NOTE: This series was mostly made with AI and in no way are any official products from the REACH Research Analytics Summit. I encourage people to go to the conference site to see more details on sessions linked here The text in this series including the summit themes are AI generated.

TLDR: Dashboards are no longer the finish line. The 2026 Research Analytics Summit made clear that the field has moved past “we built a Tableau dashboard” toward harder questions: who is the audience, what decision does the dashboard support, what definitions does it lock in, and what story does it tell. Sessions across the program showed how research offices are designing for action, not just display.

A note on how this was written. This post was drafted with AI assistance from the official conference session abstracts and my personal notes on the sessions I attended. Sessions I did not attend are summarized from the speakers’ published abstracts, not from observation. I have spot-checked the framing and takeaways, but the language and structure are largely AI-generated. Treat it as a curated overview of the program, not a firsthand recap.

The Shift This Year

For a long time, the dashboard was the deliverable. A team got a request, built a Tableau or Power BI page, shipped it, and called the project done. The 2026 Summit retired that model. Across the program, the consistent message was that a dashboard without a clear audience, a locked KPI definition, and a narrative arc is not a finished product: it is a screen.

Three currents ran through the theme. The first is design discipline: building dashboards as decision tools, not as data buffets. The second is operational substance: live data refresh, federated source integration, role-aware access. The third is specialization: geospatial, compliance, faculty performance, and other use cases where generic templates fail.

Sessions Covered

Grouped by emphasis:

Foundations: Building Dashboards That Work

Workshops and conceptual sessions on how to design dashboards that actually drive action. Tool training, narrative design principles, KPI definition discipline, and shared data literacy across stakeholders.

• WA2: Tableau Dashboards Made Easy: a Beginner-Intermediate Workshop

• B3: From Strategy to Screens: Designing Dashboards That Support Decision-Making

• I2: From Data to Decisions: Designing Narrative-Driven Dashboards for Research Administration

• I4: It Depends!: Building Data Literacy as a Shared Language for Research Analytics

Operational Dashboards in Practice

Working systems built and deployed at specific institutions. Enterprise data warehouses, automated expenditure monitoring, faculty performance, award management, and the platform-selection question.

• A6: Enhancing Research Performance Reporting via an Enterprise Data Warehouse and a Self-Service Analytics Portal

• B5: Data-Driven Award Management: Turning Reporting and Analytics Into Action

• F4: Automating Expenditure Monitoring: Enhancing Decision-Making With Power BI Dashboards

• G4: Research Analytics Dashboards: Visualizing Faculty Performance Data for Actionable Insights

• C2: Bridging Data Silos in Academia: Smartsheet, Tableau, and Power BI as Catalysts for Transparent Research Analytics

Specialized Use Cases

Domain-specific dashboards where generic templates fall short: research security compliance, geospatial analysis, audience-tailored reporting, and three lightning-talk case studies.

• C1: Developing a Research Security Training Compliance Dashboard Through Cross-Team Collaboration

• D4: One Dataset, Many Audiences: Designing Research Reporting That Actually Gets Used

• H2: Research Administration Is Geospatial: A Practical Guide to Integrating Location-Based Data Into Data Dashboards and Analysis

• B6: Lightning Talks on Practical Analytics, an AI-Powered Connectome Tool, and Reviewer Matching

Speakers and Institutions

Speakers from many organizations contributed to this thread of the program.

R1 Research Universities

• Rutgers University: Julia Zappi, Christopher Perkins

• Emory University: Alex Wagner

• Arizona State University: Ferenc Feher, Audrey Reichard

• Stanford University: Anna Romanowska-Pawliczek

• Columbia University: Janki Patel

• University of Arizona: Sarah Dasher, Abraham Silva

• Texas Tech University: Taylor Fidler, Carrie Romo

• University of Texas at San Antonio: Hanin Alhaddad

• Michigan State University: Jack Lipton, Nathan Kuhn

• University of Kentucky: Elizabeth Larimore, Laura Lodder

• Penn State University: Paulina Krys

• Northeastern University: Laura Beaupre

• University of Nebraska: James Geiger

• Colorado State University: Lori Ann Schultz

Other U.S. Institutions

• Pepperdine University: Alex Howard

• Loyola Marymount University: Rachel Marr

• Norco College: Hayley Ashby

• OSU Center for Brain Injury Recovery and Discovery: Colin Edwards

International

• Edith Cowan University (Australia): Michelle Duryea

Industry

• Cayuse: Matthew Kingore

1. Foundations: Building Dashboards That Work

Sessions in this group tackled the conceptual work that determines whether a dashboard ever gets used. The pattern is consistent: tool fluency is necessary, but it is the smaller half of the job.

