store, Govern, Integrate, Analyze, Contextualize and Disseminate all data across the enterprise enabling them to deliver faster time to market and in so doing become a digitally transformed organization.

 All Enterprise Data Management Patterns are aligned with a common Data and Information Management Eco-System that will make seamless the journey towards becoming a Digitally Transformed organization.

 This solution enable enterprise architects to easily create environments for all

data consumers to access the data they need and trust in near-real-time, in a

format they understand, utilizing any tool/device they desire – Enterprise data

availability anytime, anywhere.

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Data and Information Management Eco-System – Reference Architecture

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The Converged Data Architecture® has evolved significantly to meet enterprise requirements, and now encompasses the functional areas that are foundational to a modern Data & Information Management solution.

Data Management Store and process vast quantities of data in a scale-out storage layer

Data Governance and Integration

Quickly and easily load and integrate data, and manage according to policy.

Data Access

Access and interact with your data in a wide variety of ways — spanning batch,

interactive, and real-time use cases.

Security

Address requirements of Authentication, Authorization, Accounting and Data Protection.

Operations

Provision, manage, monitor and operate Data & Information

management clusters at scale.

The Converged Data Architecture®: Enterprise-class, Enterprise-ready

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Oil Gas Companies – Maximize yields and reduce risk in the supply chain

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The United States is enjoying resurgent fossil fuel production. In fact, the International Energy Agency estimates that as of 2016, the U.S. will surpass Saudi Arabia and Russia to become the world’s largest oil producer.

At the same time, total world oil production has ceased to expand. In a testimony before the United States Senate Homeland Security and Governmental Affairs Committee, T. Boon Pickens stated, “World oil production, I believe, has peaked, and the world’s current oil fields are declining at the rate of 8 percent a year.”

These fundamental changes in the global hydrocarbon markets drive more production to a focus on shale and other less-accessible deposits. The oil and gas industry must increase CAPEX investment to identify and extract those new deposits while simultaneously reducing the environmental, health and safety risks of bringing that resource to market.

Universal changes in the availability of data are also underway, changing the petrochemicals business in ways similar to changes in telecom, retail and manufacturing. Advances in instrumentation, process automation, and collaboration multiply the available volume of new types of data like sensor, geo-location, weather and seismic data. These can be combined with

“human-generated” data like market feeds, social media, email, text, and images for new insight.

This reference architecture shows how oil and gas clients can use Apache Hadoop to make the most of these changes.

The reference architecture shown in slide three (3) represents a combination of approaches that oil and gas companies can adopt in order to Ingest, Store, Govern, Integrate, Analyze, Contextualize and Disseminate their data and make most of the changes alluded to above.

Oil and Gas Applications include:

 Integrate Exploration with Seismic Image Processing

 Optimize Lease Bidding with Reliable Yield Predictions

 Define and Monitor Operational Set Points for Wells

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Better data enables smarter drilling. But anyone with a digital camera or a smartphone knows that images gobble up storage capacity—and those are tiny images, compared to detailed seismic maps.

Three-dimensional seismic maps help oil and gas companies know where to drill, and the Converged Data Architecture® is ideal for Ingesting, Storing, Governing, Integrating, Analyzing, Contextualizing and Disseminating images with their metadata. Storing and integrating seismic data from multiple experiences permits learning in the aggregate across all of those experiences. This improves a firm’s long term return on investment, across multiple projects.

Applications in Oil & Gas / Energy - Integrate Exploration with Seismic Image Processing

Oil Gas Companies – Maximize yields and reduce risk in the supply chain

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Applications in Oil & Gas / Energy - Optimize Lease Bidding with Reliable Yield Predictions

Oil and gas companies bid for multi-year leases to exploration and drilling rights on federal or private land. The price paid for the lease is the known present cost paid to access a future, unpredictable stream of hydrocarbons.

The well lessor can outbid his competitors by reducing the uncertainty around that future benefit and more accurately predicting the well’s yield. The Converged Data Architecture® can provide this competitive edge by efficiently Ingesting, Storing, Governing, Integrating, Analyzing, Contextualizing and Disseminating image files, sensor data and seismic measurements. This adds missing context to any third-party survey of the tract open for bidding, and enables the company to confidently bid or pass on a lease based on yield predictions.

