From the Field: Musings on Work to Achieve the Dream

What is Your Data Strategy: Offense, Defense, and Special Teams

The fall is “Data” season at Achieving the Dream. We recently concluded our sixth annual Data & Analytics Summit and in early September, our team visited North Carolina to speak to presidents about data as part of ATD’s partnership with the Belk Center at North Carolina State University.

The data and technology capacity of our Achieving the Dream colleges, even with our 15 years of attention in supporting colleges in building cultures of evidence, remains stubbornly low. Aggregate results from Network colleges completing our Institutional Capacity Assessment Tool (ICAT) continue to show that they are lagging in the “data and technology” fundamental. In fact, it is the weakest fundamental of the seven capacities that Network colleges focus on.

My hypothesis on why this capacity remains our weakest is that our colleges and our leaders do not have a clear data strategy—a tight game plan. A data strategy offers a vision and a plan for organizing, governing, analyzing, and deploying information assets. Without a data strategy, organizations struggle to protect and govern their data while also leveraging their data (internal and external data) to improve student success results.

Our lack of a data strategy is not just a higher education sector issue.

Data Game Plan
The 2017 Harvard Business Review article What’s Your Data Strategy? by Leandro DalleMule and Thomas H. Davenport highlights studies that show that on average, in most industries, less than 1 percent of unstructured data is analyzed or used at all. The same studies show that more than 70 percent of employees have access to data they should not, and 80 percent of analysts’ time is spent discovering and preparing data rather than analyzing it and gaining insights for action.

As more attractive opportunities for data come upon us—text analytics, predictive analytics, data visualization, and even artificial intelligence applications—it is easy to jump to these solutions without stepping back to attend to the development of a basic data strategy.

“The plumbing aspects of data management may not be as sexy as the predictive models and colorful dashboards they produce, but they are vital for high performance. As such, they are not just the concern of the CIO; ensuring smart data management is the responsibility of all C-suite executives, starting with the CEO,” writes DalleMule and Davenport in their article.

This is an interesting point. How many of our college presidents are asking for a specific data management strategy?

In the article, DalleMule and Davenport use a simple framework to guide the development of a data strategy offering a rubric that considers “offensive” and “defensive” strategies and a consideration of their trade-offs.

Defense
Data defense is about minimizing downside risks. Activities in defense include compliance, using analytics to detect fraud or other irregularities, building systems to prevent theft, and efforts to improve data integrity and documentation. Think about our college financial systems, our financial aid systems, and our enrollment reporting systems.

Offense
Data offense includes activities that generate insight. This includes data analysis and modeling, integrating data across platforms, and developing interactive dashboards around key performance indicators. Think about our student success key performance indicators, our early momentum metrics, the disaggregation of data, and integrating into our program review models external data like labor market demand for graduates and the labor market value of specific credentials and degrees.

Defense and offense are important. The challenge for our colleges is to find the right balance.
Many factors influence this balance. External factors might influence the balance of offense and defense, such as the regulatory environment. Strong competition or a full focus on equitable student success results might force a tilt toward offense. Internal culture can also force a tilt toward either offense or defense. I often see tensions on our campuses between IR, IT, academic affairs, and institutional effectiveness around the ownership (defensive position) rather than the stewardship of data (offensive position). Of course, FERPA is the ever-present defense for not playing offense.

Where is your college on this spectrum? 

Many data strategy decisions at colleges are rooted in whether to standardize data or keep it more flexible. So, before moving more deeply into offense and defense, we need to look at our  data and information architecture.

Data Architecture
A college’s data architecture describes how data are collected, stored, transformed, distributed, and consumed. Without a strong data architecture, teams within an organization might create and store the data they need in siloed repositories that vary in depth, breadth, and formatting. Does this sound familiar?

How many of you have shadow data bases?

Some colleges have created highly centralized, control-oriented approaches to data and information architectures. These top-down approaches are not well suited to supporting a broad data strategy, as they can inhibit flexibility, making it difficult to customize data so they can be applied strategically. This is described by DalleMule and Davenport as a single source of truth (SSOT) structure. Yet to play offense, organizations need this single source of truth structure to feed multiple versions of the truth (MVOT). This ambiguity is often difficult for our colleges as SSOT is logical and not having some standards can lead to chaos. Yet, to meet our student success goals, we need the multiple sources of truth that transform data into information and insight, or as DalleMule and Davenport describe it, as “data imbued with relevance.”

I see these debates about SSOT and MVOT in action on our ATD Network campuses. For example, institutional technology departments produce data that may not sync with the institutional research department’s data. Then these data are translated by departments and it, again, looks different. The integrity of the data is then questioned. At our Network colleges, I also see the clashes between campus data hawks who want to keep all the data and data doves who want to anonymize it so we can use it for analysis. Data hawks are often playing defense. Data doves want to play offense.

We need an AND approach on our campuses— to play both offense AND defense— so that we can balance the need for control of data with flexibility.

Deeper into the Game Plan
Looking deeper into the data game plan we see that on the data defense side, a strong defensive strategy includes robust data governance and controls, and a more centralized data-management organization. On the data offense side, we see high data flexibility, and a more decentralized data-management organization.
 
When I think of offense and defense as it relates to a data strategy, I think about John Kotter’s work around networks, innovation, and hierarchies. In his book Accelerate, Kotter makes a case for leaders to balance hierarchy and networks to build well-run, innovative, and energetic organizations. Using Kotter’s framework, what goes in the hierarchy is data defense, as data defense is not ambiguous; it helps you get from A to B, efficiently. What goes into the network is data offense.

Perhaps that includes the work of your ATD data teams.

In our student success work, we fumble on offense when we:

  • Fail to put the student voice at the center of our work and rely only on lagging internal quantitative data to inform our actions
  • Worry more about deploying technology than designing and documenting processes to solve problems and drive a technology implementation
  • Focus on data-informed learning but do not facilitate data sharing
  • Fail to put stakeholders with multiple perspectives and in key teaching and learning roles on our data teams
  • Take data strategy work with a “data ownership” rather than “data stewardship” perspective

Special teams
Moving through our data game plan, our special teams—additional assets to build data capacities— include ATD Data Coaches as well as partners to support predictive analytics, data visualization, scheduling optimization, and labor market data. All of these special team members have a critical role to play in advancing and supporting the work of our defensive and offensive positions. It’s vital that these special team members are working from the same playbook so that their work is aligned with the overall data strategy game plan. To optimize our work the effectiveness of our special teams, we need to be sure that faculty, staff and administrators have data fluency otherwise we run the risk of outsourcing important institutional insight.
We are in the heat of the football season. Coaches talk about playing both sides of the ball. Our colleges must be equipped to do the same.

We can no longer win with our students if we don’t have a strategy or are over reliant on defense or even offense as we build the data capacities of our colleges. We need a tight, coordinated game plan.

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