WA2: Tableau Dashboards Made Easy

Julia Zappi, Christopher Perkins (Rutgers University), Alex Wagner (Emory University), Ferenc Feher, Audrey Reichard (Arizona State University)

A hands-on Tableau workshop pitched at beginner-to-intermediate practitioners. The session used publicly available NIH RePORTER data to walk through cleaning, import, and the construction of interactive visualizations. The workshop revisited basic concepts from the 2025 Summit before moving into intermediate territory. The Rutgers and Emory data analytics teams (established in 2019 and 2020) supplied the worked examples, which makes the session double as a quiet case study in what a maturing analytics function ships in its first five years.

B3: From Strategy to Screens

Hayley Ashby (Norco College), Elmer Yglesias

Ashby and Yglesias treated dashboard development as a governance project, not a design project. Their work at Norco College aligned KPI dashboards directly to the 2030 Strategic Plan and Governance Manual. The session moved through three segments: strategic plan and KPI identification, the methodological work of locking definitions and clarifying denominators, and the emerging dashboard development plan with the District IR/IE Office. The standout phrase from the abstract: dashboards are not plug-and-play. They require clarity, negotiation, and intentional design before a single visual is built.

I2: Designing Narrative-Driven Dashboards

Paulina Krys (Penn State University)

Krys gave the theme its name. Great dashboards do not just look good: they tell a story that compels action. The session laid out narrative-driven design principles applied to research administration data, with specific use cases showing how the principles translate into practice. Common failure modes in dashboard design got direct treatment. The framing fits the broader pattern of the Summit: a dashboard without a story is a chart pile, and chart piles do not change behavior.

I4: It Depends! Building Data Literacy

Laura Beaupre (Northeastern University), Alex Wagner (Emory University)

Beaupre and Wagner addressed the universal problem that every analytics professional eventually meets: stakeholders speak different languages. When a dean says “show our research excellence” or a provost asks “are we competitive,” the analyst’s job is to figure out what decision is actually being made and what data can appropriately inform it. The session covered data literacy concepts directly applicable to research analytics, and the daily-work benefits of investing in shared vocabulary. This is the upstream work that makes every downstream dashboard better.

2. Operational Dashboards in Practice

Sessions in this group presented working systems built at specific institutions. The technical stacks vary (Power BI, Tableau, Smartsheet, SharePoint, Azure), but the design problems rhyme.

A6: Enterprise Data Warehouse and Self-Service Portal

Michelle Duryea (Edith Cowan University)

Edith Cowan University faced the familiar problem of fragmented, inconsistent research performance reporting. The response was a university-wide collaborative project to build the ECU Analytics Portal: a central, self-service platform on SharePoint backed by an Azure-based Enterprise Data Warehouse. The portal provides timely access to research outputs, grants, income, and HDR student supervision data through interactive dashboards and comprehensive documentation. This is the foundational work that I2 and B3 take for granted: without a governed warehouse underneath, narrative-driven design has nothing to narrate.

B5: Data-Driven Award Management

Matthew Kingore (Cayuse), Rachel Marr (Loyola Marymount University)

Pre-award reporting tends to be static, siloed, and reactive. Kingore and Marr presented a case study transforming reporting into actionable analytics for real-time decision-making, anchored in an integrated ERA and reporting framework. The session covered descriptive, predictive, and prescriptive analytics together: visibility, forecasting, workload management. KPI definition, cross-system data integration, and the use of analytics to strengthen sponsor relationships and compliance oversight rounded out the discussion. The vendor-plus-institution co-presentation format gave the session a credibility that pure marketing decks rarely manage.

F4: Automating Expenditure Monitoring With Power BI

Hanin Alhaddad (University of Texas at San Antonio)

Alhaddad walked through the migration of UT San Antonio’s Expenditure Monitoring Report from a multi-sheet Excel workbook with hard-coded formulas and manual year-to-year comparisons into an automated, interactive Power BI dashboard. The dashboard connects to the VPR Server for live data refresh. Leadership now explores trends, projections, and variances through intuitive visualizations and drill-downs. Technical depth was a strength of this session: dashboard design, DAX-driven automation, and forecasting logic all got concrete treatment. This is the kind of unsexy automation work that quietly returns hundreds of analyst hours per year.

