As I think back on my time leading fundraising for a nonprofit, I’m not sure I considered myself much of a data leader. But, in truth, I was utilizing data every day to steer decision-making that would ultimately positively impact our organization’s health and growth. In fact, I was likely the heaviest user of organizational data.

Data powered everything in my day-to-day, from helping me segment and send targeted messaging to donors based on preferred channels, to analyzing past giving patterns to predict when they’re likely to give again, to predicting future grant funds and event revenue so that I could plan accordingly. Data helped me track campaign performance and donor engagement, as well as show donors and foundations how their funds were impacting service population outcomes. I was able to tell incredible stories of long-term impact because of data collection and analysis. And, had I ignored what my data was telling me, I would have been aimless—instead defaulting to random gut-based decision making, unable to back up my decisions or make a case to shift away from long-held strategy beliefs.

No matter your job, data plays a critical role in both your individual success, and in the success of the organization as a whole. From influencing day-to-day decisions to informing long-term strategy, data is an essential resource to tap. And in today’s world, embracing data leadership is no longer optional — it’s a necessity.

The imperative of data leadership

In today’s digital age, data is generated at an unprecedented rate. Every interaction, transaction, and process produces valuable data points that, when analyzed correctly, can provide incredible insights, including opportunities for cost savings and new revenue streams. A McKinsey Global survey found that respondents at high-performing companies “are three times more likely than others to say their data monetization efforts contribute more than 20 percent to company revenues.”

Organizations that leverage data effectively are better positioned to outperform their competitors. A recent found that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times more likely to be profitable. In an era where competitive advantage is fleeting, the ability to swiftly interpret and act on data can be the difference between success and failure.

Data leadership across business functions

Let’s dive into three business functions and how they benefit from harnessing their data.

• Marketing: Personalization is no longer a luxury; it’s an expectation. By analyzing data from various touchpoints, marketers can gain a 360-degree view of their customers. This includes understanding their preferences, behaviors, and pain points, which in turn enables the creation of highly targeted and relevant marketing campaigns. Data allows marketers to tailor their messages to individual customers, improving engagement and conversion rates. According to McKinsey, companies that thrive at personalization earn 40% more money from these activities than the average competitor. What’s more, data-driven marketing provides clear metrics to measure the return on investment (ROI) of marketing campaigns. This allows leaders to allocate resources more effectively, optimizing marketing spend for maximum impact.
• Sales: In sales, data is a powerful tool for identifying opportunities, improving customer interactions, and forecasting future performance. Data can help sales teams prioritize leads based on their likelihood to convert. By analyzing past interactions, demographics, and behavioral data, sales leaders can create predictive models that identify high-potential leads. Data-driven forecasting models can provide sales leaders with insights into future sales trends, helping them make informed decisions about resource allocation and target setting.
• Strategy: Strategic decision-making is inherently data-driven. Leaders who incorporate data into their strategic planning can make more informed, forward-thinking decisions that drive long-term success. Data provides valuable insights into market trends, competitor activities, and customer needs. Strategic leaders can use this information to identify opportunities for growth and innovation. When it comes to team performance, data-driven performance measurement allows leaders to track progress against strategic goals. Key performance indicators (KPIs) and dashboards provide real-time insights into how well the organization is performing and where adjustments may be needed.

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Arguably, more than ever before, digital will be more important than ever as we await the result. Why? Social media, of course – with diverse views, but generally an echo chamber effect. Targeted messaging and advertising through different platforms. The role of data in gaining support. The spreading of news – and potentially fake news – online. In 2024, what’s an election without the debate and discussion that happens across digital platforms – whether that’s sharing your opinion on Facebook, following political commentary, or analysing data.

Unless you aren’t online.

If you aren’t online at all, you won’t be hit by the swirling news. In fact, you probably didn’t spend this afternoon receiving news alerts or debating with your team what the date would be, and arguably, you might not even know the election has been announced. Similarly, if you’ve run out of data or had to switch off your broadband due to rising costs.

And not only will you not know, but you won’t be sharing your views. With so many campaigns kicking off online these days, if you are digitally excluded, ironically, you’re not going to be able to spread the word about the need to tackle digital poverty. If you are one of the 11 million people who lack essential, basic digital skills, you may also be at greater risk of falling foul of related scams, of fake news – or purely struggling to register for new voter ID.

