Meet the team: Liz Elliott
Wallscope’s Data Analyst Liz chats about her background, her views on the data challenges facing businesses, and the need for improved data quality and literacy
In March we were excited to welcome a new addition to the Wallscope team — Liz Elliott. Liz has a broad background in data analysis, data management, and commercial technology research and development. She has previously worked as a data analyst for the NHS, and a validation engineer in the semiconductor industry.
As well as working for Wallscope, Liz is studying for a vocational MSc in Data Science at Edinburgh Napier University, funded by a scholarship from The Data Lab.
Hi Liz! Can you tell us a little bit about your background and how you became interested in working with data? What are your particular areas of interest?
I started out as a hardware engineer, working with sensors and signal processing systems. I like investigating how things work in the real world. In engineering that tends to involve gathering and analysing a lot of measurement data, but also working with users to understand their priorities and preferences.
Developing audio and imaging hardware led to an interest in psychophysics (how our brains process and respond to physical stimuli such as sound and touch) and then in health tech more broadly. There are huge opportunities for using data and tech to solve problems in health and social care, so for me that’s a really exciting and rewarding area to be involved in.
What are you working on at the moment?
I’ve been learning about Wallscope’s Data Explorer tools and methods for knowledge mapping and data discovery, doing some background research for various upcoming projects, and working with open datasets to generate synthetic patient data based on Scottish population demographics.
In your opinion, what are the main data challenges facing businesses at the moment? Do you feel that these challenges are fully understood?
Many organisations are already gathering a lot of information as a by-product of their core business, but not necessarily making the best use of that. It could be because they don’t have a clear picture of what data they hold, or because they don’t know how best to organise or work with it.
It’s not uncommon for data to be disorganised, undocumented, or stored in formats that are challenging to analyse — such as image files, unstructured text within emails or reports, or even hard copy documents.
SMEs in particular are not always in a position to recruit data specialists to address these issues, and larger organisations may struggle with interoperability problems across different teams or platforms. Traditional approaches to organising company data, such as hierarchical file structures or relational databases are not best suited to capturing complex or dynamic relationships between disparate information sources, so knowledge can end up in silos or fall between the gaps completely.
Areas such as information governance, security, and data ethics can also be quite daunting, even to experienced data practitioners. These are all evolving fields, and it can be hard to keep up with current best practices.
Scotland has a really supportive data community, and lots of initiatives to help smaller businesses and third sector organisations with their data challenges, but there’s still a lot of untapped potential.
What can businesses do to improve their working practices in relation to data and data management?
Data quality and data provenance are really important. It’s almost always much better to have a smaller dataset that you are confident is accurate and complete, than to have a large, noisy dataset with gaps and inaccuracies. The confidence comes from having robust, transparent data gathering processes but also from capturing good metadata.
Documenting what’s in a dataset and where the information comes from provides the context needed to make your data useful, discoverable, and able to be linked to other data sources. Even simple steps like adding a brief description to a spreadsheet can save a lot of time and avoid duplication of effort.
Underpinning all this is the need for data literacy. It can be difficult to maintain quality, ensure metadata gets properly recorded, and that data is made available in suitably machine-readable formats if the people involved don’t understand why those things matter or are not comfortable working with the available tools. So there’s a need to train people but also a need for systems with accessible and intuitive interfaces, able to work with natural language rather than a more structured format.
How do you see Wallscope being able to help with this?
Wallscope’s knowledge mapping process can help organisations understand what data they hold, identify any gaps, and map out a pragmatic route to achieving their goals. That doesn’t necessarily have to involve custom tools or platforms. You may already own the tools you need, or could use an off-the-shelf product.
Where a bespoke solution is the best option, Wallscope’s tech employs knowledge graphs to describe connections between data from different sources, so a single, accessible interface can be used to search, visualise, and learn from all the available information. That could cover data from a single organisation, multiple organisations, or incorporate wider networks, such as IoT systems, open data catalogues, and the web.
One of the cool things about Wallscope’s approach is that it doesn’t require your data to be stored in a specific format or database, which is often a barrier to implementing other search and visualisation tools. The AI Toolkit works with a wide range of formats, including unstructured text, and can accommodate existing file systems, so organisations don’t need to undertake time-consuming cataloguing and indexing processes, or try to make their data fit into overly complicated database systems to make it searchable.
If you’re interested in finding out more about Wallscope’s technology and consulting services, we’d love to have a chat! Drop us a line at contact@wallscope.co.uk
