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Context brokering


Context brokering - understand the hype.

If you haven’t heard the term ‘context brokering’ - you will do soon.  The 2016 Gartner ‘Hype cycle’ highlighted it as a smart machine technology that will begin to mature over the next 10 years.  

Gartner Hype Cycle 2016 - from

Gartner Hype Cycle 2016 - from

To understand this development a little more it’s probably worth defining what we think ‘context brokering’ could mean and its different levels of application, as at present it seems to covers a range of different problems and timescales, which can cause confusion.  So, let’s look at the first part.


At the time of writing (19/07/2017) - if you google ‘context’ you get the following:

In life (and business generally) we regularly make plans to deliver a particular outcome or to get to a particular point.  To form such plans, we generally need some kind of understanding of what’s going on.  This is where the context part comes it.

Having a picture of the ‘context’ you’re currently facing (to use the google definition - the circumstances that form the setting for an event, statement or idea), or likely to face in the future allows you to both understand what decisions you might need to make and any assumptions that could be implicit in them.  To understand this a little more, have a look at our post from January 2017

Once you understand what context means, you start to appreciate the issues of ‘timeliness’ (when do you want to do something) and 'relevance' (what sort of data is there to help you understand a context, and how regularly is this data produced) - and these two qualities relate to the ‘brokerage’ side of things.  


A ‘broker’ is someone or even a section of computer code that offers a particular service and this is generally to find or provide some kind of data to help you make a better decision.  In the application of context brokering, the broker is (in theory) a type of service that finds and returns data relevant to the particular context required to form a plan or make a decision.  

However, the decisions that people need to make vary greatly, both in terms of complexity and timeliness and this is what’s crucial to understand when considering how context brokering could work.  What’s crucial about ‘context’ in the context of context brokering (I won't do that again, I promise) is that it currently has different meanings - and these generally relate to the timeliness of the data being used.  By exploring what 'context brokering' could mean, and how it could be applied we've deduced that there are two key applications for how context can be useful in the real world:

  1. Immediate prediction of consumer behaviour
  2. Understanding of strategic insights

To understand these two different applications, we've unpacked them a little here.

1. Immediate prediction of consumer behaviour

The immediate predictive benefit of context brokering is probably in the form of brand and consumer insight generation.  With advances in big data, many organisations generate and/or have access to a large amount of data across a wide range of sources.  By managing and aggregating all these different data sources organisations can start to generate particular contexts, perhaps for how a brand is performing or how consumer spending happens.  For example, an online sales platform could use different cookie data to track what associated web-pages a shopper has viewed before and around a purchase.  This could yield interesting predictive data, for example, do people look for certain products during certain climatic conditions for example, in a heat wave - do sales of hats and fans go up? Or would a person purchasing sun cream, mosquito repellent and beach towels, be interested in new sunglasses?  As such, does such an understanding of unique contexts derive a commercial advantage?

To fully get value from immediate context brokering, there are a number of research questions to consider.  For example, how does an organisation bring together and model data?  For a large organisation with an established infrastructure, this isn’t likely to be an issue - the sources and data collection mechanisms already exist - it’s mostly a question of making sense of the data and being able to translate it into some form of action or insight.  It is perhaps a bigger challenge for an organisation trying to understand the utility and applicability of this type of service to their business, for example, if you don’t have access (or the need) for big data and you’re not generating masses of data, what value will it have to your business?

Additionally, if context brokering is likely to add value to your business, how wide ranging are you data sources?  How accurate a context can you produce just using data from facebook? Can you get a more accurate context from looking across as wide a variety of social media channels as possible?  If so, what are the cost implications for drawing data from such a wide variety of sources?

Another question to consider is what technologies exist to make context brokering a reality? This is currently a hot area.  Predictive techniques are improving as people use more sophisticated statistical models.  Machine learning can be applied to train algorithms to detect patterns and find specific terms in larger and larger datasets, ditto for machine vision algorithms and visual data.  At the same time, databasing and data storage continues to rapidly increase as does processing power.  All these technology trends make the immediate predictive benefits of context brokering increasingly enticing.  However, it’s still worth reflecting on the fact that however pure the model and the maths, at some point, context has to equate to action for it be of value and this is something that can often be forgotten.  Essentially, the final challenge is making sure the right kind of predictions are linked to the right kind of behaviours!