G4: Visualizing Faculty Performance Data

Jack Lipton, Nathan Kuhn (Michigan State University), Pam Bach

Michigan State built a home-grown research analytics ecosystem on Power BI. A publication aggregator pulls OpenAlex data and tracks output by author, unit, and college. A centralized Faculty Dashboard visualizes KPIs that go well beyond publications: grant submissions, awards, extramural and institutional salary coverage, all over time. Unit-level trends are superimposed to support real-time benchmarking for faculty, chairs, and deans. Most notably, the dashboard is being used in a piloted annual review process that combines research analytics with user-entered teaching and service data for holistic year-end reports. That is dashboard-as-workflow, not dashboard-as-poster.

C2: Bridging Data Silos With Smartsheet, Tableau, and Power BI

Anna Romanowska-Pawliczek (Stanford University)

Romanowska-Pawliczek tackled a question every analytics leader gets asked and rarely answers cleanly: which tool, when? Her department-led approach assigns roles. Smartsheet is the operational and workflow platform. Tableau handles exploratory analysis and visual communication. Power BI sits as the enterprise layer for integration and governance. The session used a real academic example to show how clarifying each tool’s job avoids the misuse patterns that plague mixed-platform shops. This is the kind of practical guidance that saves teams from a year of architectural drift.

3. Specialized Use Cases

Sessions in this group covered domains where generic dashboard templates simply do not work: compliance, audience tailoring, geospatial reporting, and a set of focused lightning talks.

C1: Research Security Training Compliance Dashboard

Elizabeth Larimore, Laura Lodder (University of Kentucky)

NSPM-33 and the CHIPS and Science Act now require research security training for covered individuals on federally funded projects, with DOE, NNSA, and NSF rollouts in 2025. Larimore and Lodder built a cross-team compliance dashboard tracking completion of these required trainings. The session covered database design that consolidates training records from multiple sources, the role of compliance experts in informing accuracy, transparency, and federal alignment, and a real-time interface for monitoring. This is dashboard work as audit defense, and the timing could not be sharper.

D4: One Dataset, Many Audiences

Taylor Fidler, Carrie Romo (Texas Tech University), Sarah Dasher, Abraham Silva (University of Arizona)

The same data tells different stories to different audiences. Fidler, Romo, Dasher, and Silva compared approaches at the University of Arizona and Texas Tech: flexible reporting frameworks aligning metrics with leadership priorities, user training and consultation, and secure, role-appropriate access. The takeaway is that audience-specific reporting structures meaningfully improve stakeholder engagement and the actual use of data in decisions. The session pairs naturally with I4: data literacy is what lets audience-tailored reporting land.

H2: Geospatial Research Administration

James Geiger (University of Nebraska)

Geiger argued that research administration data is geospatial whether anyone treats it that way or not. Funders and governing bodies are expanding geographic reporting requirements, and most research offices are unprepared. The session covered practical approaches to gathering, visualizing, dashboarding, and analyzing location-based data, with use cases spanning sources of funding, research partnerships, and a novel application of local research expenditure data for central space allocation and expenditure reporting. This was the most unusual angle in the theme, and probably the most underexploited capability for offices not yet investing in it.

B6: Lightning Talks on Practical Analytics, Connectome Tools, and Reviewer Matching

Alex Howard (Pepperdine University), Colin Edwards (OSU Center for Brain Injury Recovery and Discovery), Lori Ann Schultz (Colorado State University), Janki Patel (Columbia University)

Three lightning talks compressed serious work into short slots:

Stacey Croomes (USC) demonstrated Excel-based PI-facing financial dashboards that integrate hit-rate visualizations into monthly reconciliation workflows. Alex Howard (Pepperdine) presented a six-month migration of historical proposals and award data from legacy systems into a unified Cayuse dataset, with a Google Sheets dashboard for senior leadership covering Requested vs Awarded funding, Public/Private mix, and Grants Under Management.

Colin Edwards (OSU Center for Brain Injury Recovery and Discovery) showed an AI-powered Connectome Tool harmonizing siloed datasets (OpenAlex, Semantic Scholar, NIH RePORTER, NSF awards, SBIR/STTR, internal proposal/award/expenditure data) into a forward-looking strategy layer.

Janki Patel (Columbia) walked through a redesign of reviewer matching for internal funding programs after benchmarking Harvard, MIT, Brown, Penn, and Dartmouth. The redesign combines an enhanced taxonomy with AI-assisted methodology.

Three different scales, three different stacks, the same underlying instinct: harmonize the data, then design for the decision.

Key Takeaways

Reading across the thirteen abstracts, four patterns stand out.

1. Definitions before visuals. B3, B5, and G4 all spend serious time on KPI definition, denominator clarification, and federated source coordination. The dashboard is the last step. Institutions that try to build first and define later end up with screens nobody trusts.

