How ready is the public sector for AI?

Artificial intelligence (AI) should now be seen as a core part of business transformation rather than merely an interesting technical project.  It means revisiting the relationship between government and its citizens, and rethinking how public services are delivered.

There are already numerous applications deployed that use AI in public sector.  These fall into five areas:

  • Improve customer service contact centres. Assistants are being used to increase both civil servant and citizen satisfaction through greater productivity and accuracy, and extended hours of support.  There is a reduction in mundane work for civil servants, and the burden from citizens on specialists is reduced.  Benefits are being realized within a few weeks.
  • Enhance knowledge workers. AI is particularly attractive in fields with massive volumes of domain-specific data to find patterns that offer improved results.  Fields include legal and regulatory, policy development, oncology, cyber security, and more generally taking this approach helps those on rotation become productive more quickly.
  • Manage the complexity of risk and Contract governance is one such use of AI by Governments.  Operational decision making has also been augmented by AI by monitoring current situations, assessing risks and making recommendations.
  • Find the best talent and modernise learning. AI is being used to analyse the talent market to find candidates who best fit a role.  Aptitude can be assessed to help build digital skills in scarce areas, eg cyber security.  Furthermore, AI aids learning in content, its delivery and management.
  • Empower developers to build AI-powered Equipping business teams to build applications using AI tooling and training platforms has facilitated integration of AI with both existing systems and emerging technologies such as blockchain.  Governments have been able to provide access to video content for its citizens using audio analysis, improve the way they deliver services using speech to text, and better protect critical infrastructure.

The opportunity apparent here is to deliver better services to citizens more quickly whilst reducing the burden on civil servants.  General characteristics can be drawn from these implementations that can be applied to assess those processes that are suitable for AI and likely to deliver benefits.  These are:

  • Does the process exploit a lot of data? And could it benefit from using other accessible content?
  • Is a personalised service required?
  • Is the process repetitive and reliant on a degree of knowledge and intelligence?

Business teams will need to be prepared to move away from the way things have always been done.  One technique to imagine new possibilities with AI is to creatively explore problems from an end user perspective using Design Thinking.

AI needs IA

Too few artificial intelligence (AI) projects succeed.  Many organisations approach AI believing that you can collect data for an algorithm in the hope that it realises the anticipated benefits.  Instead you should look at data and design a system to address a problem, not an algorithm.

Here are some keys to success for adopting AI.

  • Select the right business problem. This must be one for which a team already exists and has the data.  It avoids the pitfall where, “We need to test AI,” results in a deceptively attractive initiative which has low business value and is hard.  For example, a business process is required to collect data.  Nevertheless, there is a conundrum for many organisations that the business case to get the data requires a demonstration of AI.
  • Look at the data. Typically, organisations significantly underestimate the effort needed to orchestrate the data in readiness for AI.  AI needs accurate data, and data cleansing and preparation takes 80% of the effort.  This is a hard engineering problem and requires a sound approach to information architecture, technologies and a range of skills, not just data scientists.
  • Build systems, not algorithms. Many assume that a sequence of steps is sufficient to generate insight and recommendations.  However, feedback is crucial to improving overall accuracy.  It is complex with lots of moving parts and demands a multi-disciplinary approach.

AI must be transparent for the public to trust it.  This is especially significant for the public sector because important decisions must be explainable.  It is essential to understand who trains the AI system, what data was used to train it, and what went into the recommendations made by the algorithm.  This extends the realm of information governance.

In summary, AI needs IA: Information Architecture.

Wimbledon technology impressions

The dominant impression left with those taking IBM’s technology tour at Wimbledon this week is how artificial intelligence is used to enhance fans’ experiences of The Championships through automation and scale.  The magnitude of what it takes to amplify match data and content out to millions of fans is always a revelation.  This spans drawing fans to Wimbledon’s own digital platforms, supporting its media partners, through to providing a valuable service to the players.