2. Context brokering for strategic insights

Another application for context brokering relates to less immediate decision making and relates more to research and development.  Timeliness is not such an issue in this application - its the scale and breadth of data covered to inform a decision that’s important here.  At present, it’s the kind of activity that kicks off many large projects, especially in research, policy and academia. The common element to immediate prediction is that, such projects are undertaken to determine what we believe the state ‘truth’ is around a particular issue/idea or event.  

This form of 'strategic analysis' is less time pressured than immediate prediction, but the sources and ranges of the data used to inform our actions, plans and decisions are still important.  In the past, organisations have generally done some kind of early activity like a literature review, or assigned an intern or student to summarise the research around a particular issue.  From this, an assessment is produced on why we want to do something or follow a particular course of action. Context is really important here, often we base our first principles for a course of action on our belief around a certain event, paradoxically, this often the point where we do the least amount of research and it can be subject to a high degree of bias and poor research.  For example, if too small a starting dataset is used our assumptions and lack of research can be quickly exposed as the work is shared more widely.  This is where context brokering can offer a decent alternative to such traditional techniques (which we see today in the form of literature searching and workshops).

Using context brokering for strategic insights improves how we gather, store and map knowledge; enabling us to have greater confidence in our initial assumptions or understanding of complex problems.  As a technique it can also allow us to produce ‘ontologies’ for particular problems that can allow more specialist data gathering and improved research gathering and network understanding and this can allow us to learn and gather more data more efficiently.

However, as with immediate prediction, there are challenges.  Selection bias can have a large impact for such a technique, especially if a small data set is used and if particular terms favoured knowingly or unknowingly, the process can simply yield more things to confirm a particular view of the world.  Additionally, the issue of perfection versus relevance still applies greatly - however good our model is, it still needs to be communicated with decision makers who need to be able to understand and interact quickly with the main findings of the model, while at the same time trust that the model relates to ‘real’, trusted data.

Additionally, strategic insight generation is probably based on a more limited format of data. Where there are considerable conversion issues for immediate prediction, strategic context brokering tends to rely on text-based analytics (this could reflect the longer lead time in data used for research and development planning?)  This means it is, in some ways, a simpler area of study, one that can benefit greatly by further research of the applications of machine learning for speeding up how the data can be processed and used.  However, its still worth coming back to the potential bias a human can apply in such analysis - but does the intelligence and insight that such human input provides outweigh the downsides? This is a key issue for further research - one that data scientists and analysts continually grapple with. How do you configure the optimum balance of machine-based learning to improve the efficiency and scale of human analysis?  What role does the human analyst have in the analysis process, when at least for the next 20 years, they are likely to remain the best predictor of context and its translation into specific insights, actions and implications?

With all these points on board, and to offer some kind of conclusion to this post, it’s probably worth defining and thinking about what 'context brokering' could mean in the future as we start to understand its applications a little more.

An updated definition of context brokering

Context brokering is a service that enables actions and insights to be generated from broad sources of data and information.  It can be applied with different levels of timeliness - from the immediate to the strategic.  Immediate context brokering as a service applies advanced forms of computer science to provide actions and insights either to another system or a human.  Strategic context brokering, applies the same principles to wide ranging problems that have considerable published literature (often from a scientific or research basis) to map and better inform decisions and insights to be formed around the dataset.  

Additionally, another thing to reflect on is how context brokering works as a process - which whatever the timeliness of the data, tends to rely on the following process.  

  1. Definition of problem and sources

  2. Data gathering

  3. Data storage

  4. Mapping

  5. Action/insight generation

  6. Feedback to 1 (as required)

Final thoughts

'Context brokering' is a newly emerging area and it’s exciting to be in it.  Our own insights have come from the smaller scale applications of strategic context brokering, but what’s interesting is how applicable many of the techniques are to different sources and timescales. However, it may still be worth reflecting that certain principles for analysis still hold true, and are perhaps more important than ever when applied to the era of 'big data'.  As well as issue of timeliness and relevance, trust is still key.  How much do you value and rely on your sources? It is your sources that will ultimately still drive and sustain the validity and quality of whatever context you produce.

