Developing a single view of businesses

Businesses often complain that public bodies provide a fragmented set of services that makes it harder and more expensive to comply with legislation. Depending on its trade or industry and its size a business may have very little need to transact with the council, or may do so frequently for various licences, permits as well as offering apprenticeship opportunities to local people.

Hackney has curated a ‘Citizen Index’ for over ten years which takes data from our major business applications and matches it to create a unique record of each citizen. This enables us to provide more accurate business intelligence and verify some customer requirements.

We decided to take the same approach to join up services for businesses. If we took the data about the tax businesses pay the council (non domestic rates) and the licences they’ve requested, we could match those to create a single view of transactions.

Steve Farr, who led the work, explains how we’ve done this in a series of posts:

An introduction to Business Index

Understanding user needs

Introduction to User Research

What an interesting and informative week we’ve  had in ICT!  It all started with ICT adopting the Digital Services standards in January and then developing the Hackney manifesto, one of which is….

Last week saw the launch of ‘People first’ concept starting with User research week in ICT.  The purpose of the week was to  help colleagues in ICT learn about and understand our users and how we can create services to meet their needs.  A number of activities were designed over the week to help colleagues understand what this is, how to do it and why it matters. This is important because we want everyone to spend an hour a month doing user research. This week was the first chance to gain some skills and confidence to do this.

We lined up a range of experts to share their experiences of user research (that’s another of our manifesto principles), and to set minds thinking  about how this can benefit each and everyone in their role.

We started the week off with Ben Unsworth from FutureGov explaining how user research can help us design digital services so good that people prefer to use them.

Daniel, Andrea and Elspeth from Healthwatch Hackney helped us understand the challenges that people with accessibility needs face when they use our digital services. By attempting to use speech and text to navigate we learnt the importance of making our digital services accessible for all and discussed how to design digital services for accessibility.

Beatrice Karol Burks, from leading charity the Good Things Foundation, took us through the design of services to meet the needs of people who have never been online and  helped us understand how to engage people who may be reluctant to use computers and build their digital skills.

Helen Gracie, from the Home Office, explained how we can engage with users who are harder to reach, so we’re not just talking to early adopters.

Research experts, ClearLeft, facilitated a training session for 11 staff to learn new techniques using Guerrilla Research .  Guerilla Research  is a ‘rapid, low-cost method of quickly capturing user feedback that involves asking questions about specific areas of an application’. Clearleft’s  final reminder of Guerrilla Research  sums it all up:

As part of the continuous development, staff will be expected to commit to 1 hour practical experience every month working directly with users, to help us understand how to carry out user research and build our confidence.  By working closely with users, watching them  perform tasks and finding out more  information on how they work, we will understanding their requirements and build better digital services.  Other activities planned for this period include volunteering  to visit CLR James Library in Dalston & Hackney Central Library to help people get online for the first time.

If we’re to put people first, we need to focus on  their needs.  To achieve this we are focusing on the process and not the eventual outcome. It is a long journey but we’ve made a good start.

How Master Data supports better business intelligence

We are all visual beings now. Every day we absorb graphs, maps and informatics through many channels. We are comfortably stepping into them but often overwhelmed by the sheer amount on offer. So what we all need is well-designed, colourful, intuitive information, which allows users to toggle between summaries and details: Hackney dashboards.

The evolution of the Qlik BI project has been rapid, starting from “what is needed” accelerating quickly into “what is possible”, with new opportunities constantly emerging.

First, we created dashboards by engaging with colleagues who needed to replace their legacy reporting tools, in areas like Parking and Planning. We worked with the Housing Repairs team to show data they could have not easily see nor interact with using traditional reports. Our customers either knew or suspected that the answers to their challenging questions lurked in the data they collect every day but they were swamped with little time to analyse and draw meaningful conclusions.

Initial success came quick. Our customers received information which were not only facts or dry statistics but colourful, interactive and up to date information available to the most granular level. They further asked us to focus on the relationships between the figures, within which they are connected visually both in  depth and breadth.

As with all data projects we continued experimenting with exploration: both what is possible and how to make it approachable and accessible. We have style guides, we follow the local gov digital service standards, borrowed from other organisations. But in Hackney we have something unique. We have a pervading common denominator: master data indices which link disparate service data by a single reference key: unique address and customer reference numbers. We have been managing them for years –  insisting they are included in all new systems- and they allow data to flow between systems. Not only are they are very useful backgrounds link when serving our customers, they are very useful in all BI work to leverage much more meaningful insights.

So what could be gleaned from using our master data? Our data mining looked into the money element first: what is the overall debt? What properties cost us the most to serve? How can we promote people to pay by direct debit?  Which tenants could be illegally subletting their properties?

But by using these links we can do much more than just protect the ever dwindling public purse, we can improve the lives of our residents: We can get the full picture of who lives in a block of flats, rather than rely on one incomplete database;  understand which vulnerable tenants are being chased for rent debt when the long-term cost of our actions is far greater than money recouped; or understand the attributes and demographics of people affected by a new policy change such as the Council Tax Reduction Scheme. Most of all however, we aim to deliver dashboards which would inform us how to build a better relationship with our customers.

