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4 posts tagged with "Data Quality"

Data Quality for AI

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How to build a business case for reducing repeated data and reporting work

· 13 min read
Dan Peacock
Dan Peacock
CEO
Dan Peacock on LinkedIn

Build the case around one recurring workflow, measure the work honestly and ask leadership for a small, specific decision.

The short answer

Build the case around one recurring reporting workflow. Measure its current effort and delays, find the cause of the repeated handling, compare realistic options and include the full cost of change. Then propose a limited pilot with agreed success measures. Treat staff time released as capacity unless you can show a genuine budget saving.

Every month, someone on your finance team reformats a file, chases a correction and reconciles the result. The report goes out on time, so the work stays invisible. A business case makes it visible.

If you lead forecasting, planning and analysis, you may already see the problem. Finance managers and controllers know where the work happens. A useful proposal brings that knowledge together in a form that finance leadership and IT can evaluate.

Why do finance, sales and operations report different numbers?

· 14 min read
Dan Peacock
Dan Peacock
CEO
Dan Peacock on LinkedIn

They count different events on different dates. Some differences are legitimate. The problem starts when nobody can explain how the figures relate.

The short answer

Finance, sales and operations report different numbers because they count different events, use different dates and apply different definitions and adjustments. Some differences are legitimate. A reporting problem starts when teams cannot explain how their figures relate, or when one agreed KPI is calculated in more than one way.

At month-end, finance collects actuals from subsidiaries that run different ERPs. Forecasts add another set of spreadsheets and CRM inputs. Before anything reaches leadership, finance has to bring the figures together, explain the differences and say why actuals moved away from the forecast.

If you lead forecasting, planning and analysis, or work as a finance manager or financial controller, you need data that is consistent enough to use and a way to spot the submissions that need correcting. Data and systems colleagues can help trace the technical causes.

How Do You Know If Your Data Is AI-Ready?

· 9 min read
Dan Peacock
Dan Peacock
CEO
Dan Peacock on LinkedIn

Most organisations answer "is our data AI-ready?" with a feeling rather than a fact. The platform is modern, the warehouse is populated, a governance programme exists — so it probably is.

It usually is not, and the gap only surfaces once a model is in production and its answers start contradicting things the business knows to be true.

These seven questions give you a straight answer. They are deliberately phrased so you can respond yes or no. If a question needs a qualified answer, treat that as a no.

How do you plan to govern AI at your organisation?

· 3 min read
Dan Peacock
Dan Peacock
CEO
Dan Peacock on LinkedIn

A practical place to begin this conversation is by asking: what does AI actually require to be successful Despite the noise around models, innovation, and tools, the reality is simple:

AI is only as good as the data that feeds it.

For AI to deliver trustworthy, scalable value, organisations need three foundational capabilities:

  1. Good-quality data.
  2. Data with business context.
  3. Data stored in a consistent structure.

Let’s tackle these one by one.