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Filling in the Empty Space – The Personal Data Store

February 27th, 2010 3 comments

I said here that at present there are very few genuine VRM tools available right for use right now, and that the main reason for that is that the underlying plumbing is not yet in place at any kind of scale.

By ‘plumbing’, I mean that ‘personal data stores’ and all that they imply are not as yet deployed en masse, or with any degree of robust functionality.

Before we get into what it will take to change that, let’s take a look at what I mean by the term ‘personal data store’, because obviously that is open to interpretation, and indeed this has been the subject of much debate in the Project VRM community. To get to the heart of that, I think it is useful to draw a parallel to the deployment of data warehouses within organisations, a process which began some 30 years back, and continues to evolve and extend today. The raison d’etre for a data warehouse within an organisation is normally to pull together the data from multiple operational sources (silo’s), organise that data, enhance it and make it available for use – whether that be for analysis within the warehouse, or via applications that will tap into it. Pulling data in from multiple operational systems is the key, because what is being acknowledged is that no one operational system can pull together a data set that is sufficiently rich, deep and broad to enable all of the functions required to run the organisation. That is to say, we need to distinguish between systems that are there to fulfill a specific task (an operational system such as a CRM application, an ERP instance, or a web site), and those whose main purpose is to generate knowledge, enable understanding and enable sharing information built across multiple business functions.

A further defining characteristic of the data warehouse is that it runs on ‘atomic level’ data, that is to say data that is stored at the lowest level of detail available  from the feeder system (e.g. line item of a receipt). When data is stored in this way, it can be aggregated and summarised where appropriate or necessary for use. This then enables a further defining characteristic of a data warehouse….that one cannot predict in advance all of the uses to which the data might be put which storing at an aggregated level would limit. The same will apply in the personal data store.

So what else is involved in data warehousing that might inform our thinking about personal data stores?

Firstly, i’d suggest there is a (mainly manual) ‘discovery’ phase in both that is about identifying and engaging with valid data sources (i.e. inputs to the store). In practice the data to be sourced is driven by the prioritised functionality sought by the user. For example, if my main purpose for the personal data store is to help me manage my health, clearly i’m going to need my health and my health care supplier data, or links to it, in the store.

Next, we need to consider the personal equivalent of the ETL processes and tools deployed in data warehouses; ETL is short for Extract, Transform and Load. In recognising the likely need for ETL equivalents, we imply that:

a) the personal data store will have its own target data schema (design), with greater of lesser degrees of flexibility built in dependent on technical choices. I think there will necessarily be open standards around personal data store design. That’s not the case in the data warehousing world (Oracle, SAP, Teradata, IBM are all largely proprietary), but I don’t think that approach is sustainable for the personal variant which needs to run at greater scale and much lower cost.

b) most/ all of the data sources will not hold data in precisely the same format/ design as the target data schema.

Extract, Transform and Load usually consists a set up phase, and then automation; many ETL tools exist in the data warehouse world and it is reasonable to assume that the same will emerge in the individual space (indeed they already are tactically with data exchange formats like OFX) in the banking world for moving transaction data around. Note that ETL may only be a precursor to a direct feed from a source system into the warehouse, whether they be batch, trickle or real time feeds.

Now that we have data in the warehouse/ store the task lies in organising the data and preparing it for use; there are a range of technology candidates in this area from standard RDBMS to NoSQL databases. At this point, it may be worth diverting briefly to a harsh reality, because it is pretty certain that this same reality will apply within personal data stores. This reality is that many data warehouses actually become ‘digital dumping grounds’ into which data is put ‘in case we need it later’ (note the clash with data minimisation principles in privacy law), and/ or it is not organised/ optimised for use. That does not make them a complete waste of time necessarily, it just means that they are not providing maximum value; ……the well worn phrase ‘Garbage In, Garbage Out’ springs to mind. My colleague John McKean tells this story much more eloquently in his first book, The Information Masters, which dates back to 1999 but is as valid today as it was back then. His research amongst the 30 or so ‘Information Master’ organisations sets out what differentiates the tiny percentage of organisations that get mega-returns on their information investments, versus those that just plod along or suffer regular failure to get a return on investment (hint….the master’s don’t regard the issue as something that ‘the IT folks do’).

The further functions of the data warehouse/ personal data store beyond getting data in, and organising it are:

– Data maintenance, i.e. refreshing data as appropriate, and having processes to keep it up to date, whether that be static data, dynamic data, or reference data.

