 Analytics is being used throughout higher education. It can help colleges and universities advance institutional goals, strengthen student outcomes, improve quality and efficiency, and enhance teaching, learning, and research. EDUCAUSE's showcase on analytics offers two lessons. Let's explore them, learn how to act on them, and put them in context. We've been working on analytics for over a decade, and yet in many ways, we're still struggling to build good foundations. In their recent EDUCAUSE review article, Laura Jones and Steve Burrell highlighted three challenges that you may find familiar. Everything takes longer than expected, staff will balk at adopting new processes, and prioritization is hard. How can we hope to get to more advanced analytics tools like artificial intelligence when we're stuck pouring the foundation? And in fact, a recent quick poll concluded that we're just not ready to adopt AI. About two-thirds of respondents reported that institutional deficiencies to support the adoption and maintenance of AI are the main challenges to using AI at their institutions. We need to replace our straw houses with strong brick houses. Where to start? EDUCAUSE has developed an analytics maturity assessment that you can take to identify your opportunities to improve. The rubric-based assessment will help you see the activities and capabilities that can strengthen your analytics programs. The self-assessment will help you create your blueprint, but how do you build that brick house? Learn from others. A new case study from Drake University describes how IT and IR leaders focused on meaningful data adoption by asking key decision makers what problems they were facing and what data outputs would help address those problems. And the article by Jones and Burrell describes the data governance journey at Northern Arizona University. What else has our showcase taught us? Well, it's easy to get lost on the journey from data to insight to action. At least we know now how important it is to put data to use, but there are a lot of ways to go wrong. The emphasis is on big data these days, but less may actually be more. There are many sources of bias in data, using past data to make decisions about future choices can prevent us from breaking out of the frame of the past and taking a new direction. That's little help if we want to support traditionally underserved groups. Our quick poll on AI found that concerns about ethics related to AI and about algorithmic bias pose significant challenges to adopting AI. And it's people who will use the data to act. Elena Zady offers thoughtful advice on how to avoid the perils of AI and on which ethical issues to consider. Her advice applies to analytics and data use beyond AI. Jack Seuss advocates for thoroughly understanding the data and how it led to recommendations rather than reflexively adopting algorithmic results and giving them to students or others to act on. It's people who will be applying analytics. That means we need strong leadership as well as committed capable staff. As Colleen Carmine says, we need to define the characteristics of a good practitioner in analytics. And so we've learned two lessons from Educa's analytics showcase. Where do we go from here? The 2021 IT issues report suggests three scenarios for higher education. Let's apply them to analytics. Each scenario offers a different path for action. Institutions on the restore path for analytics will begin a data governance program to manage data and analytics at the institutional level and focus on the highest priority decisions they want to use data to inform. Evolving from where the pandemic has left us may lead institutions to focus on creating standard processes and policies to ensure data are clean, used consistently and not misused. Those institutions will also focus on training the institutional workforce to understand and apply data more effectively. And those committed to using analytics and data to transform their institutions and to helping lead the way for higher education will recognize that analytics is integral to digital transformation. They will take an enterprise architecture approach to their analytics foundations and they'll ensure their DX and analytics efforts are part of a larger transformational agenda.