Author: Data

Understanding Machine Learning (ML) in 5 Minutes

Posted on by in Collaboration and Data

By Ejaz Hussain – Lead Data Scientist

What is machine learning?

To make it simple and short, “machine learning is one of the foundational branches of Artificial Intelligence (AI) which focuses on the use of data and mathematical based calculations (algorithms) to imitate the way we (as humans) learn, and then gradually improve its accuracy to predict outcomes (results)”.

What is predictive modelling in machine learning?

In a nutshell, predictive modelling is “a statistical technique using machine learning and data processing to predict and forecast likely future outcomes (results) with the support of historical and existing data”. Predictive modelling works by analysing historical and current data where it projects what it learns using a model ‘forecasting likely’ outcomes.

Machine learning examples and initiatives

Machine learning practices has helped many businesses in both private and public sector and potential for opportunities are unlimited which really support local businesses to achieve their targets. To make it more relevant for the London Borough of Hounslow, here are few examples which have acted as a driver for a greater change.

  • Data Science helped identify potential savings of over £581m for the NHS – click here to view detailed article.
  • Leeds Institute for Data Analytics using machine learning in a completely new way to improve climate models – click here to view detailed article.
  • Data Science to tackle ‘global targets for sustainable development’ which are set by the United Nations – click here to view detailed article.

How Data Science and Predictive Analytics can contribute to sustainable development

In the past, data science practices were limited to the top fortune 500 global businesses, however; there is a larger shift where data science is now adopted by nearly all private and public sector organisations which is mainly due to big data ease of access and reduced costs of using open-source technologies.

The illustration below is one of the examples, set by the United Nations where the focus is on data science and analytics. In this example, data is an integral part where we (London Borough of Hounslow) can step in and support the common goal which is to make our world safer and sustainable.

Source: United Nations – Data Science Contributions towards SDG’s

Understanding Data Science and Data Quality

Posted on by in Collaboration and Data

As part of the digital strategy, the London Borough of Hounslow (LBH) now have a Data Science and Data Quality Team.

We interviewed the team to get a sense of what the Data Science and Data Quality Team will be working on.

The team

Ejaz Hussain, Lead Data Scientist

Anna Trichkine, Data Quality Lead

Ahmed Babalola Lasisi, Data Engineer

Neil Gordon, Data and Development Manager

What does Data Quality and Data Science mean to the team?

Data Quality:

Data Quality is a focus area for many teams. Data Quality can be developed in many ways including focusing on data engineering and creating good data pipelines, running regular data quality reports, and visualising data to showcase the quality to users.

Anna

Data Science:

The role of data science is unique and stands between the business operational world and the technical world. Data Science offers opportunities of deep data analysis where artificial intelligence technologies such as machine learning play a vital role to design and build predictive models. Such predictive models run on algorithm-based principles and help us to achieve business specific outcomes, for example data enabled decision making.

Ejaz

Data Science and Data Quality:

This is a genuinely exciting time at Hounslow, with the creation of a dedicated Data Science and Data Quality Team helping to leverage data as one of our most powerful assets driving greater sharing, analysis and insight across all areas of the council. Predictive modelling, AI and machine learning are all dependent on data quality so combining both disciplines within the new team provides a wonderful opportunity to progress at pace and deliver real value to colleagues and constituents alike.

Neil

What are the 3 things you love most about your role?

Ejaz:

 1. I love to explore complex data and to predict the best data-enabled options moving forward so that the London Borough of Hounslow and its residents can see real benefits

 2. I love to be able to see hidden opportunities and then interpret such opportunities for wider good

 3. To learn and share data science activities (like machine learning) with collaborative channels such as LOTI (London Office for Technology and Innovation)

Ahmed:

1. I love data engineering because it involves picking pieces of data from diverse sources and integrating them for data driven decisions

2. Consolidating and cleaning the data to create data pipelines

3. Working within the data quality and data science team to make sense of the council data that will support the council’s data-enabled decision making

Anna:

1. Data Quality is like solving puzzles and having the opportunity to solve puzzles is so fun

2. Data Quality tasks are not restricted to any single tool and provide opportunities to constantly upskill in either new programming languages, or new tools, or both

3. Data Quality is something that is important for every team and having the opportunity to work with every team means that you feel integrated into the council very quickly

Neil:

1. Transforming unstructured, poor quality data into intelligence, insight and learning that informs decision making and delivers positive customer experiences

2. As a fellow ‘data geek’ who cut his teeth processing name and address data on mainframes, I really enjoy seeing how technology has evolved and enables the Team to cleanse, analyse and visualise data seamlessly

3. Collaboration and team building

What are your favourite things about working for Hounslow Council?

