hdaa 2023
analytics with altitude
poster abstracts
A Mile-High of Analytics puts Houston Methodist on the Mountain Top
The Houston Methodist Physician Organization (HMPO) started 15 years back with around 30 Providers and has now grown to 1200 Providers with 1.6 million clinic visits. The growth was a result of outstanding leadership, exceptional team-work, and excellent tools, and technology. The adage, “You can’t manage what you can’t measure” is part of the culture at the HMPO. Measurement and benchmarking are in our DNA and has helped us to achieve these outstanding results. Over the last few years, the HMPO has embarked on a Self-Service Analytics Portal to provide Leadership with information at their fingertips to drive performance and growth. We have deployed over twenty dashboards in areas of Access, Scheduling, Referrals, Virtual, Patient Quality, Covid Vaccine, TeleStroke, and Revenue Cycle. These dashboards contain over 100 self-service metrics relevant to the HMPO such as Provider Utilization, Schedule Utilization, No Show Rate, Canceled Rate, Referral Rate, In-Clinic Visits, Virtual Visits, A1C Poor Control Rate, Colorectal Cancer Screening Rate, Breast Cancer Screening Rate, Patient Satisfaction Rate, Denial Rate, Net Collection Rate. The HMPO Leadership regularly accesses these metrics in couple of clicks to monitor performance, diagnose problem areas to get to the root cause and implement improvements. We will showcase screenshots of the Portal and the dashboards and will detail several use cases where the dashboards and metrics have provided value and helped improve performance. We will walk through our methodology to implement a dashboard from Epic Clarity and the Portal, which can serve as key takeaways for organizations wanting to deploy something similar. This has been an exhilarating journey with valuable lessons learned and we look forward to adding more components to the Portal to make it a Total One-stop Shop of Analytics for the Houston Methodist Physician Organization.
Automated Generation of Study Controls in the EMR
Well-designed randomized trials provide high-quality clinical evidence but are not always feasible or ethical. In their absence, the electronic medical record (EMR) presents a platform to conduct comparative effectiveness research, central to the emerging academic learning health system (aLHS) model. A barrier to realizing this vision is the lack of a process to efficiently generate a reference comparison group for each patient. Our objective was to test a multi-step process for the selection of comparators in the EMR. Prioritized algorithm inputs were collected from the EMR and applied using a greedy matching technique. Feasibility was measured as the percentage of patients with 100 matched comparators and performance was measured via computational time and Euclidean distance. The final process successfully generated 100 matched comparators for each of 1.8 million candidate patients, executed in less than 100 min for the majority of strata. We concluded that EMR-derived matching is feasible to implement across a diverse patient population and can provide a reproducible, efficient source of comparator data for observational studies, with additional testing in clinical research applications needed.
Central Lines in Real Time
Clinical outcome data is often utilized to identify gaps and illustrate where action can be taken throughout the clinical system. However, there is a challenge to make the data actionable in the moment as there is often a delay in aggregating and analyzing over time. Children's Hospital Colorado (CHCO) has been working to reduce Central Line Associated Bloodstream Infections (CLABSIs) since 2008. For many years, CLABSI prevention work has been focused on the auditing practice from documentation and nurse interview against an evidence-based care bundle. Despite bundle reliability, CHCO continues to have CLABSI events. Starting last year, CHCO has moved to real-time observation of care and reporting upcoming or overdue care in an effort to use data to act in the moment to provide real-time feedback on processes and overdue cares. This presentation will take you through the process we followed to generate reports to impact our clinicians' workflows and empower clinical leadership to make real-time decisions.
Conquering Data & Analytics Challenges for a Successful EHR Transition
This presentation highlights St. Jude Children's Research Hospital’s journey through its EHR transition and how they overcome the data analytics challenges to ensure a smooth transition for its end users. St. Jude decided to transition to a new EHR system to equip the end users with the latest functionalities and move away from the previous EHR system that was in use for 15+ years. An EHR system is as good as the data it generates. All EHR systems come with their own data & analytics capabilities. When organizations transition from one EHR system to another, they need to be aware of the strengths and weaknesses present in the EHR system they are transitioning to and more importantly have a plan to overcome those weaknesses. Successful EHR transition comes with many challenges from inception to implementation and beyond. We plan to focus on the data & analytics challenges in this presentation and provide a framework that other organizations can employ to overcome common pitfalls and learn from our experience. We plan to share how our approach to understanding the current landscape of data & analytics usage among the end users and aligning them to the capabilities of the new EHR system enabled us to unearth the gaps early on and prepared us to address those gaps before the go-live. The framework we used will provide insight that other organizations can use to create a plan to anticipate potential needs from end users and proactively address them to minimize risks and increase the likelihood of a smooth launch. Achieving these goals, especially in a Healthcare setting requires careful planning, coordination, and execution. By leveraging the insights gained from our transition experience, we hope that other organizations can avoid common pitfalls, adopt best practices, and achieve better outcomes without compromising patient care.
