Tag: data engineering

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.

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!