Qlik has provided us a canvas on which to illustrate these explorations with rich palette of further insights we are continuously working on.

Different lenses for understanding users

Ahead of user research week. I thought it might be useful to introduce some other service design principles, that it might be useful to think about when designing a new service, namely Life-Cycles.

Lifecycles can aid your design by giving an “outside-in” perspective of what customers experience across an entire sector.

Human Lifecycle– this describes how people behave in different key stages in their life. Thinking about this, gives you an overview of what really matters in people’s lives beyond what your organisation offers, and helps you understand how your organisation can support customers in major transitions in their lives e.g. from school attendance (Hackney Learning trust) to school leaver (ways into work), to employment, to renting home a home (choice based letting) to parenthood (children’s centres) and onto retirement (day centres for the elderly). What customers may need from your service will vary according to what phase they are in their lives and what transitions they are going through in their life.

The Consumer lifecycle – describes how people behave in a market when they make choices about their needs or wants. So if someone is going on a business trip they may use a number of services to meet their needs. How a service comes together and interacts (with those offered by your partners and competitors) affects the overall consumer experience. Understanding how people make choices enables you to design a service which supports them to make the right choices.

The Customer lifecycle – describes how customers become aware of a service, choose a service, pay for a contract, use that service, upgrade a contract, have incidents with that service and then either renew their contract or leave. Anyone whom has purchased a phone under contract knows that how you are treated during that lifecycle will reflect whether you renew your contract or go elsewhere. So thinking about that whole lifecycle and how a service deals with customers during the lifecycle can help increase customer loyalty, retain customers and optimise the contract holding experience. Commercial waste contracts is an area the council deals with where consideration of the customer lifecycle in designing a new service may help.

The user lifecycle – is a tool to help reduce costs, drive efficiencies and trigger new behaviours when people use the product or service. This tool helps you visualise the journey a user goes through maybe using multiple channels when they interact with your service. It enables you to visualise the service delivery across multiple channels to enable you to simplify and improve customer interactions.

In conclusion when designing a new service you need to do so through the lenses of the human, user,consumer and customer lifecycles.

Rent Arrears and the Sweet Science, v.01

As Digital Transformation Manager for Housing Services at Hackney Council, I get to run some really fun and interesting projects. This week saw the start of a great one however, as we kicked off a data science project which we hope will enable us to identify tenants most at risk of falling into rent arrears (it also marks the start of what will no doubt become a remarkably infrequent blog, but that’s another story).

Arrears are bad news for everybody; they cause stress for tenants who may lose their home and have to move out of the borough away from their support networks, and they reduce the amount of money we have to maintain homes and estates. When people fall into arrears we set up repayment plans, but for people on limited incomes the size of the repayment we can realistically expect them to be able to pay consistently on top of their rent is quite low, making debt stubbornly difficult to clear.

But what if there’s a better way, and we can predict those most at risk of falling into arrears so that interventions can be targeted to prevent the problem before it occurs? Within the council we have a Financial Inclusion team that helps residents with financial planning and can point them towards training to help them get better paying jobs. If we could better target that team’s resources to those most at risk then this wouldn’t just help reduce rent arrears, but give our tenants more control of their finances and help tackle unsecured debts, or payday loans.

To try and answer this question we’re working with a company called Pivigo, that run a programme to train candidates with PhD’s in quantitative disciplines to be data scientists. As part of the programme they need organisations to present them with real world problems to solve, so this week I met with our team to discuss the challenge. The team comprises:

  • Francesca Renzi, holder of a PhD in Nuclear Physics and winner of three research grants from the Umbria Region in Italy.
  • Philipp Ludersdorfer, holder of a PhD in Cognitive Neuroscience who has previously developed statistical models to predict outcomes and recovery of stroke patients.
  • Tom Northey, holder of a PhD in Bioinformatics and award winner at the TfL Data Science hackathon.

    Over the next month they will be analysing our data using techniques such as clustering, decision trees, and time-series modelling and building a model to try and quantify the risk of a resident falling into arrears. This model should then enable us to play with certain parameters such as anticipated inflation or wage growth rates to see how this may impact on our residents in given scenarios. Microsoft have given us free access to their Azure Machine Learning platform for the duration of the project, but the algorithm we develop will be platform-agnostic and available on Hackney Council’s GitHub repository. We hope that other local authorities and housing associations will test their data against it also and that we can work together to build upon and refine it.

    In a project dealing with such personal data as this privacy is of course incredibly important. Personally identifiable data is not needed to develop the model and so not included in any of the data sets used for analysis or testing. Similarly, should the project be successful and we create something that can be introduced to our working environment it wouldn’t be something that staff would be able to dig around in, but a tool that selectively highlights only those that may be considered vulnerable to the teams that can help them.

    The project is scheduled to run until 7th September and I’ll post again on what we’ve learned, but the end point is really just the MVP and I’m hoping that we can work with other local authorities and housing associations to develop this further.