AI and Augmented Reality

The Championships Poster for 2018 applies artificial intelligence (AI) to celebrate the 150th anniversary of the All England Lawn Tennis and Croquet Club.  AI is used not only to select 8,400 photographs that make up the mosaic from the archive of over 300,000, but also to match the colour, tone and content of the picture used in each tile to the part of the picture it represents.  The reaction when I enlarge a section of the mosaic, such as the umbrellas by the umpire’s chair which is made of photographs of umbrellas, is amazement.IMG_0066

IMG_1181There are various pictures displayed around the grounds when visitors can experience augmented reality (AR), including the mosaic poster.  Their locations are indicated on the map in the mobile app.  I show how you can tap on the ‘AR Experience’ tile in the app.  The camera recognises the picture, and in the case of the poster, the app launches the video showing how it was made.

You can experience augmented reality for yourself using Wimbledon’s phone app by pointing the camera at the poster on

Trusted data

Wimbledon Interactive is a system running on 400 press desks at The Championships that contains 2 million pages of data.  It is only available on site, and I explore the wealth of data of current matches and Wimbledon’s history all the way back to 1877 on my tours.  Screen Shot 2018-07-06 at 08.10.24IBM employs tennis players to read matches and record the data associated with every point accurately within a second.  This trust in data is essential for amplification out to fans on Wimbledon’s own digital platforms and by the world’s media.

Players from the six show courts receive point-by-point video analysis about twenty minutes after the match completes to help them prepare for the next round.  This is always a highlight on my tours.  In addition, IBM has a data science team on site to assist Wimbledon and the media with access the best possible information, including bespoke reports for any angle a journalist might want to investigate.

One example of how data is made available to fans is the tactical Keys to the Match delivered in the IBM SlamTracker on  They are the top three areas that each player in a match should focus on to maximise their chances of winning.  Keys are custom generated from analysis of individual player data, including the 4.8 million tennis data points collected at last year’s Championships.  Analytics have been further honed using ball placement and player movement insights.  Fans are able to monitor players’ performance against their keys as the matches unfolds.

Brand quality

IBM uses artificial intelligence to automatically generate a video highlights package within five minutes of a match finishing.  Clients are amazed that the speed.  Video analysis of player gestures and detection of their emotions is combined with audio Screen Shot 2018-07-06 at 08.07.59analysis of the crowd’s reaction, e.g. clapping and cheering, plus analytical insight from the data collected.  The points with the highest excitement score are assembled, along with captions generated from meta data that tell the story of the match, for Wimbledon to share with fans on social media and its digital platforms.

Wimbledon has become the host broadcaster of The Championships this year with the launch of Wimbledon Broadcast Services.  It is indicative of Wimbledon’s shift to become more of a data-driven media organisation rather than simply global sporting event – this digital transformation is food for thought for those joining my tours.  Despite this approach, Wimbledon is permitted one hour of tennis action coverage from each day so as to not undermine its media partners.  The digital team uses the excitement level calculated from analysis and AI to quickly search for the points of greatest interest.  This enables the team to optimise this hour to maximise fan engagement by easily identifying and sharing the moments that matter most.

Taking Wimbledon to its fans

IMG_1196Wimbledon aims to be where its fans are.  In 2018, it is widening its appeal to those that use messaging.  The Wimbledon messenger can be accessed from within Facebook Messenger to provide up to date scores on matches, monitor the progress of your favourite players, and access news.  It also provides assistance to all fans in natural language using an AI chatbot building on the ‘Fred’ in-app service that was introduced last year.

You can access the latest information from within Messenger by searching for Wimbledon on Facebook.

I found that all the clients that I took on tours this week, technical or not, are impressed with the scale, reach and focus of the Wimbledon’s digital operation.  Find out more about the technology at


Wimbledon’s use of AI to engage fans

At one o’clock this coming Monday, Roger Federer will walk out on to Centre Court to begin the defence of his Wimbledon Championship.  I particularly remember his semi-final match last year.  I was in the bunker where I runs the technology for Wimbledon, and about eight minutes after the match had finished, Wimbledon had produced a two-minute video highlights package of the match.  This was the first time that a sports highlights had been generated automatically.