So, having established that digital plays a massive part in democracy these days, and given we already know that up to 19 million adults are in digital poverty, perhaps this tells us that tackling this exclusion – whether it’s through affordability, skills, or lack of trust or support – must be critical in the next governmental term, which will see us through all the way to 2029. So much has changed in the last five years, who can tell where we will be in the last year of the decade

This is why we have a simple manifesto call to all campaigning parties:

1. 1. Commit to creating a cross-departmental digital inclusion task force – across every department – with transparent accountability for real change and a joined-up focus.
2. Take immediate action now, with meaningful communication campaigns that signpost support.

Why every department? Because digital touches every area of life. Education, health, welfare, jobs, rural services, housing – the list doesn’t end.

And why communications? Around two-thirds of people would improve their digital skills if there was free support. There is free support – but people can’t find it. Perhaps because they aren’t online, or perhaps because we want them to look in the places that society feels would be convenient, rather than taking the message to people.

How can I get involved?

Are you a candidate? Tell us if you support us. You can email me directly at elizabeth@digitalpovertyalliance.org. Tell your party you support action on digital poverty and want to see this included in campaign literature. If you need information about your constituency, we can share that information with you. Want to know more about how to support?

As well as the asks above, we are seeking greater awareness, support for affordability, increased accessibility of services, a dedicated skills programme and device access in schools, and greater support for local authorities to tackle digital inclusion. If you want to know more, drop me an email.

Are you working with a candidate? Tell them what you’ve just read. Ask them to get involved.

Are you a member of the public? Canvassing season is just beginning. Ask something different on the doorstep – ask about digital poverty. Ask both what the party is doing, but also what the local candidate is doing – whether that’s for the one in five children nationally in digital poverty, or the one in two families on low incomes who are digitally excluded.

Are you a business, perhaps with public affairs goals that align? Join our Industry Forum and be part of the industry response to helping millions of people access the online world – and your services – conveniently, safely, and effectively.

We sometimes hear digital considered a luxury, that it comes after other forms of poverty. A keyboarded device (laptop or tablet) with a stable internet connection isn’t a luxury. Without it, children can’t complete homework. Parents can’t access job opportunities (90% of which are only advertised online, and need a digital application). People can’t apply for benefits, healthcare, or other support. There’s a poverty premium – from insurance to driving licences, you pay more if you’re offline or struggle with accessing online services.

So, if someone tells you a phone is enough, ask them – could they do GCSE coursework on a phone with a cracked screen? And if they say no, ask them what they are doing to help.

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Establishing trackable KPIs/OKRs at departmental levels

Using dashboards and visualizations to convey data more powerfully

Increasing data literacy org-wide

Leveraging data to set direction and see what is and isn’t working

Creating a culture of accountability via monthly/weekly report outs

Huge and important wins!

But as companies advance along their maturity journey, how they want to win with data often starts to change. They begin eying things like AI/ML adoption and acceleration, progressive KPI-ing (more on that here), eliminated data siloes, data science, automated analysis delivery and… for many…

Predictive analytics. Or using data to shift from asking “what happened?” to “what might happen?”

At the simplest of levels, predictive analytics is about leveraging advanced analytics and modeling techniques to make predictions about the future… future outcomes, trends, customer behavior, macro events, organization performance, etc.

Companies can leverage predictive analytics across so many different domains and focus areas from answering questions about buying behavior to supply chain management to product development to risk management and so on. In fact, it’s become such a pervasive focus and investment of leaders across all domains that the predictive analytics market alone is expected to grow from $12 million in 2022 to $38 million by 2028.

So, what steps can you take today regardless of the functional area you support? Here are a few quick tips:

• Data as Treasure: What role do you want data to play in your day-to-day? Do you want it to be a core team member whose main focus is to “report the news,” or share what happened? Or do you want it to be a treasure? Trustworthy, full of insight, and rife with untapped opportunity worthy of steering your future. The role you want data to play is in fact a choice. Because no mater the functional area you lead, once you decide the significance of data in shaping both your day-to-day and future objectives, you can turn attention to making sure data foundations are in place — storage, quality, governance, literacy, etc. — so that you can then leverage advanced analytics and AI/ML techniques to turn your trusted data into a treasure of possibility. Lean into your partnership with your data/tech peers (or a third-party provider) to ensure you are fortifying the core pillars of your data ecosystem so that you can pave the wayfor data to be a business treasure.
• Think Like a Futurist: According to research from the Institute for the Future, as a human population, most of us naturally struggle to think about the “far future.” In fact, 27% of Americans rarely or never think about their lives five years from now, as compared to 60% who think about the near future — one month from the present — every day. As you reflect on your relationship with the future, consider how this might help or hinder how often you spend time pulling your team, department, or organization to the future. For example, do you spend more time reviewing historical or current data? Predicting no more than a few weeks out? Forecasting months and years out? As you shift towards prioritizing predictive analytics, see if you can also strengthen your personal focus on the future, channeling the mindset and behaviors of Futurists.
• Identify and Aim: Instead of starting big — going from descriptive to predictive team or organization-wide — consider picking a specific use case against which having predictive analytics would greatly help. For example, do you most need insight into customer buying patterns? Pricing models? Risk mitigation? Cash flow? Product stickiness? Once you have your top use case identified, work with your data/tech teams on building specific models and analyses with the data you already have (or pinpoint the data you need to begin tracking) and start deriving insights for that specific opportunity at hand. Leverage this mini use case to establish the proof of concept and generate the momentum needed to then drive greater predictive analytics impact.

