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Research & Startup








Key Facts

  1. Data, reliable data, drives the global economy, helps planning, policy formulation and economic development.  

  2. Data costs money.  Databases are major national and institutional assets that are capitalised. 

  3. We are in the era of Big Data characterised by Volume, Velocity, Variability, Veracity and Value of data (5 Vs of data).

  4. Data Mining is big business. Data is the new black gold.  

  5. Global databases are used for diagnostics and predictive purposes

  6. Data obtained from Nigeria will be useful to foreign Universities and Nigerian researchers will have the opportunity of collaborating with their foreign counterparts. 

  7. Through data analytics, the Centre will empower researchers to discover, disseminate, translate and capitalize new knowledge that could become the catalysts for creating start-ups incubated in the Technology Park.  These start-ups when matured into spin-offs would form MSMEs that contribute to GDP and economic development.  In this way, we create strategic innovation, commercialisation and entrepreneurial ecosystems.

Key Performance Indicators (KPIs)


There shall be measurement and evaluation of KPIs to monitor progress.  Such KPIs include:

  1. Number of Databases developed (such as oil pollution, kidnap cases, etc. like the COVID-19 database kept by the US University)

  2. Number of Thesis and Dissertation supported

  3. Research Grants

  4. Contract Research

  5. Data Mining and Data Analytics

  6. Publications in internationally indexed Journals

  7. Citations of publications, Total research output/Research citations (ISI Web of Knowledge)

  8. Improvement in University Ranking

The Startup

Developing new Mindset, new Skillset, new Dataset, new Toolkit, new Work Culture and Ethics, new Attitudes


DataLab is startup established at the Technology Park to offer professional services in Database Development and Data Analytics.


DataLab is set up to mine and analyse data to support research and innovation, policy formulation and implementation.  



  1. Data Mining & Data Analytics

  2. Data-centric problem diagnostics 



  1. Database Development 

  2. Repository and Data Storage

  3. Experimental Design and Statistical Analysis

  4. Quality Assurance

  5. Product Development

Key Facts & Performance Indicators

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