In the past, data reporting and analytics was defined by commodity "push" reporting, which was conducted with one-way, non-interactive, macro-analytic systems that produced high-level, one dimensional data. With next generation systems today, the design construct has essentially been turned on its head so that data analysis is be conducted using "pull" reporting, which gives end users, "the ability to control, contextualize and customize information unique to the individual needs of a user." Essentially, reporting and analytics has become interactive. No longer is it a spectator sport.
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From Business Intelligence to Business Optimization
In order to better understand effective data reporting and analytics in the online learning market, it is necessary to reflect on the changing role of business intelligence (BI) in private and public sectors as a backdrop and predictor for how the evolution of information architecture
is relevant to higher education.
Traditionally, reporting and analytics in first-generation, “mega-vendor” BI platforms were expressly designed for mass consumption data and broad-based adoption for high-level information “push” to an end user, business unit or department. These macro-analytic systems were put in place for generalized one-way, non-interactive information flow between the data source and the data user involving complex processes for customization and contextualization of information. Because these systems are universally designed for high-level information “push,” businesses need to conform to the technology as opposed to the technology conforming to the organization. In the former, valuable strategic information is sequestered within central repositories of data that IT controls, owns and operates. These systems are scalable and efficient, especially within large organizations that amass large volumes of data where reporting and analysis “presupposes” what an end user wants to know through a high-level specification of reporting requirements.
Numerous revisions and iterations of this commodity reporting environment results in end user data clients becoming “data spectators” under strict control of central IT. What was designed initially to be a scalable system for broad-based information and data reporting and analysis actually led to business intelligence barriers with painfully long processing times, and restrictive reporting due to onerous change cycles with heavy reliance on IT.
The end result is that departmental use of BI needs to conform to the solution, rather than the solution conforming to departmental or individual needs. Finally, these macro systems, due to IT centralization, have historically experienced high total cost of ownership with enterprise licensing fees and the need for central IT programming and support. Finally, these macro systems, due
to IT centralization, have historically experienced high total cost of ownership with enterprise licensing fees and the need for central IT programming and support. In the new age of analytics, data and information are presented, accessed and managed from a “pull” design construct, giving end users the ability to control, contextualize and customize information unique to the individual needs of an end user.
Next-generation BI reporting analytics systems provide real-time progress monitoring, extreme data flexibility and extensibility, fast data query, and the ability to explore relevant data sets in real-time. Data and information, still managed in central IT repositories, can be extended to the department level supported by localized tool sets designed for non-statistical end users. These tool sets feature actionable analytics assisted by high-end data visualization and usability promoting pervasive and viral departmental adoption of a business optimization platform as opposed to traditional BI.
These evolutionary platforms are also especially suited for strict IT governance due to advanced security schemas allowing central IT to control hierarchical access to data resources and content. Business optimization platforms enable information workers to build their own personalized views from reusable data components that are developed and managed for users by central IT. These systems put the power of BI into the hands of novice data consumers, allowing ease of access and personalized role-tailored content and context-sensitive views.
Another important distinction between traditional BI and business optimization platforms is that these next-generation systems embrace and facilitate data and information collaborative networks. These systems provide a secure information architecture where information mash-ups can be shared within a “data network cloud” of departmental subscribers where reports and analytics can be shared, customized and contextualized for adjacent departmental use.
As compared to traditional systems, business optimization platforms reduce total cost of ownership, cut end user costs, reduce development backlogs, and speeds delivery of intelligence and analytics to decision-makers. With the private sector BI market transition as a backdrop, the increased focus on enterprise performance management in higher education, and the pervasive and viral adoption of online learning as an effective means for teaching and learning, contemplate the following questions:
- As higher education adoption of online learning matures, what advanced analysis and reporting tools are needed to leverage these vast data repositories of student performance?
- How do course design strategies in blended or fully online programs change with the advent of more sophisticated systems from tracking learning behaviors?
- How can LMS outcome measurement, analysis and reporting be positioned as a gravitational force for multivariate data from other data sources outside of the LMS environment?
- How can LMS business intelligence solutions, such as predictive analytics, data extension architecture and data visualization, be implemented, deployed and utilized throughout stakeholder hierarchies at institutions?
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