Creating Dynamic Parameters in Tableau
I created a Anesthesia Efficiency dashboard in which you need to know the first case of the day and the last case of the day within parameter filters. Easy to do when parameters are stagnant because can just do this piece in sql but becomes tricky when parameters are dynamic. I also use time parematers that don't look at a 24 hours period but a 48 hour period so you can start at night and crossover into the next day.
Data Mart for CAP Cancer Checklist Synoptic Data from Epic Beaker
Synoptic pathology results encode into discrete values data points previously only obtainable via NLP or chart abstraction: anatomic site, histology, grading, TNM staging, and others. Epic Beacon has implemented SmartData Elements to capture synoptic pathology results via the College of American Pathologists (CAP) Cancer Checklists. The native Epic SmartData data model is not easy to use for analytics. The Northwestern Medicine Enterprise Data Warehouse (NMEDW) has built an optimized data mart of synoptic pathology data that simplifies and normalizes the retrieval of synoptic pathology data from Epic.
Data Governance: From Committee to Program
University of Utah Health's data governance has been evolving over the past several years. We have gone from wild west to a Data Governance Committee to a Data Governance Program. Our CMIO formed an official Data Governance Committee about four years ago. The chairs of the committee (myself and our EDW director) quickly learned that effective data governance is more than just a committee that meets once a month to prioritize EDW data projects. Although the Data Governance Committee is needed as a community of practice in data usage to help make decisions, our data governance has evolved into a program with established ownership, stewardship, standards, and processes to manage data as a critical asset.
Enabling Collaborative Research in the Era of Heightened Sense of Privacy and Security
Clinical and translation research is essential to knowledge discovery and clinical innovation that can transform patient care and improve patient outcome. Frequently researchers need access to EHR systems to perform chart reviews, identify patients for recruitment, and assess outcomes. This access often needs to be extended to medical trainees and non-employee research collaborators to leverage their clinical expertise, accomplish education missions, and augment research capacity. At the same time, to protect patient privacy, enforce information security, reduce legal liability, hospitals are increasingly keen to restrict access to their EHR systems. Striking a balance between the opposing forces, the two options that’s currently available - full access vs no access, is no longer feasible. In this talk, we present two new ways to restrict EHR access for research, leveraging Epic’s auditor access for fixed contents chart review and workbench reporting functionality for full cart review, both on a restricted patient list. We will also present how we have created a new workflow and governance for non-employee EHR research access through building consensus and productive relationship among key stakeholders in Information Security, Privacy Office, IRB, Compliance, Legal, and research leadership. In this talk, we will also share how we navigated the challenges of supporting research needs while enforcing research compliance in our REDCap environment, which is an Electronic Data Capture platform especially for clinical research. Standard Operating Procedure coupled with a multidisciplinary “research compliance response squad” were the key to identify issues proactively and address them timely Harmony among research flexibility, corporate compliance, patient privacy, information security, and legal protection can be achieved through deliberate efforts and planning.
Evolution of Business Intelligence: From Standard Tables to Interactive Dashboards for Enhanced Analytics
Effective data visualization is crucial for extracting valuable insights from complex datasets. In this presentation, we emphasize the importance of data visualization in healthcare and university analytics, and address the challenges faced by academic centers. We showcase case studies illustrating successful data visualization implementation in healthcare research, School of Medicine administration, and COVID response. Through training programs and self-study, we have enhanced data visualization skills among healthcare professionals and researchers. The implementation of these best practices has resulted in improved data accessibility and enhanced collaboration. We highlight the significance of knowledge sharing and collaboration in advancing the field of data visualization in healthcare analytics. This presentation offers valuable insights and guidance to inspire others in leveraging data visualization to drive improvements in healthcare delivery and outcomes.
