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Smarter Data Smarter World (8/11/2017) Transcript and Slides| FME – The Driving Force behind Efficiency & Innovation

SLIDE 1 Transcript

Hello! My name is Kamrul Kashem, I’m a Senior GIS Analyst at Nottingham City Council.

I’ve worked with FME for over 5 years now. We tend to have a love-hate relationship with software. But I don’t hate

FME, far from it. As you can see, I’ve used an FME model as an inspiration for my presentation design!

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SLIDE 2 Transcript

In the beginning, there was a creator! Let’s look at what I am aiming to get across today.

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SLIDE 3 Transcript

One of the aims of this presentation is to prove to you that you’re potentially wasting a lot of time working! Just get

the little minions behind FME to do that work for you while you go get yourself a well-deserved cuppa tea. Yes, the

transformers are effectively like minions, they do stuff for you. I can recall in my early days of FME when my boss

told me to have a go at using the software to help me with my work. Work that would take me weeks, was now

taking me 5 minutes to achieve in FME. I was plotting things instantly with a coordinate list rather than manually

going into ArcMap and plotting them myself one by one. Obviously, I didn’t tell my manager that right away… like I

said, go get yourself a well-deserved cuppa tea.

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SLIDE 4 Transcript

I want you to understand the importance of talking about FME within your organisation and really letting people

know how it can change the way they work radically. Not only understanding the capabilities of FME yourself but

also sharing that knowledge with the wider organisation really helps bring use out of FME. Most of the projects I will

be talking about shortly were not because they happened to land on my desk, but rather because people knew of

this amazing tool called FME that I had and knew it could make their life a whole lot easier - Just don’t put them to

sleep.

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SLIDE 5 Transcript

I hope to get across to you today that FME is a tool that will help you find efficiencies within your day-to-day work

but also give you the ability to innovate in areas you’ve never explored before. FME makes complex data analysis

and spatial analysis look so easy and by the end of this presentation I hope to spark a few lightbulbs on how you

could use this in your own organisations to find revenue.

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SLIDE 6 Transcript

FME isn’t just for efficiency or finding extra revenue. There’s a whole host of advantages I could talk about but I’ve

focused only on a couple, visual and comprehensive. What do I mean by visual? It’s very easy for you to share your

model with other FME professionals, and they’ll work it out faster than they would have if you had sent them a huge

Python script. Whether you send it to an FME beginner or an expert, they’ll both know exactly what the model is

doing. This has to be the core for every business, because if I packed my bags and left the Council tomorrow, at least

my managers know that someone else can find the models I’ve made and make sense out of them. It’s almost self-

documenting. I can remember taking over from the last guy who built everything in Python, it was a nightmare to try

and learn Python by the time he leaves and then try to decode it all and recreate the processes in FME, but now that

it’s done, someone else will pick it up much more easier without the jargon.

What do I mean by FME being comprehensive? Well if you don’t have FME you will know that you need multiple

solutions or packages to fix issues with your data. If you were to do everything in Python you’ll need to go download

numerous modules and then learn how to use them just to fix some simple issues. FME is comprehensive in the

sense that it provides you with hundreds of transformers or as I call them, minions. You can do everything in one

workspace; fix attributes, map them, convert, calculate, aggregate, you name it!

And, if all else fails, you can get your geek head on and dive into the PythonCaller or have the workspace call a script

before or after it runs! Comprehensive.

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But there’s one problem, where’s Ken? We’re in trouble if anything ever happens to FME. We need you to promise

that you’ll be around forever because if anything does happen to FME …. We are utterly screwed. We’ve become so

dependent on it to do everything for us that we’ve forgot how it felt to do things manually. If I have to go into

ArcMap ever again, manually input points from a spreadsheet. I’ll quit. This just proves how invaluable FME has

become though.

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SLIDE 7 Transcript

Right, on to the Efficiency Finder! Let’s look at some examples now. Open Data – I did a presentation about this in

2015 for FME’s World Tour hosted by 1Spatial. But it’s something definitely still worth mentioning. We at

Nottingham City Council couldn’t possibly deliver open data to our citizens without FME.

Not only do we use FME in keeping our corporate spatial database up-to-date but we also have models to then

export this data into various formats and share it on our Open Data website. Here’s some of the formats we deliver

the data in.

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SLIDE 8 Transcript

1Spatial kindly did a Case Study on this last year and I highly recommend you all to go read it. There’s a lot more

detail in it than I can possibly talk about here. Just google Nottingham City Council 1Spatial and you should find it.

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SLIDE 9 Transcript

One of the examples it talks about is how we reduced the time it takes to redact personal information from our

Council Spend data by applying rules-based transformations against the data. Instead of a number of colleagues

manually looking through 15,000 or more records, the model now outputs around 50 to 100 exceptions to check

before uploading it to our Open Data site. This is not only ‘Good Transparency’ but also helps us answer FOI requests

on this data, which is highly sought after.

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SLIDE 10 Transcript

In simple terms, we wanted to find out how many residential households will have provision for open spaces. It

wasn’t as simple as just counting LLPG (Residential) points in a given area though! Each site has a local buffer of 600

meters, a neighbourhood buffer of 800 meters, a city buffer of 1000 meters and a destination buffer of 5000 meters.

The open spaces are also buffered in this manner and then essentially we are counting the residential properties

within the overlaps in percentage to the total number of residential in the buffer.