What do you think?  If you have any thoughts you’d like to share on context brokering, please either add them here or drop us a line at!














Using context brokering to map the strategic consultancy industry

There are many applications and levels that context brokering can be applied.  To provide a basic example, we’ve applied a simple network analysis to map and understand the market for strategic consultancy services (at this stage we’ve focused the mapping on the UK, but a large number of secondary, global sources have been found in the search).

To do this we designed an analysis process that took openly available data on strategic consultancies based in the UK.  Using a starting source of around 26 strategic consultancies, which we took from  We implemented a series of crawlers that would return organisations and sources connected to these source organisation websites and produced the following maps to summarise the data we found.  At this stage, we've used a fairly limited sample - we haven’t taken the searches further to specific organisations or government departments who clearly also care about strategy.  The purpose of these maps are to show the utility of mapping network data(particularly for subject matter experts) in the early stages of a plan or strategy formation.

Map 1 - A network diagram illustrating consultancies and businesses related to 'strategy'.  'Primary' relates to source web sites, 'Secondary' relates to further web sources uncovered in the crawls.

Map 1 - A network diagram illustrating consultancies and businesses related to 'strategy'.  'Primary' relates to source web sites, 'Secondary' relates to further web sources uncovered in the crawls.

In addition to the sources for strategic consultancy, we were also able to harvest email addresses for different contacts in the organisations.  Map 2 below, shows how the organisations broke down into specific email contacts.

Map 2 - Bubble map indicating which source organisations provided email contact details, gathered through crawls.

Map 2 - Bubble map indicating which source organisations provided email contact details, gathered through crawls.

How does this context add value?

Such a network analysis of open data allows us to produce a context for who cares about strategy and potentially highlights who could be interested in context brokering as a service. We’ve aimed this study specifically at the UK strategic industry to illustrate how context brokering can be applied to a sector that prizes strategic insights and one that also produces a wide range of rich data on strategy.  By mapping out who we believe the ‘players’ are - we have a good start point to work from.  We can add more sources and contact details as we find them, but also use these starting sources to gather further data and literature for further contextual analysis, such as topic modelling.

See what you think and if you have any questions about our dataset or analysis, get in touch at!



Could the Silver Economy promote healthier, more sustainable ageing? A case study using data from the Netherlands.

Around the world, people are ageing.  The phenomenon is more marked in the developed world.  In Europe and focusing on The Netherlands specifically, the average age of the population has gone from 71 in 1960, to around 81 in 2014.  This trend has been seen in many countries around the world and has led to an increasing number of policy decisions on how to better support and utilise this increasingly significant population.  For example, ‘The silver economy’ as a concept is more positive than traditional concepts of ageing, that tend to focus on the simple provision of care and support to the elderly community.  The silver economy as a concept, focuses on how economically significant the +65 age group can be.  Could the significance of 'silvers' in the future lead to a change in how we treat and support people as they age?  Could policies and attitudes to ageing become more nuanced and lead to increasingly diverse ways in which this group can contribute and support local and national economies?

To help understand the Silver economy better and the opportunities and challenges it could present in the future, we analysed some of the current research around the concept - specifically in the Netherlands.  This gave us a clearer idea of what trends and insights currently surround the silver economy and ageing in general.  The analysis also allowed us to produce a ‘topic map’ that summarised these trends as well as low frequency ‘outlier’ trends and insights of general interest to the silver economy and ageing (for the method on how this was conducted please see - The silver economy in Holland a data driven Horizon scan).

As the topic map above illustrates, despite the positive opportunity the Silver Economy represents, the data gathered in this analysis suggests that a lot of the current data and research mostly focuses on the current constraints, costs and concerns around ageing in general.  To understand this further, we’ve broken each theme down into specific narratives based on the data collected (using the most frequently occurring keyword themes as a means of prioritising them).

