Data Quality — Trust Your Numbers; Trust Your Analysis.
High quality analytics requires high quality data.
Data quality is a precondition to useful analytics. Rubbish in, rubbish out. This represents a huge obstacle for most companies. At Tickl, we specialise in solving this widespread and fundamental problem.
Specialists in Solving Data Quality Problems
Time and time again we saw that data quality was the barrier to our clients accessing high quality analytics. So, we became specialists in sorting out messy data.
It’s not glamorous.
It’s not exciting.
And yet it makes all the difference.
The Pillars of Data Quality
The three aspects that determine the quality of your business data:
1. Completeness
Do you have all the data you need? Without the full picture, your data is only telling part of the story.
2. Accuracy
Is the data you have accurate? Without proper validation techniques, it can be hard to tell when your data is wrong.
3. Shape
Is the data captured in its most insightful form? Out of the three pillars, Data Shape is by far the least understood.
Why is Data Quality Such A Common Problem?
Because maintaining data quality is mind-numbingly dull.
Manual tasks that impact data quality – like inputting data, moving data across systems, and checking data has been entered correctly – are repetitive, boring, and time-intensive.
And so, no matter how many times someone cries: “The system would work if everyone used it correctly”, it’s simply not going to happen. People will always make mistakes.
What's Our Solution?
Save time and increase accuracy with automation.
In a word, automation. Wherever possible, we automate processes to reduce the amount of manual data input, transfer, or review required to an absolute minimum. With our processes we:
1) make it harder for teams to get it wrong,
2) make it obvious when it’s gone wrong & who’s responsible
3) give those responsible an incentive to fix their mistakes
1
Make it Harder to Get it Wrong
Pre-population:
We pre-populate fields with automated data extraction and use data validation to minimise the amount of manual input required.
Intuitive UI:
Where manual data input is still required, we design an intuitive User Interface to simplify data input for users, making it as easy as possible to get it right.
2
Make it obvious when it’s gone wrong and who’s responsible
Cross-system Validation:
We use automated scripts to extract data from key systems on a scheduled basis, then cross-reference and validate that data against other sources, raising an immediate alert if there are discrepancies.
Hygiene Reports:
We build automated hygiene reports that identify errors, classify them by type, and flag them to the user responsible. This creates accountability, while also making it easy for users to swiftly fix errors.
3
Give those responsible an incentive to fix it
Gamification:
We turn data entry into a game, creating individual scorecards and a “league” table based on compliance with data accuracy targets and number of outstanding errors. This simple step drastically improves data quality across companies.
Our approach replaces high-frequency standalone exercises with tools that can be reused again and again. This allows data to flow seamlessly, significantly reduces errors, and frees up resources so your people can focus on meaningful and impactful work.
01
Case Study: TPGC
Supplier invoice data extraction with 99% accuracy with AI
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Case Study: TBD
Example of process completed with AI
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Case Study: SCALE
Enriching client data for marketing purposes with AI
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