2. Audience is a design input, not a footnote. D4, I4, and I2 treat audience analysis as a first-class part of dashboard design. A dashboard for a department chair is not a dashboard for a provost. Pretending otherwise produces tools that get opened once.

3. Live data is the new baseline. F4, A6, and G4 all emphasize live refresh against governed warehouses. The era of static screenshots, monthly Excel exports, and quarterly PDF reports is closing. If a dashboard cannot answer “as of when?” with a precise timestamp, it is already legacy.

4. Specialized beats generic. C1 (compliance), H2 (geospatial), G4 (faculty performance), and B6 (reviewer matching, connectome tools) all show that the most interesting work is happening in domain-specific dashboards where the generic vendor template falls short. The next wave of value is in offices that pick a hard, specific problem and build for it.

If you missed the conference, the two sessions worth seeking out in the recordings are I2 (Krys) for the narrative-design framing that gives the theme its name and G4 (Lipton, Kuhn, Bach) for the most operationally ambitious working system. If you take home one principle, make it this: lock the definitions, know the audience, and only then open the design tool.

Series: Part-01 |

This is a direct copy/paste from a webinar i gave recently.

Who am I

• Robert Pilgrim, Ph.D. | rpilgrim@uark.edu

• Associate Director of Data Strategy Insight

• Office of Sponsored Programs, UARK

• B.Sc Astrophysics | Ph.D. Space Science

• My Website rpilgrim.com | LinkedIn) | Substack

Purpose of these Webinars

• Met many people:

  • Conferences / Summits

  • Workshops / presentations

  • REACH mentoring

• Most people just need help starting
  

A Note on Data

• BIG friction “no data access”

• Dummy data or public data (ASU)

• No direct system access needed

• Federal: HERD, IPEDS, SBIR/STTR

• Let’s get started...
  

Session Framework

Simplified Data Workflow

Power BI Desktop

• 100% Free, no restrictions

• Why need a license?

• Publishing, sharing, collaboration
  

Power BI Service

• Cloud based

• Share / Collaborate

• Microsoft campus?

• app.powerbi.com

Power Query Get Data

• Wide range of data sources

• Same cloud / desktop / Excel

• Code / No-code

Power Query Transform your Data

• Connect , Clean, Transform and Merge

• Code directly or GUI

• Python, R and SQL

The Data Model

• Relationships NOT merging

• Similar to SQL relationships

• Handles very large data

• Powered by Data Analysis Expressions (DAX)

Data Analysis Expressions (DAX)

• Data Models = BRAINS | DAX = LOGIC

• Powerful language

• Not coding, it is an expression language

• Built for cross-data analysis

• Examples later
  

Core DAX Function Groups

• DAX function reference

• We write DAX later

• Date and Time

• Filter

• Time Intelligence
  

Deployment Models

• Single report does everything

• NOT scalable, breaks easily

• SPLIT into 2 buckets

• Data Models | Reporting

Is it Time to Split the Data model from the Report?

• Multiple reports, same data?

• Easy to refresh?

• What if data source changes?

• Local files or cloud?

The Data Model as Foundation

• The Brain (Remember connect not merge)
  

Reporting Layer

• Multiple reports, one data model

• Same data, different story

  • Executive

  • OSP Director

  • Department Head

  • Grant Specialist

Deploying Reports

• Report >> Users

• Quick one-off share?

• How many users?

• Need to download data?

• Draft or production?

Deployment Options

Power BI Service Workflow

• Desktop >> Workspace >> Service >> APP >> Mobile

• Primary way to share

• Workspace: Staging area, build and test

• Apps: Polished reports for end-users, access control

• Mobile: Apple and Android

Let’s Build a Report

• Any questions?

• This report will be used each session

• Download file and data here

• Final file: XXX

The Data

• Dummy proposals data

Demo Live

• Page-1

  • Replicate the tables on the left

• Page-2

  • Expandable table

  • Filter improvements

• Page-3

  • Additional visuals from store

Live Demo Page 1

Live Demo Page 2

Live Demo Page 3

TL;DR: Power BI empowers users to publish reports to the cloud via Microsoft Fabric and share with Specific People, reducing dependence on IT support. This enables truly lean analytics teams to work more efficiently. Recipients receive a link and can access shared reports through the “Shared with Me” section, making it an ideal solution for small teams

Series: Part-01 | Part-02 | Part-03

Sharing is Caring

Power BI offers several methods for sharing reports, which we’ll explore in order of increasing complexity over the next few articles. One of the key attractions of Power BI, especially when compared to tools like Tableau, is its ability to empower users to work independently from their institution’s or company’s IT department. While IT teams are essential for broader organizational initiatives, they may not always respond to requests as quickly or with the level of attention you need (see this article to learn more https://researchanalytics.substack.com/p/the-university-data-wars-embracing)

The Power BI and Microsoft analytics ecosystem is vast and deeply integrated, while it's easy to get started with basic reporting, advanced users must consider how much they rely on others for administrative tasks. These tasks include: Creating and maintaining security groups, publishing reports, loading external data, running server queries, managing firewall issues, and more. Fortunately, many of these tasks can be handled without needing administrator access in Power BI.