  1. Care

Society will see continued demand for care for ageing populations.  With an ageing population there will be considerable demand around how and where care is provided.  What constitutes ‘care’ can be quite varied for an ageing society, social support and welfare provision will continue to be important for the ‘early aged’ (the silvers in the 65-75 age range) but becoming more chronic and concerned with the provision of long-term-care and geriatric medicine for the ‘older aged’ (+75 years).  

There will continue to be considerable speculation around how care is provided to ageing communities.  In addition to the type of care, there is considerable discussion around different processes of care delivery.  For example, across Europe there are very different models for how and where care for ageing people is delivered.  In many countries, there are models of ‘familism’ in which individuals provide direct care for their ageing parents and relatives by often having them live in their own homes together (as is the case in countries like Spain and Italy). Other countries like the UK and Holland, tend to base care on state-based models, with ageing individuals more likely to have to fund (with or without state-support) their own care requirements, which are provided by state, or state-subsidised care workers.  Across the developed world there are many different variations mostly between these two sources of funding for care - a continuum of care between the individual and the state.

Deductions for the silver economy.  Ageing is a complex process.  As a person ages their care needs will change and diversify as people go from ‘younger’ old age, to advanced ages.  At present, many nation states, including Holland, use well established social care and pension models to address these costs.  How resilient are these models for the future?  How could they be improved to reflect the increasing health and longevity of people post retirement age (65+)?  Could the silver economy represent a new employment sector for adults in traditional retirement age?  Could such communities be better incentivised and empowered to organise care systems more efficiently and in more beneficial ways than state-controlled systems that treat all members of the 65+ community with a dated, one-size fits all policy?

2.  Health

Health care needs will continue to diversify for an ageing economy.  As our knowledge of medicine and technical solutions to health care problems become increasingly sophisticated, the health care needs of ageing populations are continuing to diversify.  This trend does increase the health and well-being of the average person, as lifestyles become generally healthier and care continues to improve (as reflected in increasing life expectancies).  This also creates questions around how people can age more healthily; for example, could such a trend enable people to grow old in manners that see all of the many different components of their health addressed? As well as the clinical and functional needs of health, can the increasingly important issues of social care and mental health (especially loneliness and isolation in ageing communities) be more specifically addressed?  Additionally, how will issues such as dementia (projected to continue to dominate health care provision) and other chronic diseases be addressed over time to help promote healthier ageing?

There will be considerable demand to address the health care costs of ageing in the future.  As people continue to live longer lives, the demands to access health care will continue to grow as more treatments are available and people requirement them for longer periods of time. Technology will represent a potential response to address some of these costs, for example, loneliness (a common concern for many ageing communities) can be addressed more rapidly today and in the future using community based initiatives and increasingly accessible ICT technology.  Additionally, smarter, age-friendly homes can improve how people are supervised for care, potentially making support and care provision in later life easier and more cost effective.  However, as scientific and technological knowledge advance, the need and desire for ‘solutions’ to the ‘problems’ of ageing will also increase.  Such demand is more likely to increase the overall cost of ageing, with insurance, individuals and that state often being the main sources of finance to provide them.

Deductions for the silver economy.  In the Netherlands, and many other European countries, health trends will continue to have significant impact on ageing.  In one sense people are likely to be healthier for longer and lead more active, independent lives.  This could lead to significant empowerment of ‘silvers’, who could remain economically significant greater and greater ages and, again, could represent a significant driver for the silver economy.  In rethinking how silvers contribute their both their economic influence, but also greater available time made possible through retirement schemes based around the 65+ age range, could the young older age represent an important sector for care and organisation of elder care (+75 age ranges)?  Such considerations could be important especially for countries like Holland because, as health care demands and access become increasingly diverse and complex, the financial burden imposed on the state to provide current levels of care could be highly significant for the future.