– Data enhancement, either through combining existing data via queries into new attributes or meta data, or by bringing in further external data (e.g. my credit rating or verification via a third party that a data attribute is accurate at that point in time, or otherwise). This verification piece is a key issue, if I can prove for example that I am a gold level flyer on British Airways, or that i’ve not had any speeding tickets in the last 5 years, or that I do have a specific illness to manage then that ultimately takes a vast amount of guesswork and waste out of the current modus operandi.

– Make available for use; i.e. providing a data access layer that enables the data to flow onward to those entitled to it, in the way that they wish  to receive it.

– Archive, there comes a time in the life-cycle of a data attribute, that it is no longer useful. This situation, which will certainly apply in a personal data store, can lead the database manager to either physically move the data elsewhere/ onto back up media (usually after building summary histories that do remain), or just leave it within the warehouse on the basis that storage cost may be less than removal costs.

Two other aspects of data warehousing are probably worth noting

– whilst initially, a data warehouse was most likely to be a single computer (perhaps costing £1m upwards to buy and install), these days the concept of a virtual warehouse is also a perfectly viable option, with data stored physically in different places and brought together as and when required.

– the concept of a data mart has emerged, which means the carving off of a specific set of data to support a subsidiary warehouse tuned to particular task (e.g. a retailer may choose to set up a mart for the team managing the loyalty scheme). Typically the link to the main warehouse remains in place for maintenance and update purposes, but the mart acts more independently in terms of access and use.

So what does all of that mean for the ‘personal data store’ then?

Firstly, I would contend that there is a terminology point to be taken on-board. The data warehouse is a short, fairly well understood term (perhaps because it is 30 years old). But it actually covers a lot of ground, and is much more than just a storage facility. It covers ‘identify relevant data types and sources, enable processes for bringing that into the storage facility, keep it clean and up to date by looking back to the source and other other cross-reference files, aggregate and summarise data where appropriate, enhance and add meta data where useful, and make available for use in a controlled, auditable manner via a range of output mechanisms and formats. That’s a lot functionality to pack into two words….. I think that a personal data store will do pretty much all of those same functions, so the users of the term should ideally aligned with that description, or seek to agree different terms for each of the system components and functions.

Secondly, there should be a recognition that functionality will continue to emerge and evolve over time, rather than all turn up in one big bang deployment.That said, there is clearly a huge upside to deploying with the technology we have available now, than that of 30 years ago. Cost of storage and back up is very low, connectivity is solid, access routes/ devices are many and the range of things that will be enabled by them using the internet/ mobile internet as the main place where this user managed information will be deployed.

Third, my working assumption is that there will be both self managed, and hosted options and that people will chose the options that best suit them and their likely uses. It is probable that stand alone personal data stores might not be that common as the market evolves, and indeed the individual buys into a wider set of personal information management capabilities (e.g.  a personal data store, a set of key applications, and a hosting/ back up service).

So, after all that, here’s my working definition of a personal data store:

A personal data store helps me gather, manage, enhance and use information from across multiple aspects of my life, and share that information under my control with other individuals, organisations, or with applications or subsidiary data stores that I wish to enable.

The key, as per above, that this is a multi-life aspect data management platform that is infinitely extensible, and not constrained by the need to operate within a silo-ed context.

Here’s a diagram that seeks to illustrate the personal data store that I think will emerge over time.

One of the big issues around data warehousing is ‘the business case’ for what is typically regarded as a behind the scenes, not very sexy investment. I think the same will apply to the personal data store, but i’ll save that post for another day…

Categories: #Kantara, CRM, Data, Mydex, Project VRM, VPI Tags:

The Customer – Supplier Engagement Framework

January 25th, 2010 Comments off

Over the past few months, The Information Sharing Work Group at The Kantara Initiative has done a bit of a deep dive into an end to end ‘car buying and using’ scenario. We used the diagram below, summarised in this post, to give us context and structure and allow us to break the backwards and forwards interaction between customer and supplier into digestible chunks within an overall framework.

Note: Those studying the model closely will see that i’ve broken out the previous ‘Relationship Servicing’ box into two (Product Delivery/ Service configuration, and Relationship Maintenance); when I was reviewing that area it became clear that there was more detail in there than the one high level heading could support, and that significantly different processes are going on in those areas).


As we have worked with that methodology, it has stood up to the test – so it’s about time it had a name, and a bit of a deeper dive description. So here goes, let me introduce……(drum roll…..), The Customer-Supplier Engagement Framework…..

