Overview

Sieger & Co created a mobile application for an Energy Company in Nigeria. This mobile application enabled them to transform and query their operational data.

It was designed to speed up data retrieval by enabling employees to access and manipulate field data using simple natural language.

Natural Language Processing (NLP) is a field of artificial intelligence that aims to give computers the ability to process and understand natural human language. Recent increases in available computing power and advancements in data science have led to breakthroughs in the practical application of NLP to real world problems. As a result, NLP has been applied to everything from sentiment analysis to the creation of chat-bots.

Oil and Gas companies store various kinds of data associated with their assets and operations (typically in databases) from which it can be retrieved by stakeholders as required. The process of retrieval and transformation of data for manual analytics can often be time-consuming and NLP can speed up this process.

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Challenge

Our solution

The company wanted to speed up data retrieval and analytics from its proprietary database
To answer questions using the company's data, staff typically determined what data was required to answer the question, then connected to the company's proprietary database to extract the required data. After extraction, this data was then transformed or reduced in order to arrive a solution.

Sieger & Co created a mobile application to simplify this process. Using NLP, the application analyzes input queries to determine the data required to fulfill a given request, retrieves it from the database, and then carries out any reductions or transformations necessary to arrive at a solution. This abstracts away any complexity inherent in queries for data and greatly simplifies and speeds up the process. All data transmission is encrypted using SSL and users can only access data for which they had been granted permission by the company's IT department.

Results:

The implementation of this solution drastically reduced the time and effort required to derive intelligence from business data. The application saw widespread use by technical team members because it allowed them to very quickly and accurately answer questions pertaining to their assets in internal and external discussions.

Future Work:

The implementation of this solution led to requests from other non-technical departments for an extension of the solution to their own data. Work is currently underway to extend the application to cater these requests.

Technologies:

Java SE (Backend and Android App Development), Stanford NLP (Natural Language Analysis/Processing), Java Spring (Webservices Development), Angular (iOS and Web App Development), SQL (Database Development).

Let us transform your operations.

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