3. Service Provision

Do current services meet the needs of an ageing society?  Within the data there is a general reflection that the requirement to support an increasingly ageing society represents a future challenge on current infrastructure and services.  People are living longer, but social and health care models are not, generally, changing to reflect this.  At the global level, this is seen as a considerable discussion surrounding who should provide care - is it the state, is it the individual or their families?  At the national level (in countries like the Netherlands and the UK) debates often centre on how these services are provided, generally with the state being on one end of a spectrum and private health insurance becoming increasingly significant at the other with family care and volunteer services somewhere between these two options.   In such debates, there are often long-held cultural assumptions that the state or the individual ‘should’ provide care.  Due to the polarity of such beliefs and a lack of clarity of who should be providing care, there can often be significant gaps that older individuals can fall through when questions of ‘who should be providing care’ are not addressed.  In some countries, the state picks up the burden, in others the vulnerable, and the aged who require the most support can sometimes be left with nothing.  Is this the best way?

How could models of service provision change?  Currently, a high level of care in many countries is provided by either cheap, unskilled labour (often fulfilled by migrant workers) or volunteers and family members. Family support as a model of social care could change in the future should traditions around shared generational housing (and the general cost of housing) change, additionally, as family size decreases (a generally accepted trend of development) and general costs of living and housing rise, will future generations be less disposed to the direct provision of family care?  As well as family, a considerable proportion of unskilled care provision is often undertaken by migrant workers.  How does this impact on future service provision, if political isolationism (seen in policies such as Brexit, or current US policies on immigration) means that migrant workers are less supported in a developed country? A significant proportion of the labour required to deliver care services to the silver economy could be reduced.   Additionally, in some countries (especially those with poor national economies) there is currently a considerable shortage of skilled and unskilled paid healthcare providers as they seek better employment opportunities abroad.

Deductions for the silver economy.  A more nuanced awareness of ageing and the benefits initiatives like the silver economy could provide represent a significant opportunity for service provision, for both ageing individuals and the state.  At present, it is often the informal, volunteer and charity sectors that addresses many of the gaps in welfare provision for the ageing society, perhaps reflecting the significant differences in care models from the state and the individual.  Could the silver economy represent a way of organising the informal provision of care for greater benefit to the individuals and local economy?  For example, could the contribution of the newly retired (who often contribute to the volunteer sector for elder care) represent an important demographic for the organisation, management and delivery of many aspects of care to the older aged - especially for social support?

Image from

Image from

4. Ageing

People will continue to age, but perhaps more healthily.  As scientific advances continue to drive longevity and health improvements and as society becomes more educated on healthy behaviours it is likely that people will continue to ‘age well’.  As a result the average age of the population is likely to continue to increase in the developed world and life expectancy is likely to continue to rise.  Male life expectancy is likely to improve, with men living on average, slightly longer, although women are still likely to live longer in the future.  This is mostly driven by changes in behaviour and an increasing awareness of how to stay healthy.  However, as society progresses, the issues of ageing - such as dependency and frailty will become increasingly important to address to keep people fully healthy for as long as possible. Additionally, the psychological impacts of ageing will become as important as the physiological ones, with issues such as loneliness and mental health becoming increasingly important to address.

Deductions for the silver economy - addressing frailty and reducing dependency could be significant ways in which the silver economy could help address some of the current challenges and costs of the ageing process.  Addressing how frailty arises in older people could have a significant impact on the health and quality of later life and potentially reduce the level of unnecessary hospitalisation and institutional care.  This, in turn, can help reduce dependency on the state for the continued provision of care but, more importantly, help improve the quality of life as people move into advanced ages.  Could the resources and skills of the generations that constitute the silver economy enable a fresh look and a new approach for care provision that provides both more sustainable care models but also a healthier ageing process?

5. Pensions

Retirement age is likely to increase in the future.  People are likely to live for longer, as a result it is likely that most countries will need to increase the age of retirement.  How countries do this will see considerable variation, many will increase the age of state pensions and retirement through a gradual process that reflects the gradual increase of average age in the population.  However, change is not likely to occur at a pace that reflect this distribution of economically productive populations and the continued perception that the young are working to pay for the retirement of their elders.  This is a challenging perception, often driven by demographics, for example, in the Netherlands ‘baby boomers’ account for 28% of the national population and middle aged groups (those between 35-44) account for 12%.  As a result, is it likely that less people will be working more to sustain those older than them in progressively longer periods of retirement.  Does this represent a significant argument for more nuanced plans and policies surrounding retirement?  How could this relate to pension schemes in the future?

