17/06/2015. Data: Chaos, Challenge, Order or Opportunity? The Age of the Industrial Internet. Data, data everywhere! Nicola Haywood Alexander

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1 Data: Chaos, Challenge, Order or Opportunity? Nicola Haywood Alexander University of Manchester Deputy Chair, PCMG The Age of the Industrial Internet Data, data everywhere! All great changes are preceded by chaos. Deepak Chopra 1

2 What is happening? Data volume is growing Number of users are growing Depth of analysis is growing Query complexity is growing Use of visualisation is growing Expectations are growing We hope that data driven decision making are growing The BI and Data Warehouse Project is longer the Cinderella of the Change Portfolio They re calling it Big Data Defined by Doug Laney in 2002 as high volume high velocity and/or high variety Promoted by Gartner from 2012 Veracity added by IBM in 2013 Value added by Bernard Marr in 2014 Big Data and Higher Education Establishments Where is the wisdom that we have lost in knowledge? Where is knowledge we lost in information? T.S.Elliot 2

3 HE headlines. Funding and Fees Student Experience This means data for. Student Success Student Satisfaction Operational Effectiveness Financial Management Technology Relationship Management Student Success Student Satisfaction Accountability Technology Accountability Operational Effectiveness Financial Management Drowning or swimming in data? Volume of data increasing, but the value lags considerably Veracity for regulatory reporting burden is crowding out for universities capacity for internal strategic and operational reporting Variety arising from a highly diverse and collegial culture Our customers are digital natives and the velocity of data they produce is high 3

4 So what? Most trends in the sector have been associated with big data centres of excellence data have been concentrated on engineering and science skills Data lakes (enterprise wide platforms) will provide opportunities for data enrichment across institutional boundaries The BI and Data Warehouse Project is no longer the Cinderella of the Change Portfolio Why we need data What happened? Information Reporting Diagnostics Why did it happen? Analysis Trends What s happening? Benchmarking What do we think will happen? What do we want to happen? Forecasts Prediction Prescriptive Scenarios Who needs data Data Needs Audience EXECUTIVES Scorecards for Performance Management MANAGEMENT & PERFORMANCE DATA MANAGEMENT Dashboards, Reports to Monitor Progress KNOWLEDGE WORKERS Ad hoc analysis to understand trends TREND, SUMMARY DATA STAFF Production reports and ad hoc access for daily operations DETAILED DATA 4

5 Drivers for investing in BI and DWH projects (UCISA/UoM 2014) To move away from a plethora of individual spreadsheets to a more integrated and accessible system To have a reliable, accurate single view of data To be consistent and disciplined, to assure data quality To make sure the right information is available in the right place To report across multiple systems To be able to manage the business effectively. Management information to improve in effectiveness and efficiency aid decision making and planning student numbers, academic portfolio, student experience, finance, procurement, research funding Fewer drivers associated with intelligent or student analytics Viewed as an enabler, with no common benefits "Justification is the risk of not doing it rather than explicit benefits. Benefits are subjective, and difficult to define" Death of the paper chase: eliminating waste, using tools Eliciting information from our data. "Consumer data will be the biggest differentiator in the next 2 to 3 years, whoever unlocks the realms of data and uses it strategically will win" Angela Ahrendts. What you currently use (Ellucian/AIR /UCISA/UoM, 2014) IBM Cognos Crystal Reports Evisions Argos US MS Tools Tableau Oracle MS Tools Qlikview UK Business Objects IBM Cognos 5

6 Challenge If you can find a path with no obstacles, it probably doesn t lead anywhere. Frank A. Clark Top barriers to effective BI (Ellucian/AIR /UCISA/UoM 2014) US UK Analytical drill down capabilities Integrated data sets Departmental access to data Slow turnaround time, bottlenecks Analytical skills Data governance and ownership Single definition of the data Trust Integration Analytical skills UK Challenges (UCISA/UoM 2014) Inertia it takes longer than you think Misuse avoid the desire to use DWH be tactical rather than strategic It s not sexy engagement is possible with some thought Enterprise architecture fewer challenges if have you have EA in place Capability set up effective BI capability early Order of build Balance logic against priority Master Data Set and Data Governance before DWH Leadership Membership of the governing body or steering group is key Institutional ownership positing, mandate and policies aid change 6

7 What you said you needed? (Ellucian/AIR /UCISA/UoM 2014) Access to all required data Ability to capture all historical data and decisions Ability to engage all major departments electronically Out of box integration with ERP Built in KPIs and metrics for tracking performance Creating a new order. In chaos is a cosmos. In all disorder a secret order. Jung Technology Data Architecture (simplified) 7

8 Approach Culture Data will have to be Reliable Shared, not held Trusted Secure Consistent definitions Single source of the truth People Engage IT and MI &Reporting Services early Be prepared to train people It s a hot market for qualified BI analysts Consider options for shared services Leverage the culture of collaboration that is common in higher education 8

9 Sustainability Additional skills and resource over time Knowledge transfer Effective use of the new tools Understanding new policies and procedures How to leverage the new information environment Good governance Change management Synopsis There are 3emergingdomains for analytics in HE Student success profiles Intelligence on relationships Student focussed service analytics There is a need to understand how to grasp and utilise big data well This will not be 'just be another' project Wisdom of harnessing knowledge from information To know that we know what we know, and that we do not know what we do not know, that is true knowledge. Thoreau. 9