The rise of video

Wimbledon continues to extend its appeal to a time-poor, younger demographic, and sharing short videos is a key element of the strategy to drive engagement on its digital platforms.  Video views were up 75% year-on-year to 201 million in 2017, of which 14.4 million were such match highlights.  Automatic generation accelerates production so that Wimbledon has first mover advantage, and it enables scale.

IMG_1120 29June

It is achieved using artificial intelligence (AI): learning player reactions in analysis of video, detecting crowd reactions by applying AI to audio, and fusing both with statistical analysis of the data to identify the most important points in the match.  Meta data is used to generate captions that tell the story of the match in the highlights package which Wimbledon then shares with fans through its digital platforms and on social media.

AI becomes the artist

This is an example of technology innovation using AI, Cloud and Data at Wimbledon – 2018 is the twenty-ninth year of IBM’s partnership – that I described yesterday at the Cloud and Data Summit held at Landing Forty Two in London.

Cloud and Data Summit

I opened my talk with a video of the poster that Wimbledon created using AI to celebrate the 150th Anniversary of the All England Lawn Tennis and Croquet Club (AELTC).  AI has become the artist to create a poster.  It looks like a water colour but is actually a mosaic made up of 9,000 images.  These were selected from over 300,000 images in the AELTC’s archive using artificial intelligence to match image recognised content and colour tone.  You too can watch how 150 years of archive photography has been used to stitch together a single beautiful image.

Social engagement

I told the story of data, how it is captured courtside by tennis professionals who can quickly read a match.  They aim to accurately capture all the data associated with every point within a second.  It’s about making data simple and building a trusted foundation that allows insights to be scaled on demand.

Wimbledon combines such insights with analysis of conversations and what is trending about the Championships on social media.  It uses Watson AI to exploit 23 years of articles, press and blogs – 11.2 million words have been analysed – so that it can share facts, video clips and stories with fans in the moment.

Digital resilience

IBM runs Wimbledon’s applications in the Cloud.  Four IBM public cloud and three private cloud data centres around the world are used, offering elasticity and resilience.  The software-defined operating environment allows capacity to scaled up quickly for The Championships.  Easy access to Wimbledon’s digital platforms is sustained through huge fluctuations in demand, such as a spike in interest in an epic match.  Capacity is quickly deprovisioned when no longer required to optimise the cost of infrastructure.

Over 200 million security events were halted during The Championships in 2017.  IBM correlates and normalises security event data to prioritise them and remove false positives.  Security analysts make use of threat intelligence from IBM’s X-Force research on vulnerabilities and malicious IPs, etc.  A knowledge graph is generated to help security analysts understand what is happening.  Watson for Cyber Security offers assistance through its application of AI on the corpus of security research, information on events, security notices, blog posts and more.  The result is a reduction in the time taken to analyse a threat from sixty minutes to one.

AI assistant

Wimbledon launched “Fred” last year, an AI assistant that helps visitors prepare for and make the most of The Championships.  This year, Wimbledon continues to put content where its audience is.  “Fred” powers the new Wimbledon Messenger, a service for millions of other fans available through Facebook Messenger.

Wimbledon’s digital platforms provide the window into The Championships for many fans.  A fabulous experience is enabled by AI that is powered by the IBM Cloud to exploit data.  Experience a little of this for yourself by downloading the Wimbledon app or visiting


Re-thinking defence and security for the digital age

Strategic threats

The world if facing four strategic threats.  We have unstable but predictable threats from Russia.  Unstable and unpredictable threats in the form of migration, terrorism and nuclear from parts of Africa and the Middle East.  Stable and predictable threats, for now, from China.

This is the backdrop set out by a former senior military officer at SPADE last week in Copenhagen.  This defence conference, now in its sixteenth year, was sponsored by AFCEA, IBM, Samsung, Secunet and SES.  The fourth threat is ourselves in the western world: populism, diminishing cohesion and leadership.  It raises the question, how prepared are we to face these threats?


