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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 http://www.gartner.com/newsroom/id/3412017

Gartner Hype Cycle 2016 - from http://www.gartner.com/newsroom/id/3412017

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.

Context

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.  

Brokerage

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 info@simplexity.org.uk!

 

 

 

 

 

 

 

 

 

 

 

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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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