Education Analytics Value Chain

Josh Magdziarz, Director, Predictive Analytics, West Coast University

Josh Magdziarz, Director, Predictive Analytics, West Coast University

Many educational analytics initiatives are plagued by a plethora of barriers preventing advanced success. These barriers include multiple sources of truth, unreliable data, lack of ownership, and other undesirable features resulting installed projects, depleted resources, and an unaware customer base. Organizations today have an opportunity to create an agile data analytics program that is prepared to handle the demands of a rapidly changing environment by defining structure and building value.

The initiative should include a reachable mission that empowers a data platform with a single version of the truth for all key metrics, allowing improved decision making and insights to help achieve the objectives of the strategic plan. Unfortunately, the method to complete this mission is often overlooked and taken for granted. This effort is accomplished by leveraging the Education Analytics ValueChainthrough navigating a business-driven approach via a Mission-Driven Analytics Architecture. Ultimately, this promotes a data culture by shifting towards a more singular version of the truth, cultivating data to be treated as more than an asset, and optimizing resources to enhance time spent analyzing.

"Implementation of the Value Chain is iterative and evolves with patience, as it can take 2-3 years to progress through the various required phases while deploying each chain component"

The Education Analytics Value Chain encompasses six key components that mesh together to weave strong support for analytical excellence.

Change Leadership

The most crucial piece of the Education Analytics Value Chain is Change Leadership. As a core tenant of the chain, it enables forward progress, prepares the organization for constant change, and acknowledges the required team effort to successfully implement an analytics program. Enacting Change Leadership is also one of the biggest challenges of implementation as it requires bringing key members of leadership together to acknowledge and support the potential value. During this effort, leaders must be kept informed of progress, continue to champion analytical efforts, and promote the continued evaluation of the program.

Data Management

Once efforts are underway to bring an organization on board with the large effort ahead, Data Management exercises are initiated. This component includes the process of securing, transforming, and monitoring data elements. Executed in this piece is the understanding of the lineage and inventory of existing data structures. It is here that coordination with IT teams begins to ensure that the appropriate environments, controls, and resources exist.

Reporting

Reporting efforts begin when data are managed and structured appropriately. In conjunction with data management goals, reporting helps consolidate data environments and build filterable, drillable, mobile-friendly reports. These reports benefit from centralized repositories, enhanced security features, and monitored usage. Ultimately, the data used in these reports become the basis for building future value chain elements, including Performance Management and Analytics.

Data Governance

Operating in near-tandem with Reporting, Data Governance is the laborious task of defining terms, establishing standards, and assigning stewards. While initially time-consuming, it is ultimately a highly valuable by-product of the program. As data governance matures, members of an organization rely on exacting standards that articulate how and why key elements are leveraged. Furthermore, the use of data stewards sets forward a process to eliminate tribal knowledge and instead promote structured knowledge succession planning.

Performance Management

As reporting matures, Performance Management ensures that operational goals are defined, monitored, and met. This includes a continuous review of key performance measures and frequent communication of progress. This piece of the chain enhances existing reports and creates interactive, user-friendly dashboards and scorecards for consumption at multiple levels.

Analytics

Finally, Analytics is enabled at a deeper level to leverage reusable, clean-sourced, defined data. This allows for more defined, accessible, and practical information usage than could be achieved without the existing structure. Without the previous elements, analytics becomes increasingly more time consuming and loses value without support from the entire chain.

Implementation of the Value Chain is iterative and evolves with patience, as it can take 2-3 years to progress through the various required phases while deploying each chain component. From a business-driven approach, the program must elicit business requirements that match the strategic direction of the organization. Those requirements must be managed to build a high-quality, usable, and integrated data platform for consuming clean data and producing value-added deliverables. Finally, it is the responsibility of the initiative to ensure that the platform can be exploited to peak potential to execute data-driven decisions that create opportunities for the organization to grow and succeed.

Several critical success factors must be defined to ensure that the program produces its promised value. Consider success factors that progress the program beyond baseline maturity levels, create centralized data groups with business committees, execute data governance initiatives with identified stewards, and build evidence of strategic initiatives leveraging data-driven insights and analyses. By leveraging the defined mission and opportunity, the program should be able to meet early goals and drive towards continuous improvement.

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