Cloud-Based Analytics

If you want to avoid manually sharing your Power BI Desktop (.pbix) files, you’ll need to publish your reports to the cloud. Microsoft simplifies this process with its comprehensive cloud platform, now known as Microsoft Fabric.

Microsoft Fabric is an end-to-end analytics and data platform designed for enterprises. It integrates data movement, processing, ingestion, transformation, real-time event routing, and report building into a unified solution. It also provides services for Data Engineering, Data Science, Real-Time Analytics, Data Warehousing, and more.

For more details, refer to the Microsoft Fabric Overview.

Publishing Reports to the Cloud

Once your report is ready, you’ll need to publish it to the cloud within a Workspace (think of it like a folder). You can use the default “My Workspace” for simplicity. From your Power BI Desktop file, click “Publish “After clicking "Publish," you’ll be given a link to your workspace—click it to access your published report. Alternatively, you can always return to Power BI online via this link (link to your local Power BI Shared folder)

Your Workspace

When you enter your workspace, it should look something like this. To open your report, click on the file (e.g., “My First Report”) ensuring that the file type is set to "Report."

The Home Screen

The home screen provides basic navigation options on the left-hand panel. Take a moment to explore these options and familiarize yourself with the layout, then click your report to open up the additional options.

How to Share a Report

To share your report, first, open it and focus on the navigation bar at the top. While the File menu offers some sharing options, simply providing a link to your audience isn’t enough—they must also have the necessary permissions to view the report.

For now, let’s use the Share button located two items down from the File menu. Once clicked, you’ll see a few sharing options. Select Specific People and carefully review the permission settings at the bottom. Do you want the recipient to share the report with others? Should they have access to the underlying data? For now, it’s best to leave both options unselected.

What Happens Next?

Congratulations! You’ve successfully shared your first Power BI report. When you update and republish this report using Power BI Desktop, any changes will be reflected instantly in the cloud version that your audience has access to. Recipients will receive an email with a link to your report, and they can also find it under the “Shared with Me” section in their Power BI portal.

https://app.powerbi.com/browse/shared or click here

Managing Permissions

To manage permissions, click the Share button again, then the ellipsis (three dots) to access the Manage Permissions option. From here, you can add or remove users as needed.

Conclusion

This method of sharing is ideal for small, manageable groups. Power BI makes it easy to get started—create a report in Power BI Desktop, publish it to the cloud, and share it directly from the report interface. However, this approach becomes less feasible when dealing with multiple reports or larger audiences with different access levels. Still, for small-scale sharing, it’s a simple and effective solution.

TL;DR: Power BI offers several ways to share reports, including workspaces for team collaboration and apps for broad distribution. Tailoring your sharing methods based on the audience—whether it's leadership, department heads, or the public—helps optimize the impact. Workspaces are best for ongoing, team-based projects, while apps are ideal for distributing finalized content to larger audiences.

Series: Part-01 | Part-02 | Part-03

Sharing my Reports

The diagram below outlines various ways to create a shareable Power BI report. We'll explore this workflow in detail later. For now, if you're new to Power BI and interested in sharing, you've likely used simpler methods like direct data import or file connection. Regardless of how you created your report, our focus will be on effective sharing techniques for your audience.

Determine Your Audience

Before diving into sharing methods, it's crucial to identify your audience. In a university setting, different stakeholders require different levels of detail and presentation styles:

• The Chancellor: Needs concise, narrative-driven summaries of university performance.

• Vice Chancellors: Require high-level overviews of key performance indicators.

• College/Department Heads: Need detailed, department-specific analytics.

• Unit Directors: Focus on operational metrics relevant to their specific areas.

• Members of the Public: Require access to user-friendly dashboards with key public-facing metrics.

Understanding your audience will help you choose the most appropriate sharing method and tailor your content accordingly.

Read this article for more information on the impact of the target audience will have on your analytics projects.

Understanding Power BI Workspaces and Apps in a University Setting

Power BI offers two powerful features for organizing and sharing content: Workspaces and Apps. Let's dive into what these are and how they can be effectively used in a research context.

Power BI Workspaces

What is a Workspace?

