Presentation on theme: "CHALLENGES OF DATA MANAGEMENT IN THE 21 ST CENTURY UNIVERSITY SYSTEM BY: PROFESSOR E.R.ADAGUNODO DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING OBAFEMI AWOLOWO."— Presentation transcript:
CHALLENGES OF DATA MANAGEMENT IN THE 21 ST CENTURY UNIVERSITY SYSTEM BY: PROFESSOR E.R.ADAGUNODO DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING OBAFEMI AWOLOWO UNIVERSITY ILE-IFE, NIGERIA Being Paper presented at the 2014 Training Workshop/Conference and Annual General Meeting of the Committee of Directors of Academic Planning of Nigerian the National Mathematical Centre, Kwali, Abuja Between November 2 nd to 7 th 2014
P ROTOCOLS Standing on Existing Protocols It is with great pleasure that I am here today to brainstorm and share experience on the topic “Challenges of Data Management in the 21 st Century University System” I crave your indulgence to express my sincere appreciation to the Chairperson, Secretary, and the entire Membership of CODAPNU. Also, I wish to thank the Organizing Committee of the 2014 Training Workshop/Conference for inviting me As a Resource Person to speak on such an important topic.
P RESENTATION O UTLINE Protocols Presentation Outline Introduction Sources of Data in a University Issues Facing University Academic Planning The Big Question Some Challenges of Data Management References
I NTRODUCTION Building a 21 st Century University is all about Data and Effective Data Management. It is usually said that “Information is Power” It can actually be said now that “Useful Data is Power”. Technically, the definition of Information is processed data
I NTRODUCTION The survival of any organization including a University is about data available for effective decision making. For effective and meaningful management of data and to achieve results, it is pertinent to understand the challenges facing data management and data Managers Especially, talking about the 21 st Century phenomenon that is replete with so many development-oriented or technology-related challenges
S OURCES OF D ATA IN A U NIVERSITY (Osasona, 2012) in his Paper titled “Tools for Academic Planning” identified the following as sources of data for academic planning: 1. Students numbers: Headcount or Fuul-Time Equivalent (FTE) 2. Academic Brief 3. Resources : Human Resources (Academic Staff, Senior Technical Staff, Senior Administrative Staff, Junior Staff)
S OURCES OF D ATA C ONTD Financial Resources (Income and Allocation) Grants & Subventions, IGRs, Sponsored Research Grants, Foundations, Allocation of funds for Goods and Services, Allocation of funds for Teaching and Research Equipment) 4. Quality Assurance 5. Programmes 6. Management (financial, personnel, materials, physical, funding Accreditation (both NUC & Professional Bodies)
I SSUES F ACING U NIVERSITY A CADEMIC P LANNING Globalization and explosion of academic records with Open & Distance e-Learning, M-Learning, U-Learning. Teaching in space as demonstrated by Chinese Astronauts Webometric Ranking of Universities (Awosusi, 2013) highlighted the results of 2013: Jan 2013 (Times Higher Education), the best 3 Universities in the world are from USA and UK
I SSUES F ACING A CADEMIC P LANNING C ONTD July 2013, the best 8 Universities in Africa are from South Africa July 2013, The 1 st ranked University in Nigeria is the Obafemi Awolowo University, Ile-Ife. It ranked 14 th in Africa and 1511 th in the world !!! Others are FUNAAB ranked 29 th in Africa and 2398 th in the world; UNN ranked 40 th in Africa and 2827 th in the world; FUTA ranked 100 th in Africa and 5643 rd in the world The Big Question is:
What do we do to improve our global visibility and ranking? The indices of ranking are there and only Universities with great foresight, vision, being proactive and penchant for meaningful Academic Planning There is the need to restructure Data Management System in the Universities to meet the 21 st Century demands on Universities: Internet access and availability, global visibility,
S OME C HALLENGES OF D ATA M ANAGEMENT : Q UALITY Data quality (consistency and correctness) is an issue. The more data that is accumulated, the harder it is to keep everything consistent and correct. With really large data sets accumulated over time (which means that things change-- what was once correct may not be correct any more, and vice versa), a Data Manager has to solve for garbage in/gold out and prevent gold in/garbage out.
D ATA C HARACTERIZATION Adequate data characterization (metadata to the geeks) is critical. How to deal with data -- even how to choose to organize its storage -- requires the Data Manager to know how much data there is going to be and how fast it's likely to grow and change. A query that runs well to find 100 rows in a million-row table may not run well on 100 billion rows. It matters how to flag and track errors. Logging and auditing matter if the data changes frequently (highly dynamic)
D ATA I NTERPRETATION Data interpretation remains more of an art than a science -- or a science accessible to only a few trained specialists. Software developers have had to design efficient filters and pattern recognizers that can sift through mountains of data and find (perhaps unanticipated) patterns that are relevant to a dimension of interest – data mining, etc
D ATA V ISUALIZATION Data visualization -- representing results in an easily consumable form -- is critical. What good is all that data if it can not be understood from what the interpreters-- human or software--concluded from their analysis.