…So what does that mean then? In essence this work is a built out from many years of work on customer management issues in which colleagues and I used one model in particular (the CMAT model) to describe the big picture of how an organisation manages it’s customers. That model is excellent, has been tried and tested in 600+ organisations worldwide, and has fuelled many a good CRM deployment, but when I think about it with a ‘VRM’ hat on I came to realise that we need not only an equivalent model of what the buyer/ customer on their side of the process, we need a model that shows the inter-relationship between the buying process and the selling process. That’s what The Customer – Supplier Engagement Framework is about; it sets out eleven steps of the combined buying/ selling process and shows the flows within and across the two.

Now clearly we’re not building that framework to show how wonderful the current state is; because it’s not. It’s full of waste and missed opportunities for added value…FROM BOTH PERSPECTIVES.

There are two main reason that this eco-system is full of waste and missed opportunity in my view:

1) One side (the selling side) is fully kitted up with the tools of their trade….data warehouses, web sites, CRM systems, and an army of people paid to do the work. The other (the buying side), has nothing more that some self-assembled, amateur tools….and their brains. They don’t get paid to do it, and they typically don’t have a lot of time set aside for the process (my wife shopping for shoes aside….). That imbalance between the ‘have’s and the have not’s, as in any walk of life, leads the ‘have’s’ to take advantage, and the ‘have not’s’ to rebel against this in whatever way they can, or (more often) they just don’t engage as they might in a more balanced and equitable relationship.

And it’s not as if the individual can just walk away from the imbalanced relationship, in many product/ service categories part of the supplier kit bag is the tactics of ‘lock-in’, and the individual typically does not have the resources or the time to invest in breaking out..

2) Overall supply outweighs overall demand across many categories…so the net position is that there are a lot more suppliers losing out on pitches than there are who are winning the business…., with all that marketing and selling effort being wasted.

So this framework is about enabling us to isolate potential improvement areas without losing the overall context. It’s main impact, I would hope, would be on the buyer side; that’s the area where most time is wasted and where least work has been done to date. But it will also enable much improvement on the seller side, which is where most money is wasted at present.

The build below seeks to illustrate the current situation in terms of the capabilities that sit behind each of the parties. Those on the buyer side have been most actively built out over the last 20 years, beginning with the data warehousing boom of the  late 1980’s, and then through the CRM and e-commerce fuelled growth spurts. The spend on this side is enormous, CRM software applications alone are thought to amount to a $10bn a year market, and that’s only a tiny proportion of the spend overall represented on the diagram. On the seller side, there’s clearly a lot goes on, but in terms of capabilities able to engage on anything like a ‘peer to peer’ relationship with the seller side, then it’s pretty much empty space right now. But the good news is that there are lots of people working away on that. It’s tough, no doubt about that, because just as on the seller side, the base level data management capabilities that span in-house silo’s are key; we’ll call them ‘personal data stores’, although this a generic term for a fairly wide range of personal data gathering, managing, analysing and (optionally) sharing processes and tools. Just as in the data warehousing area on the organisation side, there is no real business case for the personal data store  as a stand alone entity; but when it is tied to the applications that tap into it, then the potential value/ return on personal investment is enormous.

I guess that’s enough complexity for one post, so my plan from here is to take each key area in turn (either capability or improvement opportunity), targeting one per week – and building out that story. I’ll kick that off with a deep dive into the personal data store, the base level plumbing for the buyer side of the framework.

Categories: #Kantara, Project VRM, Uncategorized, VPI Tags:

Sales Process… meet Buying Process; and why context trumps segmentation

August 7th, 2009 Comments off

I’ve been doing some thinking in advance of getting stuck into the development of open standards for User Driven and Volunteered Personal Information. That work is being done here if you are interested in joining in. I’ve been thinking mainly about how best to explain what happens to buying processes and sales processes when volunteered personal information is added to the mix (underpinned by the personal data store/ My Data as set out here).