Informed decision making

blog data

Let’s consider a macro perspective on information.  I have previously blogged on how data pipelines enable smarter decision making.  They take raw data, fuse and analyse it to sift out the valuable indicators.  These are used to build a coherent picture of what is happening.  The challenge is then making decisions that use the evidence rather than taking the easy option of aligning with prevailing perceptions, and then being capable of acting accordingly.

Four areas to improve

Speakers from eighteen defence departments around the world, NATO and the EU debated how technologies can help present the indicators and coherent picture.  An essential, if partial, contribution.  Here is a selection.

  1. Coherent C4ISR is required. Fusion of sensor data and integration of systems, both tactical and command, overcomes current siloes.  A step-by-step approach should be taken.  Connectivity and interoperability between allies are important design principles.
  2. Mission, including platform, readiness needs greater focus. It is dependent on network enabled capability, but also requires smarter approaches to communications, maintenance, optimising inventory for operations, and through the supply chain.  Mesh networks, IoT, predictive analytics and artificial intelligence (AI) technologies offer opportunities.
  3. Kinetic operations have become secondary to the digital domain. Information security efforts must focus on protecting communications as well as information.  Approaches are needed to deal with influence through disinformation.
  4. Use of Artificial Intelligence is gathering pace in the commercial world. Scale is being achieved through the use of machine learning, graph analytics, and video and speech analysis.  Defence will need to shift from requirements-based procurement to writing capabilities statements for what it needs on the battlefield.  It is the only way to stay ahead of the emerging technology curve.


NATO sees mobility, cloud and AI as technology disruptors.  From these come opportunities, some of which are outlined above.  Nevertheless, re-thinking defence and security for the digital age – the theme of the SPADE conference – demands more: digital reinvention.  Critical success factors are:

  1. Business projects with business commitment, not IT projects
  2. Create culture of innovation where air cover is given for teams to fail
  3. Agile thinking in place of programmes
  4. Partnerships with benefits for everyone
  5. Senior/board level focus on talent

Integration across siloes, interoperability and the application of AI are just three examples of what it takes to be ready for highly intensive, full spectrum operations.


Becoming a data-driven organisation

Organisations struggle to become data-driven if they retain traditional siloed business functions.  The hand-offs resulting from their differing business goals and inter-communication overheads incur too much inertia.

The real question is:  How do you become outcome driven?  It requires those who interact with customers to understand what is happening in context – being informed – to be empowered to make decisions and to be equipped to act according to the business goal.

It takes an end-to-end approach to become an outcome-driven organisation

I have shown how to build a slice of a data pipeline in previous posts on my blog.  This end-to-end approach is the enabler of shared situational awareness.  Data is available from source in a shared platform, which in turn feeds information to all parts of the business.  However, vertical organisation silos also need to be dissolved in favour of outcome-driven value streams.  Those at the front line must be able to see all the way back to the start of the information cycle safely within the organisation’s information governance policies.  Everyone then has improved and timelier shared awareness.

Each area of the business that interacts with customers operates as a business value stream.  These streams enshrine the concept of bringing the work to the people, rather than shipping people to the work.  This increases quality and employee engagement and reduces internal conflict.

Consuming higher value services releases business capacity

Teams are assembled for value streams.  They are multi-disciplinary and obviate the need for traditional IT programmes and shared services.  The maintenance burden of sustaining existing IT systems is reduced because migrating workloads to the cloud means that previously highly sought after, shared technical expertise can be dedicated to each business area.  Each business area can concentrate on optimising its outcomes.

Teams are able to find and access the information they need using the data platform and configure a pipeline to produce the insights they need for decision making.  This employs techniques including data analysis, identifying patterns, algorithm development, and more.  The pipeline can be augmented by AI and machine learning for greater automation and accuracy.

Micro-services architectures provide teams with the technical capabilities to act, but that is the subject of a future post.  Suffice to say that this offers a step change in automation and agility.

