At Unicon, our Data and Analytics services empower organizations to make data-driven decisions that drive meaningful outcomes. We don’t just focus on technical execution—we’re committed to continuously enhancing your data and analytics systems to ensure they deliver actionable insights that fuel better decision-making and improve the learning experience over time.
Table of Contents
- Data Analytics & Decision-Making in Education
- Using Data Aggregation Tools for Better Decision-Making
- Informing Education Preparation Programs: EPDM Starter Kit Webinar
- NCSU Improves Student Success Analytics with Learning Analytics Tech
- D2L Analytics: Data Extraction to the Unizin Data Platform (UDP)
- Student Groups and Machine Learning Clustering - Better Together
- Data Governance & Management
- Master Data Governance: High-Level Roadmap to Planning & Implementing
- Building Trust in Data: Data Governance Organization Structure
- What is a Data Catalog – And How Do You Adopt One?
- Data Analytics Issues: Overcoming Barriers to Effective Data Analytics
- The Modern Data Stack Components
- Optimizing Your Data Architecture: What is a Data Warehouse?
- Information Security & Privacy
- Data Storage & Infrastructure
Data Analytics & Decision-Making in Education
Using Data Aggregation Tools for Better Decision -Making
Informing Education Preparation Programs: EPDM Starter Kit Webinar
Texas is adopting the Ed-Fi data standard to help educator preparation programs (EPPs) centralize real-time candidate data for better decision-making. The Educator Prep Data Model (EPDM) Starter Kit offers an easy entry point for programs to connect candidate data across various sources, like student information systems and state certification data. This solution enables EPPs to aggregate and visualize candidate progress through intuitive dashboards, driving data-informed conversations about candidate success. In a recorded webinar, Unicon's Nichole Cota and Dr. James O'Meara of Texas A&M International University share insights on implementing the EPDM Starter Kit, the lessons learned, and how it has transformed discussions at TAMIU.
NCSU Improves Student Success Analytics with Learning Analytics Tech
A major challenge in higher education is improving student retention, and North Carolina State University (NCSU) is using technology to address this issue. NCSU’s DELTA department launched an initiative to implement student success analytics to identify at-risk students early in the semester, allowing faculty to intervene and provide the necessary support. With the help of Unicon, NCSU adopted an open-source learning analytics solution, including an open learning data warehouse (Open LRW) and the Apache Hadoop framework, to predict at-risk students and visualize data through user-friendly dashboards. By leveraging predictive analytics tools, NCSU can now proactively support students, improve retention, and scale the solution across their large enrollment. The pilot phase started in 2019, with further refinements planned based on feedback.
D2L Analytics: Data Extraction to the Unizin Data Platform (UDP)
Recent advancements in educational data analytics have highlighted challenges in integrating data from systems like LMS and SIS. One key solution is the Unizin Data Platform (UDP), which unifies data for more comprehensive analysis. D2L Brightspace LMS analytics, with rich insights into student behavior, are a key part of this process. However, extracting, transforming, and integrating this data into UDP requires a robust ETL process. By leveraging tools like Apache Airflow and Google Cloud Composer, we automated the extraction and transformation of D2L data, enabling institutions to ask cross-system questions and improve decision-making for student success.
Student Groups and Machine Learning Clustering - Better Together
Machine learning (ML) enables algorithms to analyze data patterns and make informed decisions, offering a powerful advantage in addressing complex problems. In education, ML is revolutionizing research, operations, online learning, and student outcomes across K-12, higher education, and EdTech. This ebook explores how machine learning can identify patterns in data to optimize student group formation, enhancing classroom collaboration and engagement. Download Student Groups and Machine Learning Clustering: Better Together to learn how ML can supercharge group activities and improve educational outcomes.
Data Governance & Management
Master Data Governance: High-Level Roadmap to Planning & Implementing
Over the past two decades, organizations have accumulated vast amounts of data and are now focused on managing and leveraging it effectively. Data governance ensures data accuracy, security, and compliance, forming the foundation for better decision-making and operational efficiency. It involves managing data through defined policies, procedures, and standards, while key roles like data stewards and owners ensure its quality and accessibility. Effective governance supports regulatory compliance, risk reduction, and improved data security. Challenges such as resistance to change and data silos can be overcome with a strategic approach that includes assessing current practices, building a dedicated team, and implementing the right tools. Regular training and auditing ensure continuous improvement, driving long-term success.
Building Trust in Data: Data Governance Organization Structure
In "The Inevitable," Kevin Kelly's concept of technology as an ongoing process of "becoming" aligns with the fast-paced changes in the data landscape, highlighting the need for modern data governance to be both flexible and proactive. A strong data governance organization structure, which includes a governance council and director-level resources, is crucial for setting priorities and implementing policies across data initiatives. Beyond compliance, data governance must focus on building trust through clear data definitions, lineage, and validation, often facilitated by tools like data catalogs. A successful governance program requires a strategic structure, clear communication, and training to ensure transparency and alignment across the organization. Without robust governance, data initiatives struggle to scale, making it essential for organizations to integrate both defensive and offensive strategies to manage their data effectively.
What is a Data Catalog – And How Do You Adopt One?