Here’s my stab at that explanation. I need firstly to set out a view of how things currently work – that’s in the first diagram below with individuals/ high level buying processes on the left, and organisations/ high level selling processes on the right. In short, at present, buyers and sellers largely do their own thing/ practice non-automated selective disclosure prior to engaging in an actual customer/ supplier relationship. That is structurally the best option for a buyer, certainly in terms of reducing complexity and protecting negotiating positions for more expensive/ complex purchases – but it does lead to a lot of guesswork; the buyer typically evaluates multiple options before deciding on one – that’s part of the guesswork referred to in the diagram below. This ‘one step removed’ approach is not the best option for the seller – which is why they try a wide range to tricks to have the potential customer engage with them. That would appear a sensible practice, but in reality it tends to fill up the ‘sales funnel’ with many potential customers who actually have no right nor reason being there – and why direct marketing conversions from prospect campaigns are often well below 1%. That’s the the other part of the guesswork in the diagram. At the relevant point in the process, the customer chooses one of the supply options and decides to commence the customer-supplier relationship; the other suppliers fall by the way side/ wonder what’s happened. But those who lost out, because they don’t have the information to do otherwise, keep on turning the marketing handle – lot’s of waste comes from that area.

Moving through the process, commencing the supply relationship in the current mode means interacting on a supplier run platform, and signing up to supplier generated terms and conditions (or going elsewhere to another supplier silo/ get the same result). What that then does is put the organisation unilaterally in charge of processes and process improvement around relationship management. As a historical note, in my view this is where CRM ‘went wrong’ in the widest sense – at least in part because many deployments occurred during the economic downturn in the early 2000’s. It moved from a having been brought in as a platform for driving improvements in the customer experience, to being run as a platform for cost cutting and for risk managment; e.g. the drive to automated processes such as web based customer self-service, offshoring contact centres. Sometimes this automation worked for customers (e.g. online banking), in many cases all it did was move the waste/ inefficiency onto to the customer. Of course what then happened was that customers took their business elsewhere, where they had that choice/ a better option, or stayed but with reduced levels of satisfaction – crazy in that customer retention and satisfaction improvements were almost certainly key drivers for the original CRM business case.

go to market space

So, the current process does not work that well; the sales process cannot be optimised much further within the current tool-set . But options for improving upon this are now emerging – and not through pedalling faster within the organisation/ the selling process; it comes from building capability on the buyer side/ enhancing the buying process. (note the clear parallel with how selling professionalised in the B2B world when professional procurement and its processes emerged, and also that in the B2B world deals are often concluded and managed on the customer side systems).

The first thing to note in the updated diagram below is what the individual brings to the party (via their personal data store/ user driven and volunteered personal information. They bring the context for all subsequent components of the buying process (and high grade fuel for the selling process if it can be trained to listen rather than shout). By ‘context’, I mean the combination of a wide range attributes that describe the individual and their specific buying situation. This would typically include their needs, their current understanding of how their needs relate to products/ services, their location, their existing supply relationships, their preferences (brand, colour), their role in the decision-making process, their timescales, how much they wish to/ are able to spend, when they wish to buy. In other words, the individual’s context bundle is what much of the early part of the sales process is actually trying to figure out – but can’t get access to as the individual has no current incentive to release it in full. The best an organisation can do at present is strategic segmentation of their market (differentiating products or services based on aggregated customer requirements), and tactical segmentation of their messaging content, communications channels, sales outlets or pricing. Then it’s over into guesswork mode – can we put our messages out in the right places to attract our potential customers and suck them into our sales process…..

The other adds to this second diagram are the ten numbered boxes, reflecting that the improvements we make to the buying process through user driven and volunteered personal information will impact differently at different points of the buying/ selling process. These ten areas are substantive enough to each require a post of their own, so for now i’ll list them out at the high level below the diagram and come back to them in more detail as the standards work unfolds.