Your journey from descriptive to predictive can begin at any time. And it often just starts by asking one simple but powerful question… what might happen in the future with our business?

Originally posted here

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Sometimes it feels like I’m stuck in the past. Too often when faced with a new challenge, my first inclination is not to face forwards with an open mind, but to look backwards to try to extract lessons from previous experiences that help me to describe and understand it. And while relying on what’s happened before can be very helpful in many circumstances, it also brings the real danger of being too blinkered, biased, or backward. I can’t work out if my past experience is my greatest asset, or the main anchor that holds me back.

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After diving into the seemingly ‘open’ datasets across the Scottish transport network, what we found did not embody ‘openness’ at all. Outdated datasets, unstructured information, and missing data were all major obstacles to carrying out effective analysis and problem-solving.

But don’t assume this is a technical problem. These challenges have real-world consequences that impact people’s everyday experiences. 

A notable example is the impact on public transport journeys, particularly to and from rural Scotland, where the lack of connectivity between transport providers leads to significant waiting times.

The challenges we encountered during our research extend beyond technical difficulties– they have a direct impact on democracy at the grassroots level.

Imagine a scenario where community activists want to improve local transport links, advocating for changes in bus/ferry/train schedules and enhanced connectivity between various modes of transportation. How can they substantiate their arguments if the relevant data is inaccessible or unusable?

In the context of Scotland, the consequences of this data usability gap become even more significant. Long waiting times between connections, as we’ve illustrated through our examination of transport schedules, compound the challenges faced by locals. Not only do they directly suffer from poor scheduling, but they are also unable to do anything about it. So, this is not merely a matter of convenience; it’s about fostering a space where citizens can actively participate in discussions that impact their daily lives.

In Scotland, the focus must shift towards making data usable, ensuring that it empowers local communities rather than hindering their efforts. By doing so, we will lay the foundation for a more transparent and accountable democracy, where citizens have access to the information necessary to actively participate in decisions affecting their daily lives.

Let’s move beyond the general rhetoric of open data and focus on making it truly accessible and usable for the citizens it aims to serve. By connecting the dots between open data challenges and their real-world implications, we pave the way for a future where local democracy thrives, powered by transparent, accessible, and usable data.

Originally posted here

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There’s certainly been some overselling going on, and people need to know what the common misconceptions are, and why people are key to GenAI and LLMs producing the results we need. 

“The copilot will sort it”

More and more software vendors are integrating copilots into their data and analytics platforms to turbocharge development, smooth out code migration, and dish out automated insights. This is all great, but these tools do not provide a one-size-fits-all solution and it’s important to remember that. It’s crucial to grasp their limitations before rolling them out to users and understand that you’ll still need developers in the trenches crafting applications.

Copilots can do a range of great and really helpful stuff, one being automatically building reports with built-in insights and anomaly detection. But while features like this are useful, the reality is you can’t just sit back and trust that these reports are flawless. You’ll need to double-check that all the filters and definitions are spot-on to guarantee 100% accuracy. This means every report churned out by copilots will need a human expert’s stamp of approval before it’s rolled out.

“The LLMs can do the maths for us”

These solutions are designed to be like neural networks that mimic the astonishing capabilities of the human brain. But as amazing and as powerful as our brains are, we’re not calculators, and neither are LLMs.

Just like us LLMs are prone to mathematical slip-ups, so banking on them for 100% accuracy is a big gamble. They do shine when it comes to handling basic number crunching on smaller data sets, like your run-of-the-mill sums and averages. But if you’re thinking they can tackle complex mathematical tasks like forecasting or intricate what-if analyses, you’ll be left disappointed.

If we want LLMs to work effectively with our data, it’s wise to have people spoon-feed them pre-summarised and pre-calculated information. That way, we minimise the chances of any mistakes occurring.

“We can move on from data analysts”

LLMs are becoming increasingly popular as a way to provide commentary on our data. This is because they excel at condensing information into neat summaries and even spotlighting interesting points and outliers.

