Tag: data science

The importance of Data Storytelling

Posted on by in Collaboration and Data

In the age of big data, it’s not enough to simply collect and analyse data – you need to be able to effectively communicate your findings as well.

That’s where data storytelling comes in.

Data storytelling is a concept that you define in a simple way of complex data analytics aiming and informing a target audience. This practice has gained popularity in recent years as a means of engaging and informing audiences through interactive graphics and narratives.

Data Storytelling in Local Government Authority has been particularly effective in presenting complex data to its residents, helping residents understand important information and improving transparency between government functions and the public. To effectively communicate data insights, data storytelling techniques help to create a compelling and easy-to-understand narrative supported by engaging data visualisations.

Data Storytelling is a natural form of passing information, as it engages with the audience and stimulates their attention through emotions. This approach is particularly effective in conveying information that would otherwise be difficult to understand through raw statistics or technical reports. It is a crucial skill for any data professional or organisation that wants to effectively communicate insights and facilitate better decision-making based on their findings.

How to process information in 6 steps?

Ensuring setting clear objectives is the first step in the data storytelling process. Next, what and how data must be collected and analysed using appropriate tools. Once the data is analysed, it’s time to identify key insights and develop a clear and compelling narrative that supports those points.

image highlighting 6 steps of data storytelling process
Source: LBH Self-Designed

6 common mistakes to avoid?

There are several common mistakes to avoid when implementing data storytelling practices, including:

  1. Picking up a wrong chart, for i.e., 3D charts look great but not easy to read or interpret.
  2. Incorrect use of colour correlation, for i.e., using low contrast colours or colours that are too similar, can make it difficult for viewers to distinguish between data points, categories, or trends.
  3. Missing out supporting guidance, labels, or tips alongside visual segments
  4. Too many data visuals in a small dashboard space.
  5. Ignoring accessibility standards when selecting colours or font sizes in data storytelling can have a detrimental impact on the inclusivity and usability of the visualisations. For i.e., Ignoring colour contrast guidelines can make it difficult for individuals with low vision or colour blindness to interpret the information.
  6. Not aware on audience needs and their expectations.
image highlighting 6 common mistake in data storytelling.
Source: LBH Self-Designed

Few Good and Bad Examples

image highlighting 4 example of data storytelling good and bad practices
Source: LBH Self-Designed

Further takeaway points for you

Exploring Hounslow’s Air Quality Data

Posted on by in Collaboration and Data

Why Air Quality matters?

It is a known fact that poor air quality is unhealthy to all of us, especially for vulnerable groups such as people with medical conditions such as heart issues or asthma, as well as children or the elderly with breathing difficulties. Air quality is not the same everywhere. In other words: pollution can build up in pockets and we call them “hot spots” and potential reasons for these occurring are that they are close to a busy road or near a commercial or industrial zone. Prevailing weather conditions are another contributory factor that impacts air quality measures. So, it is important to us all to monitor air quality regularly, identify troublesome “hot spots”, and ensure that we are using this information to help guide actions and policies focused on ensuring cleaner air for us all.

What do we know about Air Quality in Hounslow?

London Borough of Hounslow partners with Ricardo Energy & Environment who maintain 6 Air Quality monitoring sites across the borough. As well as these sites, there are also third-party monitoring stations like Breathe London. Live stations provide hourly data which hold key measurements of specific pollutants within the air. The current list of live monitoring stations is as below:

  • Brentford
  • Chiswick
  • Feltham
  • Gunnersbury
  • Hatton Cross
  • Heston

Quick understanding of Air Quality measures (Pollutants)

Do you know that air is mostly gas? Air is actually comprised of a mixture of different gases like Nitrogen (approx. 78%), Oxygen (21%) and the remaining approx. 1% hold lots of other gases in the earth’s atmosphere (NASA). The UK Government has provided a national legislation and standards on air quality that identifies key pollutants in the air, like Nitrogen Dioxide (NO2), Particulate Matter up to 10 micrometres in size (PM10), Small Particulate Matter under 2.5 micrometre in size (PM2.5), Nitric Oxide (NO), Sulphur Dioxide (SO2) and Ozone (O3).

How can data science support a ‘data-enabled decision making’ process?

The role of data science brings in a deep lens to interpret data with a new dimensions and opportunities. With the use of key data science technologies like Python and R, you can filter out answers in seconds. At the London Borough of Hounslow, the Data Science & Quality Team have been working on air quality data sets generated during the last 10 years, where we have learned and identified valuable insights such as, seasonal changes impacting the hot spots’ live feeds, last 10 years comparison between hot spots and its performance to gather data, correlating pollutants with each other, correlating data with 3rd party monitoring stations, engineering and deploying machine learning models for predictive insights and utilising cloud technologies for rapid outcomes for data-enabled decision making.

During our data science work, we have learned so many facts and picked up patterns based on air quality data insights, do you know that during winter season pollutants concentration within the air stays longer than summer because cold air is denser and moves slower than warm air. The image below explains last 10 years of seasonal recordings within Hounslow.

data visual for Air Quality and its pattern during seasonal changes.
Air Quality Pollutants / Visual covering yearly seasons

What can we do in future?

The Data Science & Quality Team regularly meets Environmental & Public Health colleagues and are working on future initiatives for the cleaner air in Hounslow. One of the future initiatives is to correlate past 10 years of air quality data against the public health’s respiratory datasets. This initiative will bring in new dimensions and thoughts to build on.

If you have an idea / suggestion to share or to correlate Hounslow’s Air Quality data against your datasets, then please do approach us.

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!