context equals segmentation build

User Driven and Volunteered Personal Information Enabled Improvements

  1. Search/ Target (sometimes referred to as the Personal RFI, i.e. Request for Information) – through the individual bringing much richer context data to the table, suppliers prepared to engage with these new buying support tools will find that their targeting becomes much more precise, better enabling them to find potential customers whose needs closely match the unique selling propositions in the organisations product/ service offering. In turn, individuals will find that the options made available to them have been pre-qualified to fit their context (to whatever level of detail they have shared). Note – at this stage my assumption is that individuals will be engaging anonymously/ pseudonymously as there should be no need to share personal data in this part of the process. It is likely that new inter/ infomediaries will emerge in this space, acting as the individuals buying agent (4th party/ user driven services).
  2. Find (engage)/ Enquiry Management (sometimes referred to as the Personal RFP, i.e. Request for Proposals)  – through having brought richer data to the table in the preceding phase the individual will now be talking to pre-qualified suppliers (and vice versa), with the qualifying data from both parties available for use in the interaction. Typically this interaction will be about having a more refined/ detailed discussion about a need/ requirement/ solution axis – potentially involving either or both parties asking for more detail, including possibly verification of data asserted in the search/ target phase. It is likely that new inter/ infomediaries will also emerge in this space, quite possibly spanning the Search and Find requirement for individuals and done from the perspective of enabling the individual to buy solutions to their needs rather than the components which they subsequently stitch together themselves.
  3. Negotiate – In this stage the individual is talking to one preferred solution option and getting down to the actual proposed ‘deal’ and the terms and conditions around that – provided by either party. Improvements in this area are likely around improved transparency of terms and conditions, initiated by the individual being much clearer about their requirements, and having access to comparison tools earlier in the process. ‘Reputation’ management tools will also come into operation as the individual shares what they find out about suppliers.
  4. Transact – I would expect payment intermediaries/ financial services providers to find creative ways to engage with/ be driven by VPI enabled services; there is certainly much potential for reduction in credit card fraud and card related identity theft from using the much higher levels of identity assurance that will become the norm in a VPI enabled data-set.
  5. Welcome – This ‘relationship set up’ phase is typically about both parties getting to know each other, i.e. getting products/ services bought set up and configured, ensuring any ongoing account management/ billing is up and running smoothly. In the VPI enabled world this phase won’t change too much in the short term as it will still run mainly on supplier systems – but in the mid and long term i’d expect it to shift to a genuine user-centric architecture which will see the individual ‘welcome’ the new supplying organisation to their personal supply network/ federation.
  6. Relationship Servicing – This is what would typically be called customer service, i.e. fixing basic operational/ service delivery problems and dealing with ad hoc issues that come up such as change of address/ change of contact details/ change of payment details. As VPI enabled tools increasingly emerge, i’d expect this whole ‘change of’ to migrate to the ‘my suppliers follow me’ approach rather than the individual have to run around updating silos as per the current model.
  7. Relationship Development – This typically includes the ‘cross-sell/ up-sell’ much beloved in the CRM business case. This stage will change in the VPI enabled world, much for the better. Customer service will be provided within the context of the individuals existing solution set rather than that little snapshot of it that the supplier currently sees/ is interested in. In turn that will mean that cross-sell and up-sell will be not only be much more informed, but it will also be much more welcome from the individuals perspective – because it is now laser sharp, and running within a more equitable customer/ supplier relationship (partnership).
  8. Manage Problems – This stage is only reached if a significant problem emerges in the customer/ supplier relationship; typically this involves escalation beyond tier 1 customer service (and an increasingly frustrated/ angry/ upset customer). I don’t expect the VPI approach to have a high impact in this area, although improvements further up the process might have a knock on effect rendering this stage less painful if/ when it occurs.
  9. Manage Exits – Exits can and will happen, either permanently or for a period of time. They may be caused by significant problems that emerged, or by a change in the customer need, or in their circumstances (their context has changed). Less frequently, a supplier will wish to leave a market or terminate a product/ service line and thus exit those relationships affected. In the VPI world, i’d expect there to be more information around impending exits and reasons for them – some of which will enable creative supplier responses. Along with relationship development, i’d expect improved customer retention to be one of the major wins for the supply side in the VPI world – but the plumbing and mechanics for that have stilled to be worked out.
  10. Re-engagement – This stage might be known as ‘win-back’ in CRM speak, and involves the lost customer being targeted with appropriate offers to return. For the individual this return to the fold might be as a result in a time-driven change of context, or that the ‘grass was not greener on the other side’ – as is often the case in utility service swaps away from an incumbent that has retained quasi-monopoly advantages. In any case, the point being made here is that in the volunteered personal information scenario, the individual would be in position to retain and share the knowledge of the prior relationship – which many current CRM architectures fail to deliver on.

So there we have it. Time to get back to working on that VPI plumbing!!!

Categories: #Kantara, CRM, Data, VPI Tags:

The Personal Data Eco-System

June 20th, 2009 2 comments

This post is a short(ish) summary of a working session led by Drummond Reed and me at the recent West Coast VRM Workshop, and also an introduction to the Kantara workgroup in which we are going to move this debate forward. It is also part of the thinking that will short emerge in a Mydex white paper.

At the VRM workshop, we discussed the need for the concept of the Personal Data Store, what it would do in practice, and what that will ultimately enable.