The Open Spaces team were doing each site manually on ArcGIS, this meant creating buffers manually, doing

selections manually and then transferring the counts onto a word document report. Each site took them

approximately 1 full working day to complete…

Until one day, they were requested to do the calculations for 50 to 100 sites. They screamed help!

That’s where FME came to the rescue and we reduced their 1 full working day per site to 3 minutes per site. Not only

does it give them the calculations but also spits out a load of feature classes into an ESRI Geodatabase and then runs

a Python Script at the end to run a load of data driven pages. This exports 13 pretty maps of each provision in order

for the Open Spaces team to use in their reports. This is an example of various bits of software and tools working

together in harmony with FME. However, the great thing about FME is that you can always do better; going back to

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this process in the future, I can imagine using FME Server to get the Open Spaces Team to simply pop a Shapefile

onto the browser and run the model themselves.

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SLIDE 11 Transcript

The Permit Scheme… As a Local Authority, we were tasked to identify streets that will require a permit before utility

providers can do works on them. We used FME quite extensively in this project to first identify busy streets based on

the Annual Average Daily Traffic Flow data, modelling this against our road network.

But the model that made massive efficiency and time savings for us was when we turned this information into a flat

file DTF file to be bulk imported back into our LSG. Rather than having to get our Address Management team to

manually enter these in. Bearing in mind it wasn’t just a click of a button, they needed to enter the times when the

street will require a permit, descriptions and even sometimes part street information.

We calculated that 1 street could potentially take us around 15-20 minutes to complete considering we were

wanting to update various other bits of information against the street at the same time. Instead, FME could handle

thousands of streets and create us a DTF file that we were confident with.

So we didn’t just update our LSG with streets that have permits, no, we went that extra mile and updated our

adoption status, reinstatement type, special designations which included traffic sensitive streets, protected streets

and special engineering difficulty streets. That took our LSG from being unrated, to Gold standard and we’ve

retained the top spot ever since!

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SLIDE 12 Transcript

Now let’s look at some innovative uses for FME! By innovation, I don’t mean that we’ve invented something new,

but rather we’re doing something we couldn’t possibly do before due to limitations on software and knowledge.

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SLIDE 13 Transcript

You’ll probably hear a lot about business intelligence, and in effect all that means is linking various data sources

together to get a broader picture or in order to identify trends you couldn’t have spotted otherwise.

Like all Council’s aspirations, our heads were in the cloud when we thought we could potentially join all the council

core systems together based on the Unique Property Reference Number (UPRN), in order to create a MASTER view

of a property. Sounds like something right out of CSI.

After realising that may be a little too ambitious, we decided to use the dataset we were formalising to target more

specific issues such as detecting fraud or where our data has let us down and lost us money.

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Therefore, we set about joining our Local Land and Property Gazetteer (LLPG) to our Business Rates database, the

Licenced Premises database, and the Workplace Parking Levy database in order to look for commercial properties

that may exist in these various databases but not actually paying any business rates.

FME chugged away through hundreds of thousands of records, comparing, spitting out reports and spreadsheets of

findings until we narrowed it down to a potential of a few hundred properties to check.

We then built a web map out of the findings for our fraud team so they can spatially locate these records and

investigate them. Upon investigation we started to uncover thousands of pounds, often back payments or simply

people getting away with not paying anything by declaring themselves as a residential unit but they happened to be

signed up as licenced premises, or paying for Workplace parking etc. We estimated around £60,000 revenue

collection on this project alone, which would have taken us only a week to deliver. However, the real value is much

higher now, maybe even double that.

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SLIDE 14 Transcript

Let’s look at Strategic Asset Management. The Council’s Property team are required to keep up-to-date their list of

assets. This also includes holding occupancy information, i.e. the number of colleagues working in a given asset. We

used FME as the data preparer in eliminating bad data and transforming it into good data. This means cleaning nulls,

joining to the corporate address gazetteer, formatting issues, date issues etc.

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Then we took that good data and had FME write it into an SQL database from which we built a web front end that

allowed the Property team to view detailed information about the Council asset as well as enter in occupancy

information.

We have regular FME models in order to keep the organisation structure data up-to-date by fetching it from its

source and importing it back into the database. The SAM tool as we call it is in effect a hub of information about our

assets and FME acts as the medium between all these datasets joining them together into a master table. Something

we’ve never done before and relied on spreadsheets of information here and there.

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SLIDE 15 Transcript

Finally, as you might know Local Authorities deal with vast amounts of information held on paper still. The majority

of this being social care data. We have over 30,000 boxes in existence and an average of 129 requests for boxes a

week. I was tasked with creating a model that could firstly read multiple databases, don’t ask why we have multiple

databases with the same information inside them, I don’t know myself, but it happened somewhere along the line.

We joined them together, calculating a number of fields, such as lifetime retention values and then exporting it out

into an Access database, which had pre-designed queries that can be run daily. It didn’t have to be Access though,

we envisage creating an online tool in the future with fancy dashboards and reporting. The beauty of FME is that you

can export your data however you want, and when you change your mind, simply change the writer.

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This helped our Records Management team achieve brilliant results! In 2016 the Council had created 999 boxes and

deleted only 503 whereas since we started the project in 2017 we have reduced the number of boxes requested to

516 and deleted a huge amount of unwanted boxes, 3255 deleted, that was costing the Council thousands of

pounds! If they were never identified we’d have been paying for them until someone carried this sort of data joining

analysis out!

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SLIDE 16 Transcript

The End. Obviously, I can’t forget about the log file, don’t we love it when a model says ‘Translation was

SUCCESSFUL’.

Thank you for listening!