R EAL -T IME V IEW OF D ATA A real-time view of the data (which may mean having to continuously re compute everything whenever the data changes). This also requires immediate response to queries and requests such as online Academic Transcript requests by Alumni of the University
T IME - FACTOR OF D ATA S TORAGE How to know in advance how long the data is relevant or valuable? Data costs money to acquire, store, analyze and back up. A retention policy beyond a typical "keep everything forever" approach is needed, and that policy has to be enforced.
D ATA M ANAGEMENT IN C LOUD A PPLICATIONS The challenge of building consistent, available, and scalable data management systems capable of serving petabytes of data for millions of users has confronted the data management research community as well as large Internet enterprises. Current proposed solutions to scalable data management, driven primarily by prevalent application requirements, limit consistent access to only the granularity of single objects, rows, or keys, thereby trading off consistency for high scalability and availability.
D ISTRIBUTED D ATA M ANAGEMENT In a distributed data management system, data is presented as a system that is physically separated (natural) but logically centralized. It requires an Operating System support that is able to manage resources globally even when such resources are physically located at remote sites.
T HE B IG D ATA : V OLUME, V ELOCITY AND V ARIETY OF DATA The previously nebulous definition of “big data” is growing more concrete as it becomes the focus of more applications. As seen in Figure 1 (below), Data exists in disparate and disjointed form There is the need to bring them together in the Big Data. volume, velocity and variety make up three key characteristics of Big Data:
Fig 1: The Three Characteristics of Big Data : Source IBM
V OLUME Rather than just capturing business transactions and moving samples and aggregates to another database for analysis, applications now capture all possible data for analysis.
V ELOCITY Traditional transaction-processing applications might have captured transactions in real time from end users, but newer applications are increasingly capturing data streaming in from systems, media or even sensors. Traditional applications also move their data to an enterprise data warehouse through a deliberate and careful process that generally focuses on historical analysis. Data streaming, Tele Conferencing and Video Conferencing require fast-moving data to achieve real- time computing
V ARIETY The variety of data is much richer now, because data now comes from varied sources and media, machines, sensors and unrefined sources, making it much more complex to manage. The variety of data that the new class of databases has to manage is arguably their most fundamental challenge
D ATA S ECURITY Big Data requires data to be stored in a large and central repository which will pose a threat to the integrity of the institution’s data and require explicit access rights, privacy guaranteed system, building fire walls against hackers and intruders using data encryption and biometrics
H UMAN C APACITY Training and retaining competent personnel with specialised skill sets needed for effective and qualitative data management is an issue for managers to consider: Competence for forecasting and predictive models, MIS, EIS, Expert Systems, Decision Support Systems, development of e-learning modules.
R EFERENCES Adedipe, Abiodun (2014), The Dynamics of Strategic Analytics in a Contemporary Economy: The Role of Information Technology. 4 th College Lecture, College of ICT, Bells University of Technology, Ota. Nigeria Akinlabi, P. I (2013): Effective Record and Information Management in Higher Education Institutions. Paper delivered at a Workshop for all Categories of Administrators held at the Federal University of Technology, Akure Awosusi Omojola A. (2013): Repositioning Higher Education Administration in Nigeria: Meeting the Global Challenges. A Paper presented at the 25 th Annual Conference/Workshop organized by the Registry Department of the Federal University of Technology, Akure. July 23-24, Divyakant Agrawal, Amr El Abbadi, Shyam Anthony and Sudipto Das (2010), Data Management Challenges in Cloud Computing Infrastructures. University of California, Santa Barbara George Gilbert (2012): A Guide to Big Data Workload – Management Challenges – Report of the Research supported by DataStax, May 2012 Joseph Izzo (2009): Emerging Challenges for the 21 st Century Insurance Data Manager. Journal of Database: Trends and Applications. August 14, 2009 (www.dbta.com/Trends and Applications)www.dbta.com/Trends Osasona Olagbemi (2012): Tools for Academic Planning. Published in Practical Guide on Academic Planning in Nigerian Universities: A Compendium of Academic Planning Tools – A CODAPNU Book Project Ed. by Ignatius I. Uvah. Atlantis Books Publishers. Tony Bain (2008): Top 10 Data Management Issues for as at March 2014http://blogg.tonybain.com Triton Consulting Ltd (2009): Meeting the Challenge of Database Management. White Paper by Triton Consulting, 2009