Why we need such things – because individuals have a complex need to manage personal information over a lifetime, and the tools they have at their disposal today to do so are inadequate. Existing tools include the brain (which is good but does not have enough RAM, onboard storage, or an ethernet socket……thankfully), stand alone data stores (paper, spreadsheets, phones, which are good but not connected in secure ways that enable user-driven data aggregation and sharing), and supplier based data stores (which can be tactically good but are run under the supplier provided terms and conditions). NB Our current perception of ‘personal data stores’ is shaped by the good ones that are out their (e.g. my online bank, my online health vault); what we need is all of that functionality, and more – but working FOR ME.

What they will do/ enable – the term Personal Data Store is not an ideal term to describe a complex set of functions, but it is what it is until we get a better one (the analogy I’d use in more ways than one is the term ‘data warehouse’ – again a simplistic term that masks a lot of complex activity). A Personal Data Store can take two basic forms:

Operational Data Stores – that get things done, and only need store sufficient breadth and depth of data to fulfill the operation they are built for (e.g. pay a credit card bill, book a doctor’s appointment, order my groceries).

Analytical Data Stores – that underpin and enable decision making, and which typically need a more tightly defined, but much deeper data-set that includes data from a range of aspects of life rather than just that from one specific operation (e.g. plan a home move, buy a car, organise an overseas trip).

A sub-set of the individual’s overall data requirement will lie in both of the above, this being the data that then integrates decision-making and doing.

In both cases, the functionality required is to source, gather, manage, enhance and selectively disclose data (to presentation layers, interfaces or applications).

We also discussed ‘who has what data on you’ and introduced the following diagrams to explain current state and target state (post deployment of Volunteered Personal Information (VPI) tech and standards).

The key terms that require explanation are:

My Data – is the data that is undeniably within, and only within, the  domain of an individual. It’s defining characteristic is that it has demonstrably not been made available to any other party under a signed, binding agreement. This space has been increasingly encroached upon by technology and organisations in recent history (e.g. behavioural tracking tools like Phorm) and this encroachment will continue. Indeed a general comment can be made that ‘my data’ equates to privacy in the context of personal data; so the rise of the surveillance society and state is a direct assault on ‘My Data’. Management of ‘My Data’ can be run by the individual themselves, or outsourced to a ‘fourth party service’.

Your Data – is the data that is undeniably within the domain of an organisation; either private, public or third sector. Proxy views of this data may exist elsewhere but are only that. This data would include, for example, the organisations own master records of their product/ service range, their pricing, their costs, their sales outlets and channels. Customer-facing views of much of Your Data is made available for reproduction in the ‘Our Data’ intersect.

Our Data – is the data that is jointly accessible to both buyer and seller/ service provider, and also potentially to any other parties to an interaction, transaction or relationship. It is the data that is generated through engaging in interactions and transactions in and around a customer/ supplier relationship. Despite being ‘our’ data, it is probably technically owned, or at least provided under terms of service designed by the seller/ service provider; in practical terms this also means that the seller/ service provider dictates the formats in which this data exists/ is made available.

Their Data – is the data built/ owned/ sold by third party data aggregators, e.g. credit bureaux, marketing data providers in all their forms. It’s defining characteristic is that it is only available/ accessible by buying/ licensing it from the owner.

Everybody’s Data – is the public domain data, typically developed/ run by large, public sector(ish) entities including local government (electoral roll), Post Offices (postal address files), mapping bureau (GIS). Typically this data is accessible under contract, but the barriers to accessing these contracts are set low – although often not low enough that an individual can engage with them easily.

The Basic Identifier Set/ Bit in the Middle – this is the core personal identity data which, like it or not, exists largely in the public domain – most typically (but not exclusively) as a result of electoral rolls being made available publicly, and specifically to service providers who wish to build things from them. This characteristic is that which enables the whole personal eco-system and its impact on data privacy to exist, with the individual as the un-knowing ‘point of integration’ for data about them.

Propeller Current State

The ovals in the venn diagram represent the static state, i.e. where does data live at a point in time. The flow arrows show where data flows to and from in this eco-system; I use red to signify data flowing under terms and conditions NOT controlled by the individual data subject.

Flow 1 (My Data to Your Data, and My Data to Our Data) – Individuals provide data to organisations under terms and conditions set by the organisation, the individual being offered a ‘take it or leave it’ set of options. Some granularity is often offered around choices for onward data sharing and use, i.e. the ‘tick boxes’ we all know and which are one of the main bitsof legacy CRM that VRM will fix.