A Power BI workspace is a collaborative environment where teams can work together on collections of dashboards, reports, datasets, and dataflows. Think of it as a secure, shared folder in the cloud specifically designed for BI content.

Key Features of Workspaces:

Collaboration: Multiple team members can contribute to and edit content within a workspace.

• Access Control: Administrators can set different levels of access (Admin, Member, Contributor, Viewer) for each team member.

• Organization: Workspaces help with logically grouping related content together.

• Version Control: They support multiple iterations of reports and dashboards.

How Workspaces Can Be Used in University Analytics:

• Research Project Collaboration: A research team working on a multi-year grant could use a workspace to collaborate on reports tracking grant expenditures, research outputs, and project milestones.

• Departmental Analytics: The Biology Department could have a workspace where various faculty members contribute reports on different aspects of the department's performance.

• Cross-Disciplinary Studies: For interdisciplinary research projects, workspaces can serve as a central hub where researchers from different departments can share and collaborate on data analysis.

Power BI Apps

What is an App?

A Power BI App is a curated collection of dashboards and reports packaged together for distribution to a broad audience. Apps are created from workspaces and are designed to be a polished, final product for consumption.

Changes in workspace reports and dashboards do not move into the App until manually triggered via an App update

Key Features of Apps:

Wide Distribution: Apps can be easily shared with large groups or the entire organization.

• Controlled Updates: Content creators can control when updates are pushed to users.

• Customization: Apps can be tailored for specific audiences or purposes.

• Easy Access: Users can find and install apps from a centralized location (AppSource).

How Apps Can Be Used in University Research Analytics:

• Research Performance Dashboard: The Office of Research could create an app showcasing overall university research performance, including metrics on publications, citations, and grant funding.

• Departmental Insights: Each academic department could have its own app, providing faculty and staff with key metrics relevant to their area.

• Public-Facing Research Impact: An app could be created to showcase the university's research impact to the public, potential students, and funding agencies.

Workspaces vs. Apps: When to Use Each

Use Workspaces When:

• You need a collaborative environment for ongoing work.

• You want to keep work-in-progress separate from finalized content.

• You're working with a defined team on a specific project or area.

Use Apps When:

• You want to distribute finalized content to a broad audience.

• You need to control when updates are released to end-users.

• You want to create a polished, curated experience for consumers of your analytics.

Using an App: Example Scenario in University Research Analytics

Imagine a large-scale climate research project:

Workspace: The core research team uses a workspace to collaborate on data analysis, create reports on various aspects of the study, and iterate on their findings.

App: Once key findings are ready, they create an app from this workspace. This app includes:

• An overview dashboard for university leadership.

• Detailed reports for other climate scientists.

• A public-facing dashboard showcasing key findings for the general public.

Distribution:

• The app is shared with the entire university.

• Specific reports within the app are embedded in the university's public website.

• The research team continues to work in the workspace, pushing updates to the app when significant new findings emerge.

By leveraging both workspaces and apps, the research team can collaborate effectively, control their narrative, and ensure that their findings reach the right audiences in the most appropriate format.

Conclusion

Effectively sharing Power BI reports within a university research context is essential for driving collaboration, informed decision-making, and broader impact. By understanding your audience and using Power BI's tools—such as workspaces for collaboration and apps for polished distribution—you can tailor your analytics to meet the diverse needs of stakeholders.

In the next post, we will explore the technical aspects of implementing these tools, ensuring that your analytics work can be efficiently deployed and managed across your institution.

Series Parts: Part-01 | Part-02

How can this article help you?

Leadership
If you are not getting clear, actionable insights from data reports, and data professionals looking to elevate their analytics to better inform decision-making. If you find yourself overwhelmed by data-heavy reports that lack strategic relevance or clarity, this guide will help you understand how to empower your data professionals to present information in ways that drive decisions.

Data Analysts
If you're aiming to transform complex reports into clear, strategic insights that resonate with university leadership. Looking to improve how you present data to drive impactful decisions.

Occasional Data Practitioners
For those occasionally tasked with analytics but lack formal training. If data analysis isn’t your primary role but you're expected to provide insights, this guide will help you simplify complex data and present it in ways that support decision-making without needing advanced expertise.

Over the course of this series, we'll explore:

• Techniques to distill complex data into clear, actionable insights

• Strategies for crafting narratives that align with institutional goals

• Best practices for creating visualizations that engage and inform decision-makers

• Methods to tailor your presentations for different stakeholders within the university

Whether you're looking to enhance your institution's enrollment strategies, optimize resource allocation, or demonstrate the impact of academic programs, this guide will equip you with the skills to elevate your data presentations from mere reports to powerful tools for driving institutional success.