Such automation enables business operations to react more quickly to changes.  It frees up time for people to learn new skills, for better quality engagement with each customer and to focus on tasks that rely on imagination, intuition and empathy.

The profile of technical skills an organisation needs to compete has shifted

Each business area will use the platform to easily create, maintain, grow, shrink and decommission its own systems.  They will be able to exploit automation, sophisticated analytics and machine learning.  As I have shown in previous posts, the barriers to deployment are so low they will be able to start small, experiment and enhance capabilities in days or weeks on the platform without creating unsupportable or under the desk IT.

Only then can you truly become data driven and maximise the benefits of a data pipeline.

Simplifying data science

Cloud computing is changing the way IT services are accessed and consumed.  We are seeing that the dependence on infrastructure expertise is diminishing by engaging higher up the stack.

In my previous post, I showed how to ingest ship positioning data into a Cloudant NoSQL database using Node-RED on the IBM Cloud.  This time I shall show you how an analyst or data scientist can find and access information quickly so that they can spend more of their time using their expertise to derive insight, discover patterns and develop algorithms.

I use the Catalog service to create a connection to and description of my Cloudant data.  The connection simplifies access to data sources for analysts, and I can associate data assets including their descriptions and tags with that connection so that data can be easily found.  The catalogue, connections and data assets are subject to access control and I can implemented governance policies, showing lineage, for example.

Let’s see how we create the catalogue entries on the Watson Data Platform in the IBM Cloud.

See how to share data assets using a catalogue.

Analysts are able to access data assets in the Catalog in notebooks using the Data Science Experience (DSX).  Create a project and simply add the required assets by picking them from the catalog.  I can then create a Jupyter notebook within DSX and generate the Python code to connect to the data asset I need.  Furthermore, DSX automatically provisions a Spark instance for my analysis when I create (or reopen) the notebook.

All this only takes a few minutes as I show here.

See how to use a data pipeline for analysis.

This degree of automation is achieved by concentrating on configuring services that make up an overall data pipeline.  The links between the services simplify the tasks of analysts to find and access data from environments they are familiar with.  The dependency on IT resource to provide and manage data platforms is removed because the analytics engine is provisioned as required, and released once the analysis is complete.

In addition, analysts can share their work, and teams can be built to work on problems, assets and notebooks together.  Data science has become a team sport.

I shall describe how such an end-to-end data pipeline might be implemented at scale in the next post in this series.  In the meantime, try out the Watson Data Platform services for yourself on the IBM Cloud at

This is the third in a series of posts on building an end-to-end data pipeline.  You can find my notebook and the other data pipeline artifacts on GitHub.

Ingesting IoT data without writing code

Cloud computing is changing the way IT services are accessed and consumed.  We are seeing that the dependence on infrastructure expertise is diminishing by engaging higher up the stack.

I described an end-to-end data pipeline in my first post.  I shall now show how to build the data capture and ingest processing as a flow in Node-RED purely through configuration without writing a line of code.  Node-RED offers flow-based programming for the Internet of Things and is available at and on the IBM Cloud.


My flow implements a straightforward pattern.  Firstly, I have a node that reads data off an MQTT feed, then I undertake some data wrangling, which in this case lifts the json message payload to the top level of the document.  As we shall see in a subsequent post, this processing could be arbitrarily complex analytics and manipulation.  Finally, I write the documents into a Cloudant NoSQL database.

See how on YouTube.

The video shows how I am able to provision the Node-RED flow environment and the Cloudant database as one pre-configured service, ready for immediate use.

We shall see how we access the data I have captured for analysis in my third post.  In the meantime, try implementing the flow for yourself using the Node-RED Starter service on IBM Cloud.

This is the second in a series of posts on building an end-to-end data pipeline.  You can find my Node-RED flow and the other data pipeline artifacts on GitHub.

Building an end-to-end data pipeline

Becoming a data-driven organization sounds so simple.  But fulfilling the vision of making smarter decisions takes more than simply providing analysts with tools.