However, they’re only as good as the data we feed them, and the amount of information we can share is relatively modest compared to most databases.

At the same time, if we try to use LLMs to produce commentary that involves its base model, such as its internet knowledge base, we risk creating hallucinations where the platform makes up its own story. 

We also can’t yet rely on them to be experts in our fields. They won’t always have the insider knowledge or grasp all the ins and outs that matter when explaining our data unless a human analyst steps in to fill in the blanks  

In summary, while LLMs can help us tell a story with our data, they’re not a one-stop shop and we’ll still need a trusty data analyst to manage and oversee this process.

“We can get rid of our databases”

Theoretically, we should be able to place an LLM on top of all our data and ask it questions, removing the need to write SQL queries. For those one-off inquiries, this can work well. Need to know the status of order 12345? LLMs should be able to answer that with minimal effort.

Challenges can arise though because these solutions can only process small amounts of data at a time. So while they can handle questions about individual records, throw in a more complex query like “What’s my total order value?” and they can then hit roadblocks. They would need to scan through loads of records to find that answer, which would exceed the LLM’s input limits, meaning we would get a result, but it would probably be incorrect.

They would also not know how to calculate and apply precise business definitions that often exist within data models or be able to process complex relationships.   

“Generative AI can generate our charts”

GenAI can create sample code, but this doesn’t mean we can use it to accurately generate code to produce a chart. These platforms would only be able to give us some sample code which we can’t guarantee will work.

It’s the same with images. GenAI can create imaginative images like a cat driving a car, but can’t generate something specific like a bar chart with 15 different bars and labels. For example, if you asked a GenAI platform to produce an image containing a company’s logo, it would generate something that looks similar to that organisation’s branding, but it wouldn’t be a perfect copy. 

When it comes to data and analytics, these tools are text wizards, not miracle workers. You’ll still need the human touch to turn their output into something truly useful.

“ LLMs can fix all our data quality issues”

LLMs can help us manage issues around data quality. Take “Eurpe” for instance; they’ll spot that typo and correct it with minimal fuss. 

However, LLMs aren’t well suited to dealing with large volumes and are not subject matter experts who can natively understand all the nuances within your information.

Lastly, these tools are trained on what’s out there in the digital ether, they’re not equipped to handle what doesn’t exist or the unknown. They will struggle to identify incomplete data.

People and technology in harmony

GenAI and LLMs already have many impressive capabilities that can work wonders when used wisely. But it’s important to remember they can’t replace the vital jobs that developers, analysts, databases, and BI tools do. People, with their expertise and insights, remain the unsung heroes ensuring these technologies hit the mark and drive organisational success. It’s a team effort, with humans and AI working hand in hand to unlock the full potential of data and analytics.

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The UK is facing a major cost of living crisis. Many households are struggling with their rent or mortgage payments, forced to tighten discretionary spending and resort to desperate measures like unplugging fridges to cope with rising bills.

Right now, effectively supporting the most vulnerable groups across the country must be a priority.

Enter Citizens Advice, a UK-based, independent, consumer charity that specialises in providing advice and assistance to people with legal, debt, housing, and similar problems. Their ground-breaking “Closing the Gap” report outlined their vision for improving consumer support in essential services.

The report shed light on the inadequacy of the current approach to data sharing among essential service providers like energy, water, telecoms, and financial companies. It stressed that the customer journey of accessing support is way too complex and disjointed, resulting in people missing out on the support they are entitled to. Additionally, the report outlined what a better system could look like, one that bridges the gap between the support people need and the support they receive.

Early last year, our team conducted Innovate UK-funded research, investigating the value and opportunities of effectively working with permissions data that describes individuals’ preferences for the use of their personal information.

To continue exploring the subject with industry peers, we held a co-creation session in collaboration with the Open Data Institute (ODI). At the workshop, the topic of supporting vulnerable groups emerged as a priority and sparked a passionate discussion about the ways in which open and therefore trustworthy data ecosystems could facilitate ethical data sharing and improve the current system.

Figure 2: Suggested roadmap for an Essential Services Industry Taskforce to establish an ethical data ecosystem to create simple and controllable support services for consumers in vulnerable circumstances.

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2024: The World continues its transition towards more resilient, sustainable growth following three years of healthcare threat, which in part have been a catalyst for economic and societal, turbulence.

Our own experience at Informed during this challenging time has seen us in the privileged position of driving and supporting digital change and developing data-led resilience and sustainability services for nationally and internationally-significant public safety, civil defence, and healthcare programmes in support of The Home Office, The National Police Coordination Centre (NPoCC), The NHS Patient Safety Team and most recently the UK Health Security Agency (UKHSA), where we are helping the organisation mature its digital operating model in the aftermath of the Covid pandemic.