Determine the Audience

TL;DR: Universities rely on data analytics to drive strategic decisions. Specialized professionals transform complex datasets into accessible visualizations and narratives, using advanced tools like Power BI to create informative dashboards and reports for university leadership with the goal of telling a data story.

These analytics are customized for different groups:

• The Chancellor: Gets concise, narrative-driven summaries of university performance. Uses these visualizations to communicate with stakeholders and support strategic messaging.

• Vice Chancellors: View high-level overviews of key performance indicators. Obtain insights aligned with strategic objectives and initiatives.

• College/Department Heads: Receive detailed, department-specific analytics. Access interactive visualizations for in-depth data exploration.

• Unit Directors: Focus on operational metrics relevant to their specific areas. Utilize data to monitor and improve day-to-day processes and outcomes.

• Members of the Public: Access user-friendly dashboards with key public-facing metrics. Explore transparent data on university performance

The core principle is to distill complex university data into clear, actionable insights.

Communicating Analytics to Leadership

Consider this common scenario….

You've spent weeks meticulously building a robust data model, equipped with key performance indicators (KPIs), time intelligence functions, and interactive, drill-down-enabled reports. The calculations behind the visuals are so intricate that even advanced AI models struggle to fully comprehend them. Your reports and code have transcended into more of a piece of art or poetry than coding. You feel confident that your 20-page analytics report, filled with rich insights, will somehow earn you a Nobel prize of some sort.

However, after delivering the report, you sit back and await the inevitable follow-up …silence!

Despite, in your opinion, the technical brilliance and the abundance of meaningful data, leadership may still struggle to engage with the insights you’ve presented.

You, as an analytics professional have failed in your mission to translate your knowledge and insights into tailored stories, specifically shaped for the audience looking at your work.

Why does this happen, and what can you do about this?

The Disconnect Between Data and Decision-Making

For leaders, whose primary focus is on strategy, vision, and long-term impact, raw data—no matter how elegantly structured—can be overwhelming. Leadership doesn't necessarily want to sift through complex analytics or wrestle with data points; they need clear, actionable insights that align directly with their strategic goals. Reports and dashboards, while essential, don't tell the full story. They need to be part of a broader narrative that connects the data to the institution's mission and key challenges.

Delivering actionable insights to leadership takes more than just a collection of reports, dashboards, and KPIs.

This is where the power of storytelling in data analytics comes into play. Beyond just presenting the numbers, your role is to guide leadership through the data, drawing their attention to the most critical insights, and translating those insights into potential actions. A beautifully designed dashboard is only as effective as the narrative that accompanies it.

Conclusion

The journey from data analytics to leveraging data in decision-making requires more than technical proficiency—it demands the art of storytelling. As analytics professionals, our challenge is to transform raw data into compelling narratives that resonate with leadership. By presenting data in the context of institutional goals, and distilling complex insights into actionable strategies, we can help decision-makers to drive meaningful change. Ultimately, the success of data analytics reports and projects lies not just in the numbers, but in how well we communicate their relevance and impact on the future of our institutions

TLDR; Without the installation of additional software the only way to keep published Power BI file synced with the latest data in local Excel files is with SharePoint or OneDrive via their URL connection strings. When setting up the initial data load, make sure to use “Connect to SharePoint” and do not make the mistake of connecting a your local SharePoint folder on your machine.

The Challenge

If you're relying on Power BI for your analytics, you might find yourself caught in a tedious loop of manual updates to ensure your reports reflect the latest metrics from your team's Excel files. Yet, there's a streamlined approach that leverages cloud technology to keep your strategic metrics aligned with real-time changes.

In the fast-paced world of strategic management, maintaining up-to-date accuracy in metrics is crucial. The challenges of keeping diverse teams aligned on the latest data, especially when dealing with files updated by multiple stakeholders, can significantly impact decision-making processes.

Identifying the Bottleneck

The conventional method involves a local file system where Excel files are updated, necessitating a manual refresh of your Power BI reports, followed by saving and republishing to ensure the latest data is available in the cloud. This approach not only consumes valuable time but also introduces delays in data reflection, which could lead to strategic missteps. The underlying issue is the disconnection between local updates and the cloud environment where Power BI reports reside, leading to a gap in data synchronization.

Correct Workflow

The essence of a more effective strategy lies in cloud connectivity. By shifting the focus from local file systems to direct integration with SharePoint or OneDrive, where your Excel files are stored, you can automate the data refresh process in Power BI. This pivot not only enhances efficiency but also ensures that strategic metrics are consistently up-to-date, facilitating more informed decision-making.

