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Big Data

Overview

Home to World Innovators in Big Data

The Columbus Region has a unique data analytics landscape populated by dozens of businesses and educational programs across multiple industries.

Home to more than 150 faculty working in data analytics, The Ohio State University has created a state-of-the-art data analytics program and undergraduate major. Majors specialize in biomedical informatics, business analytics or computational analytics in addition to core coursework.

In addition, companies like IBM have located in Columbus due to strong demand for data analytics services in the Region’s many retail, finance, insurance, logistics and manufacturing companies. The abundance of both entry level and experienced IT professionals have helped continue the Columbus Region’s rise as a center of big data expertise.

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Notable Big Data Employers

Company Generation Storage Processing Analysis Interface Interpretation Collaboratory
American Electric Power X
Alliance Data Retail Services X X X X
Aver X X X
Battelle X X X X
Cardinal Health X
Centric Consulting X X X
CAS X X X X
ComResource X X X
CoverMyMeds X X
CrossChx X X
Health Catalyst X X
IBM X X X X
ICC X X X X X
Impact Radius X
Infoverity X
Intelligent ID X
L Brands X
Manta X X
Nationwide X
Nationwide Children’s Hospital X X X X
Navigator Management Partners X X X X X
Neumeric Technologies X X
OCLC X X
OhioHealth X
Paxata X X
Persistent Systems X X X
Prevedere X X
Prosper Business Development X
Saama Technologies X
Soothsayer Analytics X X X
Source: Columbus 2020 Facilities Database

Generation:
Capturing raw data from external sources and transmitting to storage (i.e. sensors, Internet monitoring, digital tracking, RFID)

Storage:
Aggregating data and adding metadata for rapid locating and access (i.e. structured/ unstructured data warehousing, cloud-based server farms)

Processing:
Enabling capabilities that transform data into analyzable format (i.e. metadata sorting, matching and tagging, Hadoop)

Analysis:
Determining relevant findings out of optimized data based on user queries (i.e. data mining, sentiment monitoring, structured and unstructured algorithms)

Interface:
Turning analyzed data into digestible output for data consumer (i.e. data visualization, dashboards, manipulation tools, search engines)

Interpretation:
Use of interface output for decision or pre-decision steps (i.e. data analyst/SME advisory, consulting services decision support)

Collaboratory:
Member of the Columbus Collaboratory

One of US