Deductions for the silver economy - could the silver economy represent a new way of thinking about retirement and pension provision?  How many people currently retired devote a significant proportion of their time and resources to volunteer services to help people older than themselves?  Is it possible that the silver economy could represent a new form of employment for the newly retired and younger generations alike in a combined generational effort to build better economies around the realities of care provision to an ageing society?

6. Data

There are considerable differences in how different countries address ageing.  At present it is clear that there are considerable differences at the state level in how different countries provide pensions and services for ageing populations.  To improve and provide better forms of sustainable care, a comparison of different national systems could illustrate how different models, from volunteer to family care, through to fully state-based care are provided.  Such research could allow a better understanding of how to adapt current policies to better and more economically reflect the needs of increasingly ageing human populations.

What data exists on ‘silvers’?  The notion of the silver economy relies on people living longer and healthier lives and the assumption that many of these people would either want to give up their retirement years to continue to work and/or continue to have significant economic influence?  Is this this case?  How real and influential is the silver demographic?

As a 70 year Billionaire could Donald Trump be the champion of the Silver Economy? Image from

As a 70 year Billionaire could Donald Trump be the champion of the Silver Economy? Image from

Implications for the silver economy - presently the silver economy is an idea.  Its potentially enticing on a lot of levels.  At a basic economic level, silvers represent a significant source of spending power and an increasingly significant market.  In more abstract, policy terms, such economic potential could help address the increasing costs of ageing but also help provide greater employment opportunities for people longer into their lives.  But, is this the case?  Do newly retired people want to continue work, do they want greater employment opportunities or have they not worked enough?  Additionally, how many of this demographic actually do contribute to informal care and the volunteer sector - data on informal care is limited and often hard to collect.  To understand what the silver economy could be and how it could benefit society generally, more data is required to understand, how and if it can be applied.

Final thoughts on the silver economy

The silver economy as a concept seems to present a variety of different opportunities and challenges. Its promise is enticing and could reflect how ‘silvers’ have benefited from more consistent economic conditions that have limited other younger generations.  Could the economic and political influence of silvers change how we think about social care in the future, leading to more nuanced ways of responding to the increasingly complex demands of ageing? 

However, when thinking about the silver economy and how it could help drive more sustainable ageing, it’s worth remembering that a number of assumptions have been made surrounding how and who delivers such care in current systems.  At present, care for ageing populations tends to be delivered through a range of different providers - from informal family care (generally provided by women), the state (often underpinned by migrant workers) and volunteers (often themselves of retirement age).  The silver economy could represent a useful policy initiative to help co-ordinate and better resources such informal and formal systems. However, to avoid such a policy being overly aspirational and out-of-touch with the community it is seeking to support more data is required to understand how such a policy of empowerment could help people ageing.  

A recent example of a similar policy is the ‘Big Society’ that was implemented in the UK without the full research into how and who it could benefit.  This policy was based on the assumption that people would volunteer to fulfil the need for a wide variety of service providers without understanding the scale required to do this, could the silver economy suffer from such similar assumptions?  

As a concept the silver economy is enticing, but what is the appetite amongst the newly retired and how would it be delivered to address and support the current service providers, and most importantly, the elderly (and silvers) alike?

This research was delivered to inform an event in December 2016 organised by Future Consult for the Dutch Rijkswaterstaat that helped understand early warning signals for the silver economy in Holland.



The Silver Economy in Holland - an example of a data driven Horizon scan.

The ‘silver economy’ is a term used to describe how the increasingly healthy and demographically significant +65 population could be of greater economic significance in the future.  Thinking about the silver economy could highlight considerable economic benefits and many governments and businesses are thinking about how to better engage with this increasingly significant demographic.  Working with our associates at Future Consult we did some analysis to help the Dutch Rijkswaterstaat better understand what the implications of an increasingly significant silver economy could be for Holland.