In this series of blog posts, I shall show you how to build an end-to-end data pipeline.  It allows information to be captured from source, processed and analysed according to business need.  I shall configure the pipeline using cloud services as a business user, thereby removing the dependency on traditional IT infrastructure and the set up and maintenance of data platforms.

My pipeline is made up of three main elements today, though I have plans to augment it with more complex processing using additional cloud services.

Ships are required to broadcast their positions.  Messages are picked up by a beacon in southern England.  This AIS data is processed at the edge by an MQTT broker and broadcast by topic.  This is the data source for my demonstration.


  1. The first step is to pick up the AIS json data feed published by the MQTT broker and process it for ingest into a database. I have done this without writing any code.  I configured nodes in Node-RED to construct a flow, which inserts the AIS data into a Cloudant database.  I used the Node-RED boilerplate service in the IBM Cloud which includes a bound Cloudant service.
  2. Secondly, I used the catalog service in the Watson Data Platform on the IBM Cloud to create a connection and a data asset in my catalog. The catalog allows me to describe and share data assets so that they are easy for people to find and use subject to access controls and governance policies.
  3. Then I access the catalog from the Data Science Experience (DSX) to populate my Jupyter Notebook with the access to my database. DSX provisions a Spark instance automatically for my analysis, which is to plot the positions of ships on a graph.

Data scientists typically work individually, struggle to find the data they need and are often unaware of the assets, code and algorithms that already existing in their organisations.

These challenges are overcome by using cloud native data pipeline services available on the IBM Cloud.  The analyst is able to get started on analysis within minutes of deciding what data is needed to tackle a business problem.  It is easy to find and access using the catalog and the enabling infrastructure to execute analytics on large amounts of data is provisioned automatically for them when they create a notebook.  (Furthermore, the Spark instance is de-provisioned when the notebook is closed.)  Data assets and notebooks can be shared so that data science becomes a team sport.

Get ready to try for yourself by signing up to the Watson Data Platform on the IBM Cloud at

Other posts in this series include:

Acknowledgements: thanks to Dave Conway-Jones, Richard Hopkins and Joe Plumb for their contributions.

Data is not the new oil

You’ve heard it many times and so have I:  “Data is the new oil”

Well it isn’t.  At least not yet.

I don’t care how I get oil for my car or heating.  I simply decide what to cook and where to drive when I want.  I’m unconcerned which mechanism is used to refine oil or how oil is transported, so long as what comes out of the pump at the garage makes my car go.  Unless you have a professional interest or bias I suspect you’re much the same.

Why can’t it be the same with data?

Well for a start, the consumer of data is often all too aware of the complexity of the supply chain and the multiple skills and technologies that it takes to get them the data they wish to consume.  Systems take forever to create and are inflexible in the wrong places.   The ability to aggregate data is over-constrained by blanket security rules that enforce sensible policies, but result in slow moving or over bureaucratic processes and systems.

Today’s cloud technologies have helped, but even here, data services are aimed at developers as the consumer of data, not the end user of it.

The consumers of the new oil would love to be ignorant of where it came from, but they are all too aware and involved in the supply chain that they try and coax to do what they want.

Even with today’s cloud technologies, data services are predominantly created for developers, not the true consumers who understand the data.

Wouldn’t it be wonderful if those who make business decisions could find naturally described information when they wanted?  If they could use it as they wish without regard for the underlying infrastructure?  All with the confidence that access controls and data protection measures are built in.  Enforcing governance policies within the platform builds trust and helps achieve regulatory compliance, such as GDPR.

These are characteristics of a data pipeline: services that ingest data from sources, govern, enrich, store, analyze and apply it.  How data is stored is no longer of concern.  Data is available to all without aggravation.

With their latest cloud platforms, companies like IBM are delivering platforms that do precisely this.  IBM has even published a Data Science Experience that enables a data scientist to build their own pipelines with a rich palette of ingest, machine learning and storage technologies.

We take oil for granted.  Can you say the same for the data you need to drive your business forward?

Try out the Data Science Experience on the IBM Cloud.