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Energy-Specific Master Data Management & Quality Assurance

Create a Single Version of the Truth to Power Decisions with Validated and Calibrated Energy Data

Ensure Data Quality

As data flows in from EnerLink integrations, EnerCore automatically applies business rules and QC checks with exceptions instantly routed to data stewards for cleanup using a data quality dashboard.

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Orchestrate Big Data

Specify merge rules to combine even the largest datasets from multiple sources, filter, deduplicate, restructure, and match data across systems using a variety of built-in algorithms.

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Master Data Management

Easily create a common data model for your organization’s data based on industry standards(e.g., API 14 well numbering) and your own standards (e.g., well naming) to ensure consistent data flow across applications and clean, analytics-ready data formatting.

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Write Back Golden Records

Accelerate workflows and decision-making with the correct version of the truth by automatically writing back master data to source systems while linking transactional data, unstructured records, and documents (e.g., well files) to golden records.

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EnerCore Results

Time Savings

Gives your entire team back more of their day by eliminating manual data wrangling and clean up.

Uplift Margins

Data-driven cultures are 58%more likely to beat revenue goals and uplift margins.

Lower OpEx

66% of enterprise companies who prioritize big data management cut operational costs.

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Have questions? Reach out now to schedule time with our data management and EnerHub experts. Or download our whitepaper to learn how EnerHub can accelerate business performance across the energy value chain, uplift margins, and set your team up to take full advantage of next gen analytics and AI.

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Top EnerCore Features

Business Rule Library

Jump start data quality with EnerCore’s pre-built QC checks that draw on 30+ years of energy data management experience.

Data Quality Dashboard

At a glance, view the health of your datasets and any outstanding issues that need the attention of data managers.

Data Profiling

Generate analytics on your dataset, view volumes by data type, and get recommendations for improving data quality.

Business Rule Builder

Easily create additional rules to enforce your unique data quality requirements.

Load Manager

Load data from source systems in real time or Schedule batch load directly into EnerCore or a data lake (ELT or ETL).

Golden Records

Manage master data according to industry and company standards (e.g., well header) and attach transactional data (e.g., production volumes and completion data) to the Golden Record.

Data Shop Talk

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Data is the NewBlack Gold

Like oil, data is a commodity with tangible value. The new black gold costs organizations millions of dollars to produce (think well logs, SCADA, leases, etc.). Importantly, energy companies make billion dollar decisions on their data, however, like gold, getting your hands on the right data to make crucial drilling or M&A decisions is ironically difficult as energy professionals are often knee deep in it. E&Ps and midstream companies need to get as good at gathering, processing, and distributing data as we have become with the physical movement of hydrocarbons.

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Frac Your Data for a Free Flow of Energy Intelligence

The digital oilfield is characterized by information sprawl, a landscape of data silos that span corporate network drives, databases, and the cloud. In the past, this was met with large data migration projects that attempted to warehouse an energy enterprises’ disparate data in a monolithic way. These types of projects, while still relevant in certain contexts, are expensive and slow to adapt to evolving business needs. But are data silos bad?

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Golden Records in the Digital Oilfield

“Multiple versions of the truth” is a phrase you often hear when talking about oil & gas data management. But what does this really mean? Substitute for “truth” any number of data types, like geological interpretations, land files, production volumes, or offset well data. In the digital oilfield with ever increasing volumes, velocity, and variety of data, we all too often contend with multiple sources (or versions) of data related to the same business object as well as duplicate datasets. The process of picking a single version of the truth to use in analysis is where master data management starts.