Oil Gas Companies – Maximize yields and reduce risk in the supply chain

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Applications in Oil & Gas / Energy - Define and Monitor Operational Set Points for Wells

After identifying the ideal operating parameters (e.g. pump rates or fluid temperatures) that produce oil and gas at the highest margins, that information can go into a set point playbook. Maintaining the best set points for a well in real-time can be achieved by leveraging components of the Converged Data Architecture® - (real-time analytics and alerts etc..). Oil and Gas Companies may choose to monitor variables like pump pressures, RPMs, flow rates, and temperatures, then take corrective action if any of these set points deviate from predetermined ranges. Leveraging components of Converged Data Architecture® will help well operators save money and enable them to adjust operations as conditions change.

Oil Gas Companies – Maximize yields and reduce risk in the supply chain

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TEXT

Technology Recommendation

Patterns Properties Current State Ingestion

Storage & Governance Integration Analyzation Contextualization Dissemination

Batch Stream (Fast) Hot Data Cool Data Archiving

IoT Data Management

Fast data Ingestion Quick Scan Streaming Analytics ACID (For some use cases)

Identify Common Data Ingestion Engine - SFTP

Common Data Ingestion Engine - IoT Hub

Big Data Technology based on Volume, Velocity and Variety

Governance: RPA, DQAM Big Data Technology based on Volume, Velocity and Variety

Big Data Technology - Blob

Abstraction Layer

Abstraction Layer Abstraction Layer

MDM Structured Data

QA * Data cleansing capabilities (Deduplication, business rule validation etc.)

Identify Common Data Ingestion Engine - ETL, ELT

Common Data Ingestion Engine - ETL, ELT

MPP- RDBMS, Big Data Technology based on Volume

Governance: RPA, DQAM

Abstraction Layer

OLAP ACID or CAP

Large aggregations Quick Scan MPP (Massive Parallel Processing)

Abstraction Layer

OLTP ACID or CAP

Transaction Control Support for multiple inserts and updates

Abstraction Layer

Advanced - Analytics NLP ML AI

Identify Common Data Ingestion Engine ELT

Common Data Ingestion Engine ELT

MPP- CDBMS, MPP- RDBMS Big Data Technology based on Volume, Velocity and Variety

MPP- CDBMS, MPP-RDBMS Big Data Technology based on Volume, Velocity and Variety

MPP- CDBMS, MPP- RDBMS Big Data Technology based on Volume, Velocity and Variety

Abstraction Layer

Archival Structured Data Unstructured Data Support Compliance (Corporate Retention Policy)

Big Data Technology – Blob

Governance: RPA, DQAM Big Data Technology - Blob

Big Data Technology - Blob

Abstraction Layer

Data Lake Structured, Semi- Structured, Unstructured Data Staging Area.

Support schema on read.

Multiple methods of consumption based on needs.

Machine Learning.

Governance: RPA, DQAM Big Data Technology based on Volume, Velocity and Variety

Abstraction Layer

Supported Enterprise Data & Information Management Patterns

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Business Capabilities

Business Processes

Any Use Case

IoT Data

MDM

OLAP

OLTP

Ingestion Storage &

Governance

Integrate

Analyze Contextualize

Disseminate

Abstraction Layer 1.SFTP

2.IoT Hub Big Data

1.ETL 2. ELT

1. RDBMS 2. Big Data

1.ETL 2. ELT

1. RDBMS 2. Big Data

1.ETL 2. ELT

1. RDBMS 2. Big Data Y

N

Y

N

Y

N

Y

N Identify Data Sources

Note:

Specific components of a Common Data Ingestion Engine will be utilized for the ingestion of data from various data sources based on type ( fast, batch etc..) Governance: Will be facilitated via data anomaly detection and management - (Assessment,

Remediation and Monitoring of source and other stored data systems leveraging the DQAM and RPA technology)

See CloudFectiv Enterprise Data and Information Management (EDIM) Patterns Spreadsheet - Use Cases Design Patterns for additional information.

Mapping of Data Management Use Cases & Capabilities

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Towards the Digitally Transformed Organization

 11 Advanced Data

Anomaly Detection - ( Assessment, Remediation and Monitoring)

How does the Converged Data Architecture® help?

Dynamic Storage / Population

Management

(Ingestion)

Seamless Integration of stored data sets

Near Real-Time Visibility and Consumption of Enterprise Operational and other Data Sets.

“ Organizations who embrace this architecture will have an improved customer experience and increased revenue. It will change the way they work, they will be able to devote more resources to Advanced Analytics to include Machine Learning, Artificial Intelligence and NLP Driven Analytics.”

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 CloudFectiv Enterprise Data and Information Management (EDIM) Patterns

 Working Spreadsheet – Circa -2017 – 2021 (Engage to Learn More)

Supporting Documents

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