Ejaz:

1. I love the positivity and engagement throughout the Council

2. Trust and excellent support from line management

3. Able to speak up and contribute positives ideas and thoughts with others

Ahmed:

1. The London Borough of Hounslow is a hub for collaboration and openness

2. Good working environment, although working remotely

3. Opportunity for training and growth

Anna:

1. I live in the borough so I love learning about all the work that is being done to support local residents

2. Hounslow House is a beautiful building to work in

3. The focus on inclusivity in tech is an area that the council are working hard to support and it is closely linked to my own values

Neil:

1. The challenge of building new teams, technology, environments and processes

2. Independence, opportunity to influence strategy, direction and collaborate with internal and external colleagues

3. People!

How to get the most out of your anonymous surveys

Posted on by in Collaboration and Data

By Anna Trichkine, Data Quality Lead

During a period of change, surveys are a go to tool. If my own experience is anything to go by, I would say you’ve probably filled out dozens of surveys over the last year and for most of these surveys you are yet to see the results.

The good news is that the survey results often have important stories contained within them, even if they’re anonymous.

So how can we retell the stories whilst still maintaining anonymity?

The data team at London Borough of Hounslow have recently been tasked with analysing the data for anonymous workplace surveys. These surveys aim to capture how participants feel about working in the borough, and how they feel about working from home. As the data that are collected are anonymous, it is important to try to find patterns or stories in the responses without revealing any personally identifiable experiences. The user profile must not reveal an individual but must provide an insight into a group of people with similar experiences.

How do we do this?

One way to do this is by using decision trees, a type of mathematical model that identifies patterns in your data set by asking true/false style questions.

Using this mathematical model we are able to identify patterns in the data set. The mathematical model takes into consideration all of the questions asked by the survey, and suggests which of these questions are more important. Even if a survey had dozens of questions, it may be that only two or three of the questions have significant differences for the way people respond.

Once these key questions are identified, the data team specify minimum sample sizes for each group to make sure anonymity is maintained.

This combined approach can build a compelling story whilst also making sure that the narrative is anonymous.

Results

Once the model identified the key questions, and the groupings, the team were able to build up a user profile from the grouped responses.

We were able to create 4 main personas, each with unique reflections and experiences of working in the Borough of Hounslow. Underneath these personas were the collective responses for a group of individuals who shared similar thoughts.

Relaying these stories as personas, rather than as graphs and line charts, allows the stories to come to life. We can empathise a lot more with a person, rather than with a line. In this way, the anonymous surveys become more exciting both for our team, and hopefully for those who would like to see the results of the survey.

We will be sharing the personas internally with staff, and would love to hear which persona resonates with you.

First step towards data science collaboration

Posted on by in Collaboration and Data

By Ejaz Hussain – Data Scientist and Anna Trichkine – Data Quality Lead

An exciting learning Journey with LOTI and ONS.

The Data Science and Data Quality Team at London Borough of Hounslow is a newly formed team within Digital and IT. The team is made up of Lead Data Scientist Ejaz Hussain, and Data Quality Lead Anna Trichkine.

As a new team, we will be working to improve data practices and data ethics within London Borough of Hounslow. We will be exploring new opportunities for how to use our data and how to make sure we are making more data-enabled decisions across the borough.

To begin with, we have been selected to join the pilot data science and machine learning programme run collaboratively by ONS and LOTI.

Who are ONS?

The Office for National Statistics, the government department specialising in everything data and statistics!

Who are LOTI?

The London Office for Technology and Innovation, working to support collaboration between 33 London local authorities.

What is the programme?

The programme focuses on developing the team’s expertise in data science, specifically to improve the quality of local government data by using programming languages; R and Python.

This is an 8-12-week programme and we will be meeting with the ONS mentors on a weekly basis.

During these weekly sessions, we review data projects together with the mentors along with other local authority participants who have also been selected to join the programme.

A picture of our first session together is shared below.

Zoom call screenshot of first data science programme session

How will this benefit London Borough of Hounslow?

We will be taking the learning from these sessions and sharing the tools and techniques with other analysts across the council.

We have been given access to the ONS learning pool, a hub of well-prepared learning materials, that we can share with staff across the council.

If you are interested in accessing this content or to find out more about our sessions, please email us and we will be happy to guide and support.

Thank you to ONS and LOTI for this exciting opportunity.