To do this we applied a form of topic modelling and expert mapping, that is sometimes referred to as ‘Context brokering’ today.  This post covers how this analysis was conducted, in a separate blog we’ve detailed the key findings from the analysis and the subsequent discussions it was used to facilitate.

Using context brokering to understand strategic trends.

To understand the silver economy and the benefits it could bring there is a considerable wealth of knowledge available around ageing generally.  Ageing and the silver economy relate to research in the fields of demographics, society, health, the economy and even as far as resources and infrastructure, so they are very complex, multi-disciplinary areas of study.  To conduct any analysis on this subject, we thought it best to reflect such complexity and design a basic data gathering method that bought in data from a wide variety of open sources reflecting the different sources of data.  So we based the way of gathering the data on the following process:

Doing this, we defined an initial search that returned 18 open reports detailing the silver economy, society, ageing and limited the geographical range to Holland, or Europe more generally.  After gathering this data as reports we then applied machine reading techniques to extract the most significant keywords for the combined string set of all the documents, doing so allowed us to sample the most frequently occurring key terms:

This data was then analysed further to look at the interconnections between the key terms and, a further level of analysis was conducted to ‘tag’ further terms and specific trends and ideas with the intention of labelling and discovering any interesting ‘outliers’ or signals for new and novel ideas for trends.

Doing this analysis allowed us to start to resolve the complex issue that the silver economy represents into a series of different topics, from the most discussed topics to the least.  This kind of information analysis (albeit from a small dataset) enabled us to generate a simple, ‘topic map’ to inform and guide further facilitated discussions with representatives from across the Dutch Government, Academia and Industry.  This approach, provided a clear context to start discussions around initial assumptions in real data and provides the earliest start point for evidence-based decision making.  The full dataset for the analysis is available here and the ‘rich picture’ produced using Gephi is available here.  For those wishing to engage with a dynamic data visualisation that illustrates trends and interconnections in the master data set, this is all provided in the gephi, rich-picture visualisation to access this data, please contact the team at  For those interested in the ‘top level’ strategic narrative around the data, please see the image below and the discussion of the specific trends and themes (and overall feedback on the technique) is available at the following blog post - The Silver Economy in Holland.



Context brokering - how do you apply it?

To better understand what context brokering is and how it can be applied in decision making, it’s worth considering the following hypothetical example:

A CEO of a large UK multinational organisation specialising in mobile phones has asked the business development manager what the international strategy for engagement in Africa is.  This happens in a board room and, as often happens, the BD manager knows nothing about Africa because he’s worrying about Brexit and Donald Trump, like everyone else.  The CEO isn’t happy about this, so she asks the BD manager to prepare a full briefing on the strategic options for improving their role and relationship in African Markets.  After this, the BD manager goes away and runs through a few options.

Option 1: Expert Literature Review

There is the ‘tried and tested’ option; commission an expert on Africa to produce a paper that tells them a range of strategic issues.  Once the paper has been delivered (probably at considerable expense that directly relates to the urgency) someone in the BD Managers team will condense them into a powerpoint presentation, perhaps with a detailed report of research that they can reference if challenged.  Is this good, is this bad?  Well, it’s good as it does give you answers that can be put back to the board (arguable in a linear, bulleted powerpoint format).  This traditional approach also suffers from limitations as it depends on the scale and the process through which the data has been assessed (often the biggest value has been given to the analyst who compiled the report and learned the associated knowledge in its production).  Such reports can easily be biased and often based on a small range of reports that are limited to the number that the analyst can comfortably process in the time available to them.  Also, if its based on a small number of people and papers, the assessment is at greater risk of being biased toward particular issues or outcomes.  

Option 2: Produce a context map  

An alternative option to commissioning a single expert is to produce a context map.  At present,  this does represent a significant cultural change to how many organisations currently conduct their strategic planning.  Context brokering works on the principle that the best thing to do early in your planning, is to define and gather as much data as possible and then summarise what you believe the insights and themes around an issue could be with some kind of qualifier for how valid you think the data could be (relating back to the source data to illustrate where the insights came from).