Exploratory Dashboards in the Pursuit of Magnet Designation
Magnet is a highly sought-after nursing excellence designation for health care organizations. In 2022, just under ten percent of hospitals in the U.S. were designated Magnet. These hospitals embarked on multi-year journeys to meet the Magnet requirements. Two such requirements include outperformance of national benchmarks on 1) a RN satisfaction survey and 2) a clinical indicator for patient falls with injury. Magnet outlines strict requirements for both measures. Magnet also provides flexibility in the form of choices that hospitals can take advantage of. For the RN satisfaction survey, hospitals can choose four out of seven nurse engagement categories and they can select from 12 vendor-provided benchmark groups. For the patient falls measure, hospitals can select from the 12 benchmark groups and from different measures of central tendency. Historically, our hospital explored these options on a limited basis because it was done manually. Nurse leaders spent weeks reviewing vendor PDF reports to compare our hospital’s performance using different nurse engagement categories, benchmark groups, and measures of central tendency. We now have a more efficient method that involves the following steps: 1) Download numerous Excel files from the Magnet-approved vendor, 2) Use Tableau Prep to combine the files, transform them, and add calculated fields that will be used in the dashboard, and 3) Use Tableau to provide interactive exploratory dashboards. Tableau Prep and Tableau Desktop are both offered as self-service analytic tools at our hospital. With minimal training, we were able to produce two exploratory Magnet dashboards that allowed nurse leaders to make informative comparisons and ultimately sound decisions on when to conduct RN surveys and how much energy and resources to devote to reducing our injury patient fall rates. Finally, our new Tableau-based processes can be easily repeated and updated for these and other Magnet measures in the future.
External Clinical Data Sharing at Michigan Medicine
Michigan Medicine is a large academic medical center with over 2.6 million patient clinic visits, 46,803 hospital discharges, and 111,247 emergency department visits in FY2022. At an institution of this size, sharing data both internally and externally plays an integral role in various operations, such as enterprise operations, patient care, research innovations, and national partnerships. When sharing data external to the institution, it is essential to have an established process to ensure that data are handled appropriately, as well as being used for relevant purposes. Michigan Medicine is developing this process for clinical data to aid departments who struggle to share data externally. In a large, decentralized data and analytics environment, processes for sharing data can exist outside of data governance and often focus on specific use cases. At Michigan Medicine, the Office of General Counsel, Information Assurance, and the Compliance Office each had their own process for vetting part of a data sharing request. Led by Data Governance, the Office of General Counsel, Information Assurance, and the Compliance Office, the External Data Sharing Committee is developing a unified process for sharing clinical data externally. The process utilizes existing resources and pathways to gather information about requests, proceed with reviews from stakeholder groups, and allow the Committee to make decisions about whether data can and should be shared. By considering diverse perspectives in the development of an external clinical data sharing process, Michigan Medicine is ensuring that patient data is secure and trusted when being shared outside of the institution.
HDAA Women In Analytics Special Interest Group
Healthcare Personnel (HCP) COVID-19 Vaccination Reporting
Baptist Health South Florida is mandated by the Centers for Medicare and Medicaid Services (CMS) to enter healthcare personnel (HCP) COVID vaccination data for our hospitals and ambulatory Surgery Centers (ASCs) into NHSN beginning October 1, 2021, for 1 week of every month. Our team was given the task to construct and implement a technology solution for accurate and timely data collection and submission into NHS.
Implementation of a Self-Serve Synthetic Data Platform at the Ottawa Hospital: Benefits and Lessons Learned
The Ottawa Hospital is the first Canadian healthcare institution to have implemented MDClone, a self-serve synthetic data platform that enables fast, secure, and dynamic access to health data. MDClone is a user-friendly technology that clinicians, researchers, and hospital staff can use to generate synthetic datasets that are statistically comparable to real data, or to output original datasets. Access to secure and credible synthetic data that does not require ethics approval, and access to original data in a self-serve and interactive manner can help reduce time- and administrative-related barriers to data access for hospital operations, quality improvement, and research initiatives. At the Ottawa Hospital, MDClone was first introduced as a pilot project with historical patient and hospital data available in the platform. This has now expanded to include current data from our Epic medical record system, loaded nightly, resulting in valuable real-time access to health information. During the pilot phase, we trained and onboarded 226 users in 19 months, and within one month of the full-scale launch of MDClone at our center an additional 33 users have been trained. Since the initial go-live, over 800 data sessions have been created in MDClone at our hospital, and more than 1000 datasets downloaded of both synthetic and original data. After a 3-year implementation journey, we hope to share insights on our implementation process, benefits observed, and lessons learned from introducing MDClone at our institution.
