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Data & Analytics

Institutional Intelligence and Analytics Maturity Assessment

In 2018, Clemson University conducted a study of our Institutional Intelligence and Analytics (I2A) maturity using an instrument developed by the Higher Education Data Warehousing Forum (HEDW). The study was conducted via focus groups and a follow-up survey with stakeholders from Institutional Effectiveness, Human Resources, Finance, Research, and CCIT. The results were benchmarked against our Top 20 Public Peers.

Results indicated a need for improvement in Data Quality, Data Management, Data Access, Metadata, and Data Integration via enhanced User Engagement to increase Business Value throughout the organization.

The results and peer comparison are available in a Tableau Dashboard. Please contact Ben Wiles ( for information about accessing the dashboard or about the I2A Maturity Assessment in general. 

A ‘fully’ mature I2A environment under the HEDW model is characterized by the following items.

Institutional Intelligence Team

Training, specialization and experience have consolidated the central unit as experts on the tools, techniques and data domains. The team actively advocates the use of data products across the institution and proactively approaches all potential user groups. Permanent, clearly stated collaboration relationships are established with all necessary external units (i.e. source business units, IT, use groups). Management of the institutional intelligence area formally participates, with the rest of the functional units, in the appropriate governing bodies and committees, warranting a proper alignment with them and the global institutional directions. 


Data from all relevant domains (such as HR, Academic, Finance, Sponsored Programs) are managed. 

Business Unit Role

Business units’ role as domain experts in the information supply chain is clearly specified, communicated and incorporated in the unit’s core responsibilities. Duties of Data Stewardship are clearly defined and proactively assumed by business units, and comprise at least the following:

1) Expert functional & technical support to institutional intelligence team: providing specialized technical and functional knowledge on their business domains to the institutional intelligence team developers and maintaining coherence in the institutional intelligence platform by actively supporting impact analysis in the developed data products due to technical or functional changes in the business unit operational systems that act as data sources.

2) Data quality: diagnosing and providing solutions to reported data quality issues. Proactively ensuring completeness, correction and availability of the information acquired and/or created in their unit and general suitability for analytic use.

3) Certification of data products: certify, as experts on the source systems and data, the correction of the information being delivered through the different data products created by the institutional intelligence team.

Data Products

Appropriate deployment of the following:

1) Parameterized reports: They are basically on-line reports offering limited, fixed options to personalize its data output.

2) Ad hoc data navigation: This kind of data products allow business users to “dive” into the data of one or more subject areas without a preconceived notion of how the resulting output will be. They offer the users a catalog of all available data elements, and give the functionality to interactively build data outputs combining these data elements and navigating through different levels of data aggregation using tabular or graphical representations of data.

3) Dashboards/scorecards: This kind of data products allow the users to access a combination of complementary, simultaneous data outputs that conform to a rich, cross functional, integrated context of information delivery. Data is usually highly aggregated and displayed in a compact interface. Graphical data output predominates over tabular views, and KPIs with color-coded representations of their values play a key role.

4) Advanced analytics. This kind of data product is composed of a wide range of applications of data mining, machine learning and advanced statistical techniques. They provide the user with predictive models (predict future results based on current and historic data), what-if analysis environments (simulated scenarios, projections), automatic knowledge discovery systems (data clustering, association rules), etc. 

User Coverage

Executive leadership, staff, faculty, and students are supported by Institutional Intelligence and Analytics. 

User Engagement

Users perceive themselves as the most important stakeholders in the institutional intelligence capability. They feel co-owners of the data products they have defined, tested and evolved in partnership with the institutional intelligence team. This co-ownership implies shared responsibility in the applicability, effective use, active promotion among peers and evolution of the data products they obtain. In this level, user communities usually constitute very active channels for mutual support, the promotion of analytic culture and the institutional intelligence initiative. 

Data Management

Accountability for the data management aspects is exercised, based on the existence of clear policies and principles, and the corresponding supporting processes and tools. Unexpected conflicts and exceptional issues are addressed by new, specific, specialized committees or by existing governing entities officially tasked with this responsibility.

Business Value

The delivered data products are effectively embedded in processes that are considered critical for the proper functioning of the institution, either due to its profound impact on core decision making or administrative activities, or because analytic culture is so established and institutionalized that information effectively drives the activity at all levels, and is considered vital. Effective use and business impact are very high.

Strategic Support

Institutional Intelligence and institutional strategy are mutually dependent; Institutional Intelligence supports institutional strategy by providing a highly efficient information analysis context to support institutional goals, and institutional strategy supports Institutional Intelligence by explicitly considering it as a key capability to acquire and evolve. As a result, Institutional Intelligence has a clearly defined strategy supporting that of the institution as a whole. A clear, global development path exists and it is inconceivable that the institution can meet its agreed goals without the support of the Institutional Intelligence service.