So going back to the considering the future of Africa, using data visualisation and mapping tools a context map (or topic model) can be produced.  Such a map summarises the data behind an issue and provides a start point for strategy making.  This produces a map that is a lot more engaging and derived from a wider range of sources (there is theoretically no upper limit to the number of reports that can be analysed and mapped, although at present our own experiments at Simplexity Analysis are around 1000 documents). Such outputs are less static than bulleted lists and can be used in facilitated sessions with experts who can then interact with the context and add their own insights as required to further enrich our understanding of the context. Have a look at the one below produced to provide a context for future strategic issues surrounding Africa.


Mapping the data around an issue in this way can be daunting.  What was the domain of traditional research and literature reviews is now increasingly contested with data scientists and analysts talking in numbers and code and arguing in shades of technical purity around who’s process for mapping is more accurate (is it a complete reflection what’s in the data) or quantifiable accuracy (if you take qualitative data, is it worse than hard number predictions)? Perhaps this is why its challenging for decision makers to interact with such new techniques as context brokering does represents a cultural change - the best way of addressing this, is probably to be open and honest in the data used to make the assessment, the assumptions behind it and the limitations in the development of the context.  In the past, that’s what the weight of a large volume of research would convey.  Now, it’s probably the scale of the data that has been analysed.

Which option works best?

Concluding again with the Africa example, what’s better - a bulleted powerpoint presentation of ideas, referenced with a weighty research tome (that, lets face it, few people are going to read).  Or a map, summarising a range of options that can be discussed and assessed by the board, or through associated activities that equate to action for the board to sanction and the associated data available for analysts to reference further as required?

For more information detailing differences of approach for mapping and analysis, please see the following presentation that outlines the differences between conventional analysis and data driven approaches.


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Context brokering. What is it and what does it mean for strategy?

Context brokering, is a relatively new term that broadly relates to using data to provide a context around a particular issue (other techniques like topic modelling or ‘concept testing’ are sometimes also used to describe a similar analysis process).  Context is particularly valuable where people, in business or government, need to derive insights and understanding around a particular issue, that can be highly complex and involve a large range of data.  This is where context brokering has a strong link to strategy, which is where someone, generally a leader has to take the data and do something with it.

Forming a strategy, or even a plan, generally requires an understanding of what’s going on.  Having a good understanding of the context allows you to both understand what decisions you might need to make and any assumptions that you could be making.  This really isn’t new.  As an analyst, the first thing everyone tells you to do is start by understanding a particular problem or issue.  To do this we generally start by gathering data.  There are many ways we can do this and the choice of method usually depends on the time and resources at our disposal.  But however we do it, be it from simple google searches through to a detailed literature search, the aim is the same - to gather as much data as we can to inform our understanding of a particular issue or topic.   

So, to form a context we need to first gather data and then decide on our approach on delivering a particular outcome (our strategy).  In the modern, data rich world, this is often quite a challenging thing to do - we now live in a time where it’s not about too little data, but too much.  We continually face questions about how reputable our data is, so understanding how to refine and understand the data is becoming increasingly important.  Traditionally, this used to be limited to how much information the human gathering the data could process.  So, in a way, we’re roughly limited to say around 10 reports of maybe 20 pages a report, perhaps a 100, if you’ve got an inhouse team of people and some analysis processes to help you triage and summarise the increasingly complex research data.  

Today though we are a lot less limited by human processing.  There are many options and dashboard solutions that enable people to gather a lot more data and make sense of what is being said.  Making sense of data is now increasingly important and the range and the scale of the data is increasing.  So in some ways, the data available to be understood is far greater, potentially less biased and not limited to the human processing bottleneck.  But, this creates a new range of issues - how accurate are the processing algorithms applied to them and how and where should the human intervene to select out the most important aspects of the data for context?

And this is the challenge we now face - attempting to balance tools and techniques that allow us to gather and structure more data, whilst providing a useful, accurate and informed context that enables us to make better decisions and form policies and actions.  And this is where context brokering can help, but it can be a complicated process that yields deceptively simple outcomes, so to understand how it’s applied and how it can differ to traditionally applied techniques it’s worth considering an example.  Have a look at this post that explains things a little more.   

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