Improving OR Efficiency through Extension of Epic Clarity into Custom Caboodle DMC to Create SANDPO SlicerDicer Data Model
This presentation will focus on Extending Epic Clarity database into Custom Caboodle DMC to create SANDPO Slicerdicer Data model to improve OR efficiency at UI Health, allowing for easy access and analysis of OR utilization. SANDPO stands for: S (surgeon attestation), A (Anesthesia sign-off), N (Nursing assessment complete), P (Patient is ready) & O (OR Room Ready). Understanding of SANDPO metrics allows to identify areas of improvement in the OR workflows and optimize the use of resources. By leveraging the custom SANDPO Slicerdicer Data Model, OR Managers can track and measure key performance indicators (KPIs) related to OR efficiency, such as case duration, turnover time, add-ons, and overall surgical cases. This allows them to monitor their performance over time, identify areas of improvement, and implement targeted interventions to improve OR efficiency. The presentation will also provide a case study within UI Health on this development and the outcomes achieved. Our goal is to demonstrate on how the development of SANDPO data model can help make data-driven decisions that enhance patient care and drive better business outcomes.
Increasing Efficiency in Tracking and Reporting on Research Data Requests
The Office of Informatics fulfills hundreds of requests for research data each year. We identified a need to better track metrics related to these data requests and formed a work group to address this. In the initial meetings we identified both metrics that were critical to track and metrics that would be helpful to track and were not difficult to obtain. We built and refined the dashboard using the connection feature to extract the request data from its Postgres database and the dashboard was published to Tableau Server. The dashboard displays overarching metrics, with the option to link out to pages with related supplemental information. Project managers and team leaders use this to monitor how long tickets are in each status, increase quote accuracy using the hours quoted and hours billed charts, and examine ticket complexity over time. Utilization of the dashboard has been written into multiple SOPs. By providing information on the quantity, size, and complexity of data requests, the dashboard enables the Office of Informatics to monitor how the process is functioning overall, make informed decisions about resource allocation, and provide quick interventions.
Integrating Patient-Reported Outcomes into a Research Data Warehouse
Patient Reported Outcomes (PROs) directly reflect patients’ views about their health and are an increasingly valuable measure for clinical research as they can contribute to more holistic clinical decision-making [1]. At Atrium Health Wake Forest Baptist (AHWFB), our primary research-focused data warehouse, the Translational Data Warehouse (TDW), includes clinical data from a variety of sources. To facilitate self-service access of this data for researchers, we use the Informatics for Integrating Biology and the Bedside (i2b2) data model. This poster will describe our approach to adding PRO data from WakeOne Clarity—the reporting database for AHWFB’s Epic Electronic Health Record (EHR)—to i2b2, focusing on solutions to technical challenges relating to data standardization, such as how to align local data and identifiers to national standards, and code generalization, such as how to handle differently structured PROs when creating a semi-automated ETL process. We also describe solutions which leverage the Unified Medical Language System (UMLS) Metathesaurus, a biomedical thesaurus which links concepts having the same meaning across various vocabularies [2]. [1], [2] See references in the Learning Objectives section.
Integrating Press Ganey Data into EPIC Caboodle Data Warehouse at UI Health to enable data driven decisions for improving patient quality outcomes
This presentation will focus on the integration of Press Ganey patient satisfaction survey results into EPIC Caboodle Data Warehouse at UI Health, allowing for easy access and analysis of patient feedback. Patient experience is a crucial component of healthcare quality, and patient satisfaction data provides valuable insights into this experience. The benefits of incorporating patient satisfaction data into EHR systems are numerous, including increased efficiency, improved patient outcomes, and better communication between providers and patients. We will discuss the technical aspects of this integration, including the data collection and mapping strategies. The analysis of this data will also be explored, including the use of data visualization tools to identify trends and patterns, as well as the potential for future enhancements such as natural language processing. The presentation will also provide a case study within UI Health on this integration and the outcomes achieved. Our goal is to demonstrate how the integration of patient satisfaction data into EHR systems can provide valuable insights for healthcare providers and enhance the overall quality of care for patients. We will be covering steps to bring the data into caboodle, challenges we faced and how we went ahead with a custom Slicer Dicer model within EPIC. We would also be demoing our Press Ganey Dashboard in EPIC.
