Cleveland Police turns to Experian to create a single view of residents

Profile picture for user gflood By Gary Flood May 7, 2019
Summary:
Cleveland Police Force says data services provider Experian helped it eradicate its very high number of duplicate and incomplete records.

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If there are just 600,000 people you have to worry about in your area but you have 1.8m files about them in your database — the probability seems pretty high that something’s gone a bit wrong with your data capture.

That sounds like a boring IT problem, you might think… but not if that database is your local Police Force’s. The problem being, to do their job of protecting you, its back office team would need to continually keep searching through duplicate records of the same person and their interactions — there is no single accessible view on anyone’s criminal background and the threat they might pose to the community.

That was indeed the situation at Cleveland Police - one of the UK’s smallest local Police Forces - which was having to carry a very manual process, linking and managing all these duplicates, at a time of 25% budget cuts and 500 fewer officers than it had on roster in 2010.

But the good news is that by getting some help in addressing its data issues, it is now in a position where it is saving up to £250,000 a year and has won back a pretty impressive 10 years of man hours that can be applied to more value-add activities.

The Force’s Head of Information Management, who also doubles as its Data Protection Officer, Maria Hopper, explained the background for us — where she sees this as almost a moral imperative to get things right:

“We are a fairly small Force in the Northeast of England in geographical terms, sandwiched between North Yorkshire and Durham Police. But we do police quite large, populated towns like Middlesbrough, Stockton and Hartlepool, where the crime area is quite high, and indeed growing.

“I am an absolute believer that while our people are the heart and soul of the organisation, data is our life blood — and we've got to have them both in a very healthy situation if we want to be able to do the work we need to. What we're here for as a Police Force is protecting the public and protecting vulnerable individuals, and that means we must have a truly holistic view of the individuals in our community to be able to do both effectively.”

But was stated, that wasn’t quite happening — that holistic view wasn’t available:

“We have 600,000 residents in our area, but we have a crime management system that held three times that many personal records. That was a clear indication that we have lots and lots of duplicates in the system, and although concerns had been raised for some time, it took us getting in a new Chief Constable who actually understood what that was a problem and had a view about data similar to mine to start fixing it.”

Hopper says that happened by that internal leader re-classifying the duplication issue as an actual corporate risk — and a commitment from the top to invest into clearing up the crime management system to attain what a business would call a single customer view, but which in Cleveland Police terms is dubbed ‘the Golden Nominal’.

The search for the Golden Nominal

Hopper was then empowered to set up a special task force of seven data experts to begin cleaning up Cleveland’s data stables.

“We did have a data matching tool already, so we got that out of the cupboard, dusting it off and putting it into motion. However that very good data matching tool, only gave us back about 14,000 matches out of 1.8 million records.

“My view was that was not the way anyway, though — the solution is getting it right at the front end. What we needed was to better identify who was creating the new records at point of entry and focus on that area of the business to try to get it right there. It doesn't matter how good your data matching tool is if the records are never to a good enough standard to meet them.”

At this point Hopper started a dialogue with credit score giant and data governance expert Experian. That company had cleaner and richer data than she had, but also tools, she says, to address what she saw as this front end issue but also a way to could continually view her data — how it was structured and what was happening on a daily basis for it to be able to pull out any anomalies or any significant risks within its information store.

The result, she says — four years on from that initial decision to address the issue — is what she called a unified front, middle and back end solution to her problem. However, this is very much a joint effort, she stresses:

“Our partner would cleanse our data, put that data through their technology and again go through to match removals. We would identify the really good match, where there would be no requirement for human intervention, but then we also wanted that lower level of match, where we think it is a good quality match but not quite enough for an all tool match. We needed all this produced to us on a daily basis so we could review and make our own decisions, so we had a new, cleaned up database 24 hours behind where we actually were for us to look at.”

What are the specific benefits of all this hard work, though? For Hopper,

“We estimate a cost saving to us of approximately £250,000 each year in man hours we don’t now need to spend processing, reviewing and amending data — which also give us much quicker incident response times as call handlers have access to existing data. I also know we have improved public safety with this holistic view as in the past, vital information, might have been missed, and we have also reduced security clearance times from what was as long as 1.5 hours a day to just 15 minutes.”

Plus, now she has her famous Golden Nominal data standard, the Force is creating 100,000 fewer personal records a year and has, she claims, ‘absolutely zero exact duplicate records’ now on the system — and if any do creep in, they are got rid of 24 hours later.

Next steps for data at Cleveland Police, she concludes, include a look at how driving even greater efficiency by better data matching, working with datasets and party agencies:

“Everything will work a lot better if we improve our data as early as we possibly can, so this is the building block for developing better digitisation and making that digitisation work across the board.”