THE QUESTIONS

When New Jersey Institute of Technology (NJIT) needed to modernize its data ecosystem, the university faced a familiar but increasingly urgent problem: Critical institutional data lived in systems that didn't speak to one another.

The result was a campus-wide struggle to answer even basic questions with confidence. Different teams used different extracts and pulled at different times, often producing different answers. NJIT needed a way to unify its data without forcing every department to change the tools they relied on.

But centralization wasn’t the only issue. Building a data warehouse internally required extracting data from systems that were never designed to be open or easy to work with. Even with Snowflake and modern ETL tools in place, a time-consuming and fragile part of the process remained the first step: getting accurate, complete, and timely data out of each source system.

NJIT’s team found themselves grappling with a set of fundamental questions:

• How do we reliably extract data from every system we depend on, without spending months building and maintaining custom extractions?

• How do we ensure every stakeholder is working from the same, consistent data, rather than relying on outdated reports or conflicting snapshots?

• How do we capture historical changes over time so we can support point-in-time reporting and eliminate “version-of-the- truth” confusion across campus?

Ultimately, NJIT needed a stable, scalable, vendor-agnostic foundation that would enable them to spend less time fighting for access and more time building a modern analytics environment that could support the entire institution.

THE CHALLENGE

Like many institutions, NJIT struggled with fragmented data systems that created operational blind spots across the university. Banner held student and administrative records. Canvas stored classroom activity and engagement. Slate captured recruitment insights. HR, finance, and other operational systems each maintained their own versions of the truth.

Disconnected data meant reporting was slow, inconsistent, and prone to errors. Decisions were often made using incomplete information, and retroactive changes to data could compromise historical analyses.

The technical hurdles were equally daunting. Vendor access restrictions led to long delays just to pull the information NJIT already owned. Even once data was extracted, NJIT faced another challenge: capturing historical changes accurately. Without daily snapshots, the university could not reliably answer questions about student trends or point-in-time metrics. Traditional warehouses were either too slow to adapt to new requests or too expensive to retain years of data, leaving analysts and leadership without the comprehensive insights they needed.

NJIT needed a solution that would simplify data ingestion, preserve historical accuracy, and provide a single, reliable source of truth, so the university could spend less time wrangling systems and more time turning data into actionable insights.

THE SOLUTION

NJIT closed its data gaps by adopting InvokeUnify™, the extraction and historical-capture layer of the InvokeClarity™ data lakehouse platform. Rather than rebuilding fragile, custom pipelines for each system, NJIT uses InvokeUnify to handle the critical extraction component: reliably pulling source data out of their operational systems and delivering it into their Snowflake environment.

InvokeUnify brought three practical capabilities that directly addressed NJIT’s pain points:

1. InvokeUnify shipped with pre-built connectors for common higher-ed systems, so a major integration hurdle was already solved. These “pipelines in a box” removed much of the initial engineering burden, since the NJIT team didn’t have to design, code, or maintain separate pipelines for each application.

2. InvokeUnify preserves daily snapshots of every captured table by adding an “invoke data date” (a point-in-time marker) to the ingested records. That daily, historically-stable archive eliminates the guesswork around retroactive changes and enables true point-in-time reporting. NJIT can now reconstruct exactly what source data looked like on any past date, solving the “versions of the truth” problem and making trend and AI work much more reliable.

3. InvokeUnify is built to be cloud-native and vendor agnostic. It deposits raw source tables into Snowflake where NJIT’s team can run their existing ELT/ETL and BI processes. Once Invoke gained the appropriate access (in NJIT’s case, via the reporting databases), making the source data available in Snowflake required only minimal effort on NJIT’s side.

InvokeUnify is also flexible enough to sit beneath whatever data warehouse strategy the university prefers, powering a full InvokeClarity deployment or acting as a standalone ingestion layer that augments an existing Snowflake implementation.

THE ANSWERS

THE RESULTS

Reliable Data Extraction

Instead of spending months building custom pipelines, NJIT now pulls data from every key system automatically, using InvokeUnify’s pre-built connectors to ensure consistent and dependable ingestion.

Consistent Unified Data

All stakeholders access the same source data in Snowflake, eliminating conflicting reports and giving leadership and analysts a single, trusted version of the truth. 

Point-in-Time Reporting and Historical Accuracy

Daily snapshots of every source table allow NJIT to perform accurate historical reporting, supporting trend analysis, AI initiatives, and decisions based on how data actually looked at any given date.

How can InvokeUnify Help Your School?

By handling the heavy lifting of extraction and daily snapshots, InvokeUnify frees your team to focus on analysis, insights, and strategic initiatives, turning siloed systems into a reliable foundation for smarter decision-making.

Connect with us.

For more information about Invoke Learning and how you can uncover the why across the full student journey, contact us at sales@invokelearning.com or schedule a demo.