Leveraging Amazon Web Services to analyze large data sets
Researchers at Emory University have the opportunity to use the Merative™ MarketScan® Research Databases for research on clinical topics. The MarketScan® Databases contain individual-level, de-identified, healthcare claims information from employers, health plans, hospitals, and Medicare and Medicaid programs and include data for over 273 million unique patients. Combined, the MarketScan® data available to Emory researchers is over 4TB of data. Due to limited local computing power and VPN timeouts, researchers were often unable to complete their analysis on a local computer. The Emory Data Solutions team created a cloud computing environment using Amazon Web Services (AWS) to help enable researchers to complete their analysis of the MarketScan® data. The AWS solution involves uploading the MarketScan® data to S3 buckets and creating an EC2 instance with SAS Software pre-installed and connections to the S3 buckets. Researchers can now create a copy of the EC2 instance using an AMI, perform their analysis, and save any output and/or filtered data sets to a personal S3 bucket for future use.
Michigan Medicine ICU Liberation Initiative: Development and Implementation of a Health System-wide dashboard to track the ICU Liberation A-F bundle compliance
The ICU Liberation campaign, developed by the Society of Critical Care Medicine, seeks to provide an evidence-based guide for clinicians to address the organizational changes needed for optimizing adult and pediatric ICU patient recovery and outcomes. Implementation of the ABCDEF bundle provides well-rounded patient care and optimal resource utilization which results in protecting patients from harm caused by pain, agitation/sedation, delirium, immobility, and sleep disruptions experienced in an ICU. Through the (A-F) Liberation Bundle, the ICU teams can assess and manage pain, delirium, and sedation levels. Additionally, the bundle provides guidance on ventilator weaning protocols, early mobilization, and family involvement contributing to more interactive ICU patients who can safely participate in higher-order physical and cognitive activities at the earliest point in their critical illness. Michigan Medicine's adult ICU teams have implemented the A-F bundle clinical processes, yet they needed a method for tracking unit compliance rates. The Quality Analytics team, working in conjunction with the Michigan Medicine clinical staff, created and implemented a dashboard which tracks monthly aggregate compliance rates for ICU units across the Health System. In addition to reviewing adherence to the A-F protocols, this automated process helped the ICU nurses, respiratory therapists, and ICU leadership identify areas of continuous improvement.
Optimizing Covid-19 Data to Manage Employee Health Across a Large Hospital
When the Covid-19 pandemic hit the city of Toronto, the small 6-person Health Services (HS) department at the University Health Network (UHN) found themselves unexpectedly at the forefront of the response effort. With over 17,000 employees requiring support and no resources available to optimize the existing system, the HS team resorted to manual paper-based processes. Recognizing the urgency of the situation, the UHN Data & Analytics team responded, consulted with the existing system vendor, and established electronic processes to help TeamUHN report on all COVID-19 employee-related requirements. This initiative served as a catalyst for the HS team to update all of their data collection methods and implement automated reporting that continues to be used today.
PTERADACTYL: People of The Extraordinarily Roundaboutedly Acronymed Data Analytics Club That You Love
PTERADACTYL is Michigan Medicine’s grass roots data and analytics community of practice. Over the past five years, it has grown into our most powerful tool for reaching analytics staff at the institution. Michigan Medicine has a highly decentralized data and analytics workforce that often struggles with a lack of standardization, variable skill sets, and no enterprise-wide service to directly support analysts. PTERADACTYL fills in these gaps through biweekly meetings, an active Microsoft Teams forum, and regular discussions to surface high priority issues. The current roster includes nearly 200 members but it began as a small team meeting held by analysts within our IT group. This poster will describe the growth of this community of practice over the years and highlight milestone projects such as improving analyst on-boarding, a mentorship program, and improving coding best practices. The poster will include a detailed breakdown of the membership, results of a recent survey regarding current pain points, and a breakdown of the topics covered at the biweekly meetings and on Teams. Additionally, it will describe how PTERADACTYL is used as a critical communication vehicle to reach these distributed analysts.
