Bringing together the best people and ideas from the world of open banking and open finance


Taking stock of your data

Tuesday, June 9, 2020

When I get asked the question – “How do you take stock of your data? I always reply with a question back “your data, what do you mean by your data”? Often the individual reply is about the data the company owns. In the context of this article and response to questions posed on the recent Open Banking World Congress panel, it is my belief people (citizens) own their data.

We can get into a debate around what happens when a company adds individual data attributes, profiles, categorises and segments amongst other data processing terms. The base line remains for me. It is still the individual who should have access and control to their data even it is modelled. Models form the basis for many personal decisions non more so than financial ones.

People own their personal data and have rights to see, control what companies do with to their data. We are experiencing a movement towards data trust, ethics, transparency and enabling people to have clear useable rights.

When we explore the market further see many new, scalable business models unfurl. The pendulum is swinging from companies owning data towards being valuable data custodians.

In pulling this response together I refer a lot to I. Behind I is always a smart team working with agile mindsets alongside me.

Question 1 – When trying to understand where do data opportunities and risks lie?

As a strategist, I turn to the classical models. Firstly, looking at the challenges, problems we as people face. I create a list, trying to figure out how the broad trends, impacts are expected to be able to provide information about the likely direction of travel. I then build a holistic view.

The data helps me see the opportunities and risks. Before exploring deeper and looking at business risks, opportunities, I list out the key themes and seismic shifts. These help me build a strong sense for where possible new opportunities lie.


Kevin diagram1

Author Kevin Telford – Whilst Advising ThoughtWorks

The previous diagram shows by mapping the events and citizen trends it on a slide brings it to life the key industry impacts.

A view of the customer trends overlaid with an exploration of the seismic shifts we have on the horizon. In the case of COVID-19 pandemic it is difficult for most not to have to re-write their strategy simply replacing Industry Impacts with COVID-19 Impact brings us to the same starting point – people and their problems.

I look at the map through the lens of people, data, and technology where my interests and passion live. When taking stock of custodian data (what we thought we owned) I think in the round and take look a look to the future. Never the now.

Future gazing helps me see things that may be fuzzy but give insights into future legislation or the potential for innovation. I know we must come back to the now, however as the previous diagram shows we pretty much understand the now.

We are now in a place where we can list the risks for example from here, we can explore where our relationship with the trends are. For example.

  • Legacy tech or lack digital transformation
  • Cultural challenges and lack of agility
  • Unable to access data, lack of useable and internal view of the data only

We all experience these types of issues right now. CORVID -19 has just fast forwarded us to respond to these risks now, bringing forward digital transformation, importantly new data sources and potential for partnerships. Taking stock, searching for opportunities that will happen centre on purpose driven organisations in my point of view.

Organisations putting values in their business models will win. It is easy to list the areas that will be key growth areas for example.

  • Financial solutions for sustainability
  • Finance services for vulnerability and well being

Kevin diagram2

It is also easy to see near term opportunities around “ethical debt management” taking stock of your data and the events help assembler new ways of working too. It may mean new environments for data. New ways of sourcing data that the recipient organisations receive in a data lake so that no data is refused and the recipient works the data to a useful state.

For example, we have created a Data Space. An environment with tools, capabilities call the Global Open Finance Centre of Excellence. Here we identify new challenges out there that people are facing, we look at partners that may want to tackle those, aligned to our values and value that we can deliver together such financial wellbeing for vulnerable people.

We have five strategic pillars to frame the types of use cases we want to build and address the problems we know. Building the culture, ways of working around the precious data assets, using it for good is an extremely rewarding position and present an abundance of opportunity yet to be discovered. So much so we have over sixty use cases to test.


Question 2 – What possibilities are enabled that we haven’t explored yet?

Data has been used since time allowed. The rise of the connected world through the like of the internet of things (IoT), cloud, AI, new technology and people’s capability to innovate means the possibilities are endless. A cautionary note is innovating for good and evil or unintended consequences.

The possibilities for data driven innovation exist across all of life. In every context from combating climate change, to wellbeing, ageing well or any other matter. Finances runs horizontally through every sector so picking one area aligned to the race from open banking to open life I see a role for organisations to shift from vertical models to cross sectors cut through models as shown below.


kevin diagram3

Author Kevin Telford – Whilst Advising ThoughtWorks

Trusted organisations with exemplary data governance, compliance and customers have an understanding and control of their data to access life easier, frictionless auto optimising money at what level of wealth is underway now. It is easy to imagine trusted FS companies or other organisations becoming life partners. I see banks as an example of this through collaboration with tech and organisations with agile methods and success culture.


Question 4 – Where do you see cross sector flow opportunities for partnerships, innovation etc.?

