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Data fluency
- 1. Data Fluency BUILDING EFFECTIVEDATA COMMUNICATION SKILLS IN YOUR UNIVERSITY MARTHA HORLER
- 2. Who I am MarthaHorler – Senior Data Management Officer (Manchester Metropolitan University) 13 years' experience in higher education Student focused roles, followed by data management roles Experience in course administration, student engagement, quality processes, project management, data management, systems development m.horler@mmu.ac.uk @thedatagoddess
- 3. What we willcover •Data literacy vs data fluency •Data fluency framework •Data governance tools •Resources
- 4. Common organisational dataproblems People unwilling to engage ◦ “I don’t do data” Disparate data sources – hard to bring together and manage them ◦ “I don’t have access to all the data” Data not being captured ◦ “We don’t have that data” How many of these do you recognise?
- 5. Data Literacy vs.Data Fluency Data Literacy The ability to read data products Understanding of data formats Able to understand a table or chart Able to pick out key points from data Data Fluency The ability to read and write data products Ability to change data between formats Able to create tables or charts Able to manipulate data to find answers
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- 7. Data Consumer Understanding thejargon of data: ◦ Correlation vs. causation, statistical significance, regression to the mean, confounding factors Atomic data vs. summarised data Key questions to ask of any data product: ◦ Where does the data come from? ◦ Is the information trustworthy? ◦ Is it a sample or does it include everyone? – How were they chosen? ◦ What can you learn from it? ◦ What can you do with it? In a world of information overload, we need to make sure we are focusing on the bits that prompt us to take action, not the ‘nice to know’ bits
- 8. Quiz! Taken from “DataFluency: Empowering Your Organization with Effective Data Communication” Has it highlighted any areas you want to develop?
- 9. Data Author Learning arange of tools, from beginner to more advanced: ◦ Presentation tools: PowerPoint, Prezi, Keynote ◦ Spreadsheet tools: Excel, Google Spreadsheets ◦ Statistical analysis packages: R, SAS, SPSS ◦ Visual analysis tools: Tableau, QlikView ◦ General data management skills: data transformation, data formats, data quality tools Bridging the gap between your data and your intended audience ◦ What will motivate an audience to action? ◦ Knowing what to leave out, even if it might be of interest ◦ Creating a logical structure and narrative flow to your data product Pay attention to good design principles (see Stephen Few’s book)
- 10. Student Enquiry System– a Case Study MMU has had an enquiries logging system since 2013, used for tracking basic queries at the hubs, and more complicated referrals on enrolment or document submission Until early 2016, the data was not being used to its potential After completing an online course on Excel, I used it’s PowerPivot tool to create a simple dashboard that is now used regularly to make staffing level decisions on the front line hubs. Created over an afternoon, it became a useful tool for exploring the data This will likely be the prototype for a more advanced reporting tool when the enquiries system is replaced
- 12. Data Fluent Culture Leadingby example – set and communicate expectations Determine organisation terminology and definitions – a common vocabulary Celebrate effective data use and products Use data to inform decisions and actions Support training for staff Establish the key metrics for the organisation Be transparent about how data is sourced and manipulated
- 13. Data Product Ecosystem Traindata authors on the design skills for communicating data Invest in suite of tools for authors to use – consistency where possible Set standards of visualisation design principles Inventory your data products – then make centrally available in a catalogue Build in feedback mechanisms so that data products can improve over time Encourage discussion of the products – are decisions being made as a result of them Take inspiration from Apple Store
- 15. Data Governance Tools BusinessGlossary Metadata Management Data Profiling Data Quality Management Master Data Management Reference Data Management Information Policy Management Big Data Tools – Hadoop/NoSQL
- 16. General Data ProtectionRegulation Adopted April 2016, and will enter into application 25 May 2018 after a two-year transition period Key changes: ◦ Appointment of a Data Protection Officer ◦ Right to erasure of personal data ◦ Increased sanctions for data breaches ◦ Explicit consent required ◦ Data portability Check the Information Commissioner’s Office website for more details: ico.org.uk
- 17. Resources Data Fluency: EmpoweringYour Organization with Effective Data Communication - 978-1118851012 DAMA Guide to Data Management - 978-1935504023 Data Governance Tools - 978-1583478448 http://www.juiceanalytics.com/ Microsoft Virtual Academy Coursera / Edx www.thedatagoddess.com
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