Data Analytics Issues: Overcoming Barriers to Effective Data Analytics
After setting up your data warehouse, systems, and governance, you might encounter unexpected data analytics issues, like missing data or faulty reports, due to overlooked complexities in upstream data sources and system integrations. Common problems include mismatched time zones, tight schema constraints, or incorrect assumptions about upstream data. For example, misalignment between server time zones or re-used IDs can cause failures in daily updates or incorrect data. Furthermore, data definitions may vary across departments, leading to discrepancies. To avoid these data analytics issues, ensure flexibility in schema design, involve subject matter experts (SMEs) in the planning process, and plan for data changes over time. Unicon helps clients anticipate these challenges, providing solutions to improve data design and ensure smoother analytics processes.
The Modern Data Stack Components
Over the last decade, the rise of SaaS solutions has transformed the data ecosystem, creating the modern data stack—a collection of specialized services that streamline the entire data pipeline. This stack covers areas like data ingestion, event tracking, transformation, storage, and observability, with tools designed to make data management easier, more automated, and scalable. Key features of the modern stack include managed, cloud-centric services that are operationally reliable. With additional components like analytics, AI, governance, and reverse ETL, organizations can ensure seamless data flow and decision-making. The modern data stack remains flexible, allowing companies to select the best-fitting services as they evolve.
Optimizing Your Data Architecture: What is a Data Warehouse?
In the 1980s, IBM researchers Barry Devlin and Paul Murphy introduced the concept of the data warehouse to help businesses make data-driven decisions. This evolved with solutions from companies like IBM, Oracle, and Microsoft. By 2010, data lakes emerged to manage larger, more varied datasets, moving to the cloud for scalable, cost-effective storage. However, cheap storage led to data swamps, where vast amounts of unmanaged data were stored. To address this, the data mesh was created, decentralizing ownership of data quality. The semantic layer then emerged to make data accessible to non-technical users, allowing them to interact with data using natural language.
Information Security & Privacy
LLM Data Security: Information Security and AI
LLM AI chatbots offer incredible capabilities but raise significant LLM data security and privacy concerns. While trained on vast datasets, they can produce inaccurate or biased information. Data shared with public LLMs may be stored and reviewed, risking exposure of sensitive or proprietary information. To protect privacy, avoid sharing sensitive data, be cautious with prompts, and consider using local systems with more control over data access. Always read privacy policies and terms of service to understand the risks and make informed decisions about what data you provide.
Information Security Management Part 1: Starting Your Security Journey
When Unicon was founded nearly 30 years ago, information security was less of a concern, and hackers were often more mischievous than malicious. Back then, security programs weren't necessary. Today, however, with increasing threats from bad actors, a robust security program is essential. Building a secure business is an ongoing security journey that requires constant adaptation as information security evolves. To start, define the scope of your program, assess risks, select frameworks, and identify gaps in policies. As your security journey progresses, assign ownership and ensure executive support. Regular reviews and updates to policies are critical to maintaining security as your business evolves.
Information Security Management Part 2: Information Security Pillars
In the previous article, we began exploring the security journey of creating an information security program. This article focuses on the three core information security pillars: confidentiality, integrity, and availability (CIA). These principles protect your data by ensuring authorized access, maintaining data accuracy, and ensuring information is accessible when needed. To safeguard these pillars, you need to apply security controls—administrative, technical, and physical—to mitigate threats and vulnerabilities. By understanding and applying the information security pillars and security controls, you can build a strong, resilient information security program.
Algorithmic Discrimination and Avoiding Data Bias
Futuristic technologies, like those depicted in Star Trek, are becoming increasingly real, but with the rise of AI comes the growing concern of data bias—when algorithms unintentionally or intentionally treat certain data unfairly. Many institutions are already utilizing AI in areas like student success analytics, yet a significant portion of them are unaware of how AI is being deployed. As AI adoption expands, it's essential to address data bias by using frameworks and resources such as Microsoft's Responsible AI principles, IBM's AI Fairness 360 toolkit, and the Institute for Ethical AI & Machine Learning's Procurement Framework. In education, tools like predictive models and real-time student support apps are advancing, but ensuring fairness, transparency, and accountability remains crucial to prevent perpetuating bias.
Data Storage & Infrastructure
Data Lakehouse vs Data Lake vs Data Warehouse: What Works For You?
As data management options grow, organizations typically choose between Data Warehouses, Data Lakes and Data Lakehouses based on their needs. Data Warehouses are suited for structured data, offering fast query performance but requiring complex setup. Data Lakes store large volumes of unstructured data at a lower cost but can be slower to query. Data Lakehouses blend both, enabling flexible storage and governance for structured and unstructured data, but query performance can lag behind Data Warehouses. The best choice depends on data type, governance needs, and performance requirements, with a hybrid approach often being the most effective.
Data Lifecycle and Analytics in the AWS Cloud
Data is an organization’s most valuable asset, and as its volume grows, so does the need for efficient analytics, storage, and security. The Data Lifecycle and Analytics in the AWS Cloud eBook provides a comprehensive guide to optimizing data practices, covering key stages like ingestion, staging, cleansing, analysis, visualization, and archiving, while also addressing data security. Download the eBook to learn how to enhance your organization's data lifecycle with AWS.