Restraints and Seclusions
Collaborating closely with Nursing Quality and the EDW, we designed and developed the Restraints and Seclusions report to track DNV requirements for reporting and monitoring restraint usage. This dashboard analyzes and classifies hospital cluster specific restraints applications, and will assist various departments with continued education for restraints, their usage documentation and compliance, any process change/improvement, and/or proposing discussions on applicable interventions.
Test Data Generation
The need for generating test data from production data is becoming increasingly important in the healthcare industry. However, to comply with the Health Insurance Portability and Accountability Act (HIPAA), any Personally Identifiable Health Information (PHI) data in the production data must be removed or de-identified before it can be used for testing. Applications contain datasets that maintain references to key data elements. Data in all the tables referencing a data element needs to be de-identified. In addition, Cloud Adoption is on the rise. Many providers are adopting stricter information security policies for cloud environments. This presents a challenge for healthcare providers who must ensure the privacy and security of their patients' information. To address this challenge, this paper proposes tools and methods for test data generation from production data that takes into account the need for HIPAA compliance. The method involves the use of de-identification techniques, such as masking or substitution, to remove or obfuscate PHI data in the production data. The resulting test data can then be used for testing without compromising patient privacy or violating HIPAA regulations. Overall, this paper presents a practical solution for healthcare providers who need to generate test data from production data while maintaining HIPAA compliance and ensuring patient privacy. At Memorial Sloan Kettering Cancer Center, we are in the early stages of adoption of test data generation tools. We hope to implement few use cases by the time we attend the convention and to share the best practices.
University of Rochester Medical Center EDA Research Operational Transition to OMOP CDM
The OMOP CDM (Common Data Model) provides a data standard for observational data to ensure research methods can be systematically applied to produce results that can be easily compared and reproduced. The factors driving the project include enhanced self-service capabilities for researchers, positioning for more self-service and large-scale analytics, the ability for researchers to create, share and/or adopt novel informatics tools and to facilitate internal and external collaboration opportunities. The poster will describe the reasons for implementing the OMOP CDM, the integration of OMOP into our Denodo Research Data Warehouse, the impact on SLA delivery time, lessons learned and next steps (creation of a Research self-serve de-identified OMOP data warehouse).
Unlocking the Value of Workforce Data through Analytics and HR Partnership
As an organization with more than 30,000 employees, Human Resources department at Memorial Hermann Health System aimed to gain comprehensive understanding of its workforce by learning key insights and patterns in its talent acquisition, retention, growth, satisfaction, and flow across all departments and job roles. To achieve this, the Enterprise Analytics, HR Analytics, and Technology teams collaborated to develop an analytics tool that provides a holistic view of employee and candidate insights. Our process involves extracting data from our HR system and transforming it using a custom program, resulting in a valuable data set that is easily consumed by the business intelligence tool. Prior to this tool, it took 24 hours to obtain some of this data, but now it's readily available and more accurate. The resulting dashboard offers HR leadership insights into key areas of interest, including job satisfaction and talent retention, providing a quick and intuitive way to identify strengths and opportunities with our workforce. This data-driven approach empowers HR leadership and MHHS to make informed workforce management decisions, resulting in more efficiencies. By leveraging this analytics tool, the HR department can identify patterns, make data-driven decisions, and improve workforce management, ultimately resulting in a more productive and satisfied workforce.
You've Cleaned Up Your Analytics Catalog, So Now What? One Organization's Journey Building a Catalog and How They Keep it Clean, Fresh, and Up-To-Date
In this compelling presentation, we delve into the journey of an organization's Analytics Catalog Program, emphasizing the analytics development process that produces insightful solutions all while maintaining a well-organized catalog. With the proliferation of data and analytics assets, maintaining a comprehensive and up-to-date catalog becomes imperative. This presentation highlights the significance of a catalog, details the process currently in place to clean and populate it, and outlines future plans for its enhancement. Explore the strategies implemented to ensure the catalog remains up to date, clean, and populated. Discover the automation tools utilized, governance processes established, and ongoing data quality metrics implemented to maintain the integrity of the cataloged assets. Finally, we peek into the organization's future plans for the Analytics Catalog Program. Discover the envisioned enhancements, such as advanced search functionalities, improved metadata management, expanded integration capabilities, data glossary, lineage and pictures. If you are an analytics professional, data steward, or involved in data governance, this presentation offers practical guidance on building and maintaining an effective analytics catalog. Join us to explore the transformative power of a well-organized catalog and gain inspiration for your own catalog initiatives.
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