administrative and financial dashboards to better meet the needs of our Trustees. This has allowed us to take some mission critical steps that will benefit our subsequent development of analytics in other areas - to identify systems and data elements of record, to engage data owners as stewards and co-contributors and to agree data governance principles. We are only at the beginning of a journey towards mutual understanding of our data and embedding analytics in institutional practice. However we’re confident that our starting point will create a solid foundation. We’re also clear that the journey is essential. From an interview with Ted Dodds, CIO - Data Quality Data quality is surfaced in issues ranging from cleanliness to provenance; for example: Can the data source be trusted? Is the data complete? Is it up to date? Is the data validated against standard coding frames? Michigan State University MSU emphasises that the key to the institutional analytics mission is quality of data. You – cannot derive valid actionable insights if the data are unreliable. Our emphasis is therefore on establishing of ‘systems record’ that ensure that key shared data items used by many different systems, such as names and addresses, are entered and managed in one place. To date, we have been principally focused on developing sound analytics capabilities around finance and human resources systems. However, we are attentive to a new wave of developments in the area of learning support, focusing time’ personalized support at the point of need. ‘in From an interview with David Gift, CIO – The Requirement There is a balance between answering known questions versus discovering unknown narratives. Experience commends atomic approaches linked to the potential for data capture as well as those dictated by established KPIs; plus ‘Deductive’ data analysis. Adam Cooper of CETIS sums up this position: ‘Inductive’ My take home, then, is if we are serious about analytics we need to be thinking about exploratory data … analysis and confirmatory data analysis and the label "analytics" is certainly inappropriate if neither is occurring. [Cooper, 2012][5] To build those inductive narratives, the inclusion of generic will be essential, including institutional IDs, IP ‘connectors’ addresses and time, in addition to operational coding frames, such as course and budget codes. Given these imperatives, data management is faced with key decisions about what to keep and for how long, what to aggregate and what to delete. Such decisions are susceptible to being made on the fly, influenced by immediate availability
administrative and financial dashboards to better meet the needs of our Trustees. This has allowed us to take some mission critical steps that will benefit our subsequent development of analytics in other areas - to identify systems and data elements of record, to engage data owners as stewards and co-contributors and to agree data governance principles. We are only at the beginning of a journey towards mutual understanding of our data and embedding analytics in institutional practice. However we’re confident that our starting point will create a solid foundation. We’re also clear that the journey is essential. From an interview with Ted Dodds, CIO - Data Quality Data quality is surfaced in issues ranging from cleanliness to provenance; for example: Can the data source be trusted? Is the data complete? Is it up to date? Is the data validated against standard coding frames? Michigan State University – MSU emphasises that the key to the institutional analytics mission is quality of data. You cannot derive valid actionable insights if the data are unreliable. Our emphasis is therefore on establishing ‘systems of record’ that ensure that key shared data items used by many different systems, such as names and addresses, are entered and managed in one place. To date, we have been principally focused on developing sound analytics capabilities around finance and human resources systems. However, we are attentive to a new wave of developments in the area of learning support, focusing ‘in time’ personalized support at the point of need. From an interview with David Gift, CIO – The Requirement There is a balance between answering known questions versus discovering unknown narratives. Experience commends atomic approaches linked to the potential for data capture as well as those dictated by established KPIs; ‘Deductive’ plus ‘Inductive’ data analysis. Adam Cooper of CETIS sums up this position: My take home, then, is … if we are serious about analytics we need to be thinking about exploratory data analysis and confirmatory data analysis and the label "analytics" is certainly inappropriate if neither is occurring. [Cooper, 2012][5] To build those inductive narratives, the inclusion of generic ‘connectors’ will be essential, including institutional IDs, IP addresses and time, in addition to operational coding frames, such as course and budget codes. Given these imperatives, data management is faced with key decisions about what to keep and for how long, what to aggregate and what to delete. Such decisions are susceptible to being made on the fly, influenced by immediate availability
Analytics-for-the-Whole-Institution-Vol1-No2.docx