Although each of these engagements have different objectives, they state the importance of safe and effective data sharing between agencies for effective collaboration as a common, critical theme. Whether that be capturing and sharing patient incident data across the NHS estate for patient safety improvement, or in capturing data on every fire-related incident into a national reporting system that helps fire and rescue services understand the leading causes of fires in order to reduce fire related casualties.

Our work with the National Police Coordination Centre has seen the advancement of Intelligence-led policing through the development of a cloud-based data integration and digital mutual aid platform that integrates nine regions and 46 police forces, providing superior operational preparedness and response for national scale operations. 

And with the UKHSA, the body responsible for protecting our communities from the impact of infectious diseases, chemical, biological, radiological, and nuclear incidents, and other health threats, our team is helping the agency’s technology arm design, develop, deliver, and support the technology required to prevent, detect, analyse, monitor, and respond to public health threats across the UK. 

Through our work with the teams responsible for critical national infrastructure, we have seen first-hand a very real requirement for continued, smart investments in data sharing capability and capacity that enables and empowers resilient inter-agency network collaboration.

To realise an effective, seamless inter-service collaboration vision, agencies have to provide the integration, collaboration, communication, and information sharing needed to serve both operational staff delivering a service and the needs of citizens and patients these services  must meet. This ability to share and collaborate will enable effective service and safety for citizens wherever and whenever they need it most, and where challenge and need can be most effectively met. This will rely on more of a whole system approach to inter-agency collaboration based on effective data and insights sharing.

While some agencies are trailblazers in the use of data, digital tools and technology, others may lag in their journey of due to the challenges of legacy data estates, leaving data preparedness and readiness the next challenge to address ahead of digital service integration and collaboration.

The quality of data and the ability to share will underpin a more effective (and cost effective) transition to intelligence-led policing, emergency services and healthcare, and each service’s ability to collaborate. The key will lie in the ability for each agency to exchange and interrogate high-quality data, using information transferred within networks in order to make the best, most effective, and most timely decisions about what care, communication, or support is delivered, as well as where and how.

With existing levels of fragmentation and divergence across the NHS and Justice estates, interoperability remains a fundamental building block for new digitally enabled shared services. An effective and interoperable data sharing ecosystem will provide an infrastructure that uses technical standards, policies, and protocols to enable seamless and highly secure capture, discovery, exchange, and utilisation of information, with appropriate controls that ensure proper and effective use. Alongside this, the reshaping of legacy systems with platforms that communicate with, and work better together, can be used to more effectively access and share data.

This ecosystem must also better meet the challenge of sharing unstructured data by employing Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) techniques to discover and provide relevant information at the point of interrogation and use.  This approach offers significant potential for data sharing, as it allows more meaning and intelligence to be extracted from legacy and unstructured data, and can also support better assessment of data quality and confidence levels in real time, by for example attempting to correlate between different data sets.

In meeting this challenge, data scientists at Informed have worked over the past two years to develop InformedDECISION© – an innovative, first of its kind AI-based decision support platform that enables decision makers to categorise and extract meaning from large, distributed, and unstructured data sources in real time to support and enhance complex decision-making. Importantly, the platform’s ability to unify and extract value from existing unstructured data sources without the need to upgrade systems or take copies of the data could provide a short cut to immediate efficiency and collaboration gains for inter-agency collaboration, whilst significant legacy and data quality challenges are addressed.  The platform is already helping Medical and Environmental decision makers integrate and make sense of large, distributed datasets in real time, employing automated learning to better understand and qualify external datasets as they are used. 

This platform can integrate intelligent workflows that adapt to users’ needs and support complex requests and transactions, with multi-agency case handling delivering seamless and secure case transaction management across multiple organisations through API bridges that efficiently integrate and harmonise data.

As a two-time Queen’s Award for Innovation winner for our ability to accelerate and de-risk digital business change, we understand that the levers that help overcome the challenges of inter-agency collaboration through data sharing across estates lie in; the potential for AI to improve integration of unstructured data; shared and open technical standards that facilitate effective data interoperability; and ultimately investment in quality, decision grade data to support current and future needs.

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I needed cheering up. With so many dark and disturbing stories in the news these days, I decided that I should escape for a while and remind myself of happier times by spending a few hours on the sofa under a duvet re-reading Douglas Adam’s wonderful series of “Hitchhiker’s Guide to the Galaxy” books. Taking me away from today’s pressing problems and into another world where anything and everything is possible.

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