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Director of IT

Manage data governance and analytics support

Automates◐ 1–3 years

What You Do Today

Ensure data quality, support BI tools, and manage the data infrastructure that analytics teams depend on.

AI That Applies

Automated data quality monitoring and catalog management that keeps data trustworthy and discoverable.

Technologies

How It Works

For manage data governance and analytics support, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Data quality issues surface automatically instead of when reports look wrong.

What Stays

Building a data-driven culture and getting people to care about data quality.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage data governance and analytics support, understand your current state.

Map your current process: Document how manage data governance and analytics support works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Building a data-driven culture and getting people to care about data quality. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Collibra tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage data governance and analytics support takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CIO or VP IT

Which of our current reports are manually assembled, and how much time does that take each cycle?

They're prioritizing which IT functions to automate

your cybersecurity lead

What questions do stakeholders actually ask that our current reporting doesn't answer?

AI tools create new attack surfaces and new defense capabilities

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.