Big Data

GM Big Data service

Big data analytics can deliver massive value, but too often companies let technology guide their efforts. Instead, decisions must be based on business priorities.

 

Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.

Big Data
overview

Big data helps customers Improve decision making, Understand its customers, Improve customer offering, and finally Improve its operations.

 

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. These massive volumes of data can be analysed and used to address business problems you wouldn’t have been able to tackle before.

What is it?
Volume
The amount of data is immense. Each day 2.3 trillion gigabytes of new data is being created.
Velocity
The speed of data (always in flux) and processing (analysis of streaming data to produce near or real time results).
Variety
The different types of data, structured, as well as, unstructured.
Value
Understand the costs and benefits of collecting and analyzing the data to ensure it can be reaped and monetized.
Veracity
The quality or trustworthiness of the data. Understand the accuracy of all this data.
Who is this for?
Your business needs a competitive edge and you’re not quite sure where to find new insight into how to get there. Change comes from businesses knowing more, not through new software.
Businesses looking to boost their sales and grow their customer base, e.g. by further narrowing down customer type and by looking to improve through more targeted marketing, PR, sales and advertising.
Companies who already have a wealth of older customer data resources to mine, which is doing nothing for the business being stored in older or even offline formats.
A need to react and respond directly to lots incoming data from customers, e.g. email dissemination and categorization.
Improved data quality
Improved operational efficiency
Improved employee satisfaction
How it works
steps

Preliminary discusion, business goal identification and clarification

  • Anchor to business value
  • Pragmatic approach to IT

Initial data source assessment and verification

  • Analysis, identification and escalation

Definition of new data structure based on existing and incoming sources

Data manipulation and reorganisation

Big Data analysis and reporting

  • Implementation of insights into key areas of business as agents of change

Review results, change parameters and repeat the process.

Prerequisites
Client Cases
Practices

Agile and prototypical approach, establishment of data governance standards, understand the data quality tradeoffs between in-stream, real-time, and batch analytics.

Tools

Visualization, data mining, Storage DB, Data processing, Data architecture

Customer
Industries
Automotive
Solutions for FinTech businesses
ERP solutions, system integrations, applications and modifications
People
Anastasia S.
Data Analyst
Oleksii K.
Development Engineer
Oleksandr K.
Development Engineer
Iuriy K.
Development Engineer
Vitaliy O.
Development Engineer
Sergey B.
Development Engineer
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