Implementing the Change

To transition to a cloud-integrated approach, consider the following steps:

• Align Accounts: Ensure that your Power BI and SharePoint or OneDrive accounts are synchronized, using the same credentials for seamless integration.

• Maintain Connectivity: Stay logged into Power BI to prevent disruptions in data flow, keeping in mind the platform's timeout features.

• Link to the Cloud: Connect your Power BI directly to the Excel files stored on SharePoint or OneDrive, enabling real-time data synchronization.

Connecting to SharePoint URL

The process is relatively easy, all you need to do is have the SharePoint URL handy, and make sure you are using the same log in credentials for both your Power BI file, and the SharePoint account you wish to connect to.

Conclusion

For leaders and teams focused on strategic metrics, moving to a cloud-based synchronization model with Power BI is a transformative step. It removes the friction of manual updates, bridges the gap between data generation and analysis, and ensures that strategic decisions are informed by the most current data. As we navigate an increasingly data-driven world, the ability to swiftly adapt and respond to new information is paramount. By leveraging cloud integration for strategic metrics, organizations can achieve a competitive edge through enhanced responsiveness and informed decision-making.

Introduction

At its core, a research network is an interconnected system of researchers, institutions, and their collective works. Picture a stack of post-it notes, each representing a researcher or an institution. The lines drawn between these notes symbolize the collaboration, shared knowledge, or co-authored research papers between these entities. This visual analogy provides an intuitive understanding of the intricate nature of research networks. Their importance lies in the facilitation of collaboration and knowledge sharing, encouraging cross-disciplinary studies, and highlighting potential opportunities for future work.

Visualizing Research Networks

To comprehend the complexity and interconnectedness of research networks, visualization is a key tool. Various methodologies and software tools, ranging from network graphs to advanced computational models, are deployed to create these visual representations. By breaking down the complex structure of research networks into a visual format, we can better analyze, understand, and leverage the connections within these networks.

The Storytelling Power of Research Networks

If a picture is worth a thousand words, then networks are worth even more. Networks let us turn a big set of data (like all the proposals and awards your institution has had in the last five years) into a picture that shows what your research looks like. Just like a story, a network has a start, middle, and end:

• Start: Before an event (like ten years ago before a new research building was built)

• Middle: The event (like when the new building was finished to improve research)

• End: After the event (like today, looking at what has changed)

For example, you might want to understand how research collaborations have changed in the last five years because of hiring new people. What did the networks look like before, and what do they look like now? How have they changed? Did these new people make a big impact on the networks? How can we use this knowledge to improve future collaborations?

Exploring the Uses of Research Networks

Research networks can be used in many different fields. They are a way to share resources, spread new findings, encourage collaborations across different fields, and track the impact of research in different areas. These uses are helpful not only for individual researchers, but also for institutions, by helping with strategic planning, identifying research trends, and promoting a culture of working together.

Example Applications

• Research Administration or Grants Specialist

  •  Understanding your research landscape

  • New employee onboarding

  •   Collaboration trend analysis

• Leadership

  • Exploration of how research collaborations have changed over time

  • Visualizing the increase in time and complexity due to the rise if interdisciplinary collaborations

  • Staff turnover, who did they work with, what does the visual representation look like on your network graph.

• Executive Leadership

  • Strategic hire impact on collaboration 

  • Faculty departure analysis on collaboration landscape

  • Analysis of faculty startup funding

Network Applications

The applications of research networks are as diverse as the disciplines they encompass. They serve as a platform for sharing resources, disseminating new findings, fostering cross-disciplinary collaborations, and even tracking the impact of research across different domains. These applications are not only beneficial to individual researchers but also to institutions, by aiding in strategic planning, identifying research trends, and promoting a culture of collaborative research.

Processing Your Sponsored Research Data

At the simplest level, you just need two pieces of information: the Principal Investigator (PI) Name and Co-Investigator (CoI) Name. You can also include more information, like dates, sponsors, titles, and so on. You can even add other data, like information from Human Resources (ethnicity, gender, race, ranks, etc.), information about research space assigned to researchers, publication data, and other research outputs. This information can all be connected in the network and used for powerful analysis. The PI and CoI are connected by lines and placed according to their collaboration status in the network.

"PI Name" -> "CoI Name"

Conclusion

In conclusion, research networks, with their complex structure and diverse applications, are a cornerstone of modern research. The advent of advanced AI models like ChatGPT is set to further revolutionize our understanding and usage of these networks. As we continue to delve deeper into the realm of research networks, it is anticipated that we will uncover more innovative ways to harness their potential. The future of research networks is indeed an exciting prospect, and we stand on the precipice of new advancements in the field.