I really like this question. The depth of response is a book because I see so many components coming together to enable cross sector innovation. So much so many verticals will disappear and how we classify businesses today will become obsolete with rise of vertically integrated business models.

kevin diagram5

Author Kevin Telford – Whilst Advising ThoughtWorks

The diagram above is a representation laid out in end to end description. The reality it looks more like a constellation in reality a connected life across an ecosystem. It is complex but making it reality may not be difficult.

The reality for me here is centered around consent and a federated sovereign identity that flows through terms and conditions with my own set of permissions and for what use my data can be used as an enabler to cross sector solutions. We have built several cases studies and nine new business models that this approach opens.

It is easy to imagine wealth, health and lifestyle innovation as stand up propositions that help solve people’s problems. The harder bit is stitching the tech and data flows required. For example, some essential components may be in a data lake (as previously mentioned) to consumer all data versus structured vault with the tools to render the data useful.



Question 5 -What other scope is there for processing data i.e. training AI ML?

When taking stock of data and how it is processed, we recognize that largely the ways of processing have remained static in many large corporations. Yes, we now have the cloud, the AI capability, ML however we still have large analytical, intelligence teams centered on the data assets looking for answers.

We have data and we have analysts working to solve problems or create new outputs. The reality in the spirit of algorithms they have largely been scripted in the image of the people building them.

The role of AI and ML training the algorithms is fascinating. For the moment I see it as a clear glass box concept versus the old black box or decision engines. Right now, the concept of taking old code and testing it for bias and reconstructing it without is where some of my thinking is.

The capability to disassemble code to probe the why, the construct, the outcomes and even challenger the author(s) appeals somewhat. Can it be done? Yes. As of now it is more of a compare old code with new. Clear bias we see as outcomes of bad authors or lack of trying to see intended consequences alongside unintended outcomes. We see it in financial decisions manifest itself in racism or gender equality.

What we do not know did the author intend these outcomes. So, we have a way. If we have data spaces with data in a safe secure environment, we can run the math’s. Using the old and comparing with the new using AI and ML to do the calculations, present the results and interpret biases. Training and retraining through processing the data before it is live in the real world.

Another area we are seeing a big rise is in the activities pre-processing where design driven ways of working is setting up some very useful ways or working. Asking the Why of the problem. Querying what data points do we need for effectiveness and efficiencies plus a whole lot more. Its not just the data science in this method teams made up of right- and left-hand side, whole of brain teams are collaborating right from the onset. Legal people even joining in on early in the product innovation process ahead of processing.

Question 6 – Who owns derivative data?

If the output data is derived from personal data, then the citizen owns the rights to derived data. They can request, interrogate and have the rights to change the “ownership” or construct of the data.

If we move to anonymisation, encryption with no way of reconstruction to an individual then there are legitimate cases to harness derivative data whereby companies can be custodians of the data and use it for legitimate purpose.

The exception of relaxing on ownership is when a crime is suspected or committed, I see ownership within democratic principles acceptable with governance and redress.


Question 7 – Reciprocity of data when voluntary or compelled?


Back to the beginning with one of my favorite topics right now an area where I am spending so much of my time right now. We look to some countries around the world. One with 23m people and 6 CORVID-19 related deaths. Where they have tech track and trace. Mobile channels, real time for proactive interventions in a country that holds their government to account for the services they provide.

kevin diagram6

Author Kevin Telford – The Global Open Finance Centre of Excellence Descriptive Example

When asked would you share your data to fight CORVID-19 and the tech, gov or the people were trusted would you be happy for your data to be shared, exchanged, or held. Under what terms of reference would you do so? It varies on many levels.

When asking terminally ill people, disadvantaged, experiencing trauma the question would you share your data for the benefit of others what do you think they would say? Yes of course. When revealing it may be shared with insurance companies for their models, how their response to grant consent changes.

With every data breach and when there are bad actors doing data for good on a reciprocal or compelled model then the “sharing” question gets tougher. In an environment like we have built at the Global Open Finance Centre we will at some point face into these questions. Tough questions would organisations face into the insipid Money Mule issue.

Where crime is committed through aspects like trafficking and drugs. Where a tough problem might be solved through mobile companies, money transfer orgs, banks, fintech and other collaborating around “shared” data of the highest order. Legitimate interest, compelled or otherwise fades for many when the victims could find there way out by bringing in support agencies, professionals alongside the trusted organisations that want to make a positive social impact then we can see a way we might be able to stand up such use cases.

For the time being anonymous, deidentified data or whatever level of security to gain insights is where the centre of the opportunity and risk is. Helping use non-identifiable data to provide GOV.UK with insights, more valuable, useful data to make economic decisions is a useful point for data collaboration in the eyes of a citizen.

Trust and Social Impact using personal data for good are the consent enablers.

Author: Kevin Telford