Flow 2 (Your Data to Your Data, including Our Data) – Organisations share data with other organisations, usually through a back-channel, i.e. the details of the sharing relationship are typically not known to the data subject.

Flow 3 (Your Data, including Our Data to Their Data) – Organisations share data with a specific type of other organisation, data aggregators, under terms and conditions that enable onward sale. Typically the sharer is paid for this data/ has a stake in the re-sale value.

Flow 4 (Everybody’s Data to Their Data) – Data Aggregators use public domain data sources to initiate and extend their commercial data assets.

The target state is shown below, a different scenario altogether – and one which I believe will unfold incrementally over the next ten years or so…..data attribute by data attribute, customer/ supplier management process by customer/ supplier management process, industry sector by industry sector. In this scenario, the individual and ‘My Data’ becomes the dominant source of many valuable data types (e.g. buying intentions, verified changes of circumstance), and in doing so eliminates vast amounts of guesswork and waste from existing customer/ citizen managment processes.

The key new capabilities required to enable this to happen are those being worked on in the User Driven and Volunteered Personal Information work groups at Kantara (one tech group, one policy/ commerce one), and elsewhere within and around Project VRM. The new capabilities will consist of:

– personal data store(s), both operational and analytical

– data and technical standards around the sharing of volunteered personal information

– volunteered personal information sharing agreements (i.e. contracts driven by the individual perspective, creative commons-like icons for VPI sharing scenarios)

– audit and compliance mechanics

Around those capabilities, we will need to build a compelling story that clearly articulates, in a shared lexicon (thanks to Craig Burton for reminding us of the importance of this – watch this space), the benefits of the approach – for both individuals and organisations.

The target state that will emerge once these capabilities begin to impact will include the 4 additional individual-driven information flows over and above the current ones. The defining characteristic of these new flows is that the can only be initiated by the data subject themselves, and most will only occur when the receiving entity has ‘signed’ the terms and conditions asserted by the individual/ data subject. The new flows are:

Flow 5 (My Data to Your Data (inc Our Data) – Individuals will share more high value, volunteered information with their existing and potential suppliers, eliminating guesswork and waste from many customer management processes. In turn, organisations will share their own expertise/ data with individuals, adding value to the relationship.

Flow 6 (Everybody’s Data to My Data) – With their new, more sophisticated personal information management tools, individuals will be able to take direct feeds from public domain sources for use on their own mashups and applications (e.g. crime maps covering where I live/ travel)

Flow 7 (My Data to (someone else’s) My Data) – An enhanced version of ‘peer to peer’ information sharing.

Flow 8 (My Data to Their Data) – The (currently) unlikely concept of the individual making their volunteered information available to/ through the data aggregators. Indeed we are already starting to see the plumbing for this new flow being put in place with the launch of the Acxiom Identity Card.

Propeller Target State

The implications of the above are enormous, my projection being that over time some 80% of customer management processes will be driven from ‘My Data’. I’m pretty confident about that, a) because we are already see-ing the beginning of the change in the current rush for ‘user generated content’ (VPI without the contract), and b) because the economics will stack up. Organisation need data to run their operations – they don’t really mind where it comes from. So, if a new source emerges that is richer, deeper, more accurate, less toxic – and all at lower cost than existing sources; then organisations will use this source.

It won’t happen overnight obviously; as mentioned above specific tools, processes and commercial approaches need to emerge before this information begins to flow – and even then the shift will be slow but steady, probably beginning with Buying Intention data as it is the most obvious entry point with enough impact to trigger the change. That said, the Mydex social enterprise already has a working proof of concept up and running showing much of the above working. A technical write up of the proof of concept build can be found here. And the market implications of this are explored in more detail in new research on the market value of VPI shortly to be published by Alan Mitchell at Ctrl-Shift.

The two hour session at the VRM workshop was barely enough to scratch the surface of the above issues, so the plan is to continue the dialogue and begin specifying the capabilities required in detail in the User Driven and Volunteered Personal Information (technology) workgroup at The Kantara Initiative. The workgroup charter can be found here. A parallel workgroup focused on business and policy aspects will also be launched in the next few weeks. Anyone wishing to get involved in the workgroup can sign up to the mailing list here and we’ll get started with the work in the next couple of weeks.

 

Categories: #Kantara, Data, Mydex, Privacy, Project VRM, VPI Tags: