Beyond Office 365 – knowledge graphs, Microsoft Graph & AI!

This is the first joint post in a series where Findwise & SearchExplained, together decompose Microsoft’s realm with the focus on knowledge graphs and AI. The advent of graph technologies and more specific knowledge graphs have become the epicentre of the AI hyperbole.

microsoft_graph

The use of a symbolic representation of the world, as with ontologies (domain models) within AI is by far nothing new. The CyC project, for instance, started back in the 80’s. The most common use for average Joe would be by the use of Google Knowlege Graph that links things and concepts. In the world of Microsoft, this has become a foundational platform capacity with the Microsoft Graph.

It is key to separate the wheat from the chaff since the Microsoft Graph is by no means a Knowledge Graph. It is a highly platform-centric way to connect things, applications, users and information and data. Which is good, but still it lacks the obvious capacity to disambiguate complex things of the world, since this is not its core functionality to build a knowledge graph (i.e ontology).

From a Microsoft centric worldview, one should combine the Microsoft Graph with different applications with AI to automate, and augment the life with Microsoft at Work. The reality is that most enterprises do not use Microsoft only to envelop the enterprise information landscape. The information environment goes far beyond, into a multitude of organising systems within or outside to company walls.

Question: How does one connect the dots in this maze-like workplace? By using knowledge graphs and infuse them into the Microsoft Graph realm?

Office 365 MDM

The model, artefacts and pragmatics

People at work continuously have to balance between modalities (provision/find/act) independent of work practice, or discipline when dealing with data and information. People also have to interact with groups, and imaged entities (i.e. organisations, corporations and institutions). These interactions become the mould whereupon shared narratives emerge.

Knowledge Graphs (ontologies) are the pillar artefacts where users will find a level playing field for communication and codification of knowledge in organising systems. When linking the knowledge graphs, with a smart semantic information engine utility, we get enterprise-linked-data that connect the dots. A sustainable resilient model in the content continuum.

Microsoft at Work – the platform, as with Office 365 have some key building blocks, the content model that goes cross applications and services. The Meccano pieces like collections [libraries/sites] and resources [documents, pages, feeds, lists] should be configured with sound resource descriptions (metadata) and organising principles. One of the back-end service to deal with this is Managed Metadata Service and the cumbersome TermStore (it is not a taxonomy management system!). The pragmatic approach will be to infuse/integrate the smart semantic information engine (knowledge graphs) with these foundation blocks. One outstanding question, is why Microsoft has left these services unchanged and with few improvements for many years?

The unabridged pathway and lifecycle to content provision, as the creation of sites curating documents, will be a guided (automated and augmented [AI & Semantics]) route ( in the best of worlds). The Microsoft Graph and the set of API:s and connectors, push the envelope with people at centre. As mentioned, it is a platform-centric graph service, but it lacks connection to shared narratives (as with knowledge graphs).  Fuzzy logic, where end-user profiles and behaviour patterns connect content and people. But no, or very limited opportunity to fine-tune, or align these patterns to the models (concepts and facts).

Akin to the provision modality pragmatics above is the find (search, navigate and link) domain in Office 365. The Search road-map from Microsoft, like a yellow brick road, envision a cohesive experience across all applications. The reality, it is a silo search still 😉 The Microsoft Graph will go hand in hand to realise personalised search, but since it is still constraint in the means to deliver a targeted search experience (search-driven-application) in the modern search. It is problematic, to say the least. And the back-end processing steps, as well as the user experience do not lean upon the models to deliver i.e semantic-search to connect the dots. Only using the end-user behaviour patterns, end-user tags (/system/keyword) surface as a disjoint experience with low precision and recall.

The smart semantic information engine will usually be a mix of services or platforms that work in tandem,  an example:

  1. Semantic Tools (PoolParty, Semaphore)
  2. Search and Analytics (i3, Elastic Stack)
  3. Data Integration (Marklogic, Biztalk)
  4. AI modules (MS Cognitive stack)

In the forthcoming post on the theme Beyond Office 365 unpacking the promised land with knowledge graphs and AI, there will be some more technical assertions.
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Tinkering with knowledge graphs

I don’t want to sail with this ship of fools, on the opulent data sea, where people are drowning without any sense-making knowledge shores in sight. You don’t see the edge before you drop!

Knowledge EngineeringEchoencephalogram (Lars Leksell)  and neural networks

How do organisations reach a level playing field, where it is possible to create a sustainable learning organisation [cybernetics]?
(Enacted Knowledge Management practices and processes)

Sadly, in many cases, we face the tragedy of the commons!

There is an urgent need to iron out the social dilemmas and focus on motivational solutions that strive for cooperation and collective action. Knowledge deciphered with the notion of intelligence and emerging utilities with AI as an assistant with us humans. We the peoples!

To make a model of the world, to codify our knowledge and enable worldviews to complex data is nothing new per se. A Knowlege Graph – is in its essence a constituted shared narrative within the collective imagination (i.e organisation). Where facts of things and their inherited relationships and constraints define the model to be used to master the matrix.  These concepts and topics are our communication means to bridge between groups of people. Shared nomenclatures and vocabularies.

Terminology Management

Knowledge Engineering in practice


At work – building a knowledge graph – there are some pillars, that the architecture rests upon.  First and foremost is the language we use every day to undertake our practices within an organisation. The corpus of concepts, topics and things that revolve around the overarching theme. No entity act in a vacuum with no shared concepts. Humans coordinate work practices by shared narratives embedded into concepts and their translations from person to person. This communication might be using different means, like cuneiform (in ancient Babel) or digital tools of today. To curate, cultivate and nurture a good organisational vocabulary, we also need to develop practices and disciplines that to some extent renders similarities to ancient clay-tablet librarians. Organising principles, to the organising system (information system, applications).  This discipline could be defined as taxonomists (taxonomy manager) or knowledge engineers. (or information architect)

Set the scope – no need to boil the ocean


All organisations, independent of business vertical, have known domain concepts that either are defined by standards, code systems or open vocabularies. A good idea will obviously be to first go foraging in the sea of terminologies, to link, re-hash/re-use and manage the domain. The second task in this scoping effort will be to audit and map the internal terrain of content corpora. Since information is scattered across a multitude of organising systems, but within these, there are pockets of a structure. Here we will find glossaries, controlled vocabularies, data-models and the like.  The taxonomist will then together with subject matter experts arrange governance principles and engage in conversations on how the outer and inner loop of concepts link, and start to build domain-specific taxonomies. Preferable using the simple knowledge organisation system (SKOS) standard

Participatory Design from inception


Concepts and their resource description will need to be evaluated and semantically enhanced with several different worldviews from all practices and disciplines within the organisation. Concepts might have a different meaning. Meaning is subjective, demographic, socio-political, and complex. Meaning sometimes gets lost in translation (between different communities of practices).

The best approach to get a highly participatory design in the development of a sustainable model is by simply publish the concepts as open thesauri. A great example is the HealthDirect thesaurus. This service becomes a canonical reference that people are able to search, navigate and annotate.

It is smart to let people edit and refine and comment (annotate) in the same manner as the Wikipedia evolves, i.e edit wiki data entries. These annotations will then feedback to the governance network of the terminologies. 

Term Uppdate

Link to organising systems

All models (taxonomies, vocabularies, ontologies etc.) should be interlinked to the existing base of organising systems (information systems [IS]) or platforms. Most IS’s have schemas and in-built models and business rules to serve as applications for a specific use-case.  This implies also the use of concepts to define and describe the data in metadata, as reference data tables or as user experience controls. In all these lego pieces within an IS or platform, there are opportunities to link these concepts to the shared narratives in the terminology service.  Linked-enterprise-data building a web of meaning, and opening up for a more interoperable information landscape.

One omnipresent quest is to set-up a sound content model and design for i.e Office 365, where content types, collections, resource descriptions and metadata have to be concerted in the back-end services as managed-metadata-service. Within these features and capacities, it is wise to integrate with the semantic layer. (terminologies, and graphs). Other highly relevant integrations relate to search-as-a-service, where the semantic layer co-acts in the pipeline steps, add semantics, link, auto-classify and disambiguate with entity extraction. In the user experience journey, the semantic layer augments and connect things. Which is for instance how Microsoft Graph has been ingrained all through their platform. Search and semantics push the envelope 😉

Data integration and information mechanics

A decoupled information systems architecture using an enterprise service bus (messaging techniques) is by far the most used model.  To enable a sustainable data integration, there is a need to have a data architecture and clear integration design. Adjacent to the data integration, are means for cleaning up data and harmonise data-sets into a cohesive matter, extract-load-transfer [etl]. Data Governance is essential! In this ballpark we also find cues to master data management. Data and information have fluid properties, and the flow has to be seamless and smooth.  

When defining the message structure (asynchronous) in information exchange protocols and packages. It is highly desired to rely on standards, well-defined models (ontologies). As within the healthcare & life science domain using Hl7/FHIR.  These standards have domain-models with entities, properties, relations and graphs. The data serialisation for data exchange might use XML or RDF (JSON-LD, Turtle etc.). The value-set (namespaces) for properties will be possible to link to SKOS vocabularies with terms.

Query the graph

Knowledge engineering is both setting the useful terminologies into action, but also load, refine and develop ontologies (information models, data models). There are many very useful open ontologies that could or should be used and refined by the taxonomists, i.e ISA2 Core Vocabularies, With data-sets stored in a graph (triplestore) there are many ways to query the graph to get results and insights (links). Either by using SPARQL (similar to SQL in schema-based systems), or combine this with SHACL (constraints) or via Restful APIs.

These means to query the knowledge graph will be one reasoning to add semantics to data integration as described above.

Adding smartness and we are all done…

Semantic AI or means to bridge between symbolic representation (semantics) and machine learning (ML), natural language processing (NLP), and deep-learning is where all thing come together.

In the works (knowledge engineering) to build the knowledge graph, and govern it, it taxes many manual steps as mapping models, standards and large corpora of terminologies.  Here AI capacities enable automation and continuous improvements with learning networks. Understanding human capacities and intelligence, unpacking the neurosciences (as Lars Leksell) combined with neural-networks will be our road ahead with safe and sustainable uses of AI.
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Benevolent & sustainable smart city development

The digitisation of society emerge in all sectors, and the key driver to all this is the abundance of data that needs to be brought into context and use.

Participation

When discussing digitisation, people commonly think in data highways and server farms as being the infrastructure. Access to comprehensive information resources is increasingly becoming a commodity, enabling and enhancing societal living conditions. To achieve this, sense-making of data has to be in integrative part of the digital infrastructure. Reflecting this to traditional patterns, digital roads need junctions, signs and semaphores to function, just as their physical counterparts.

The ambition with AI and smart society and cities should be for the benefit of its inhabitants, but without a blueprint to get a coherent model that will be working in all these utilities, it will all break. Second to this, benevolence, participation and sustainability, have to be the overarching theme, to contrast dystopian visions with citizen surveillance and fraudulent behaviour.

Data needs context to make sense and create value, and this frame of reference will be realised through domain models of the world, with shared vocabularies to disambiguate concepts. In short a semantic layer. It is impossible to boil the ocean, which makes us rather lean toward a layered approach.

All complex systems (or complex adaptive system, CAS) revolve around a set of autonomous agents, for example, cells in a human body or citizens in an urban city. The emergent behaviour in CAS is governed by self-organising principles. A City Information Architecture is by nature a CAS, and hence the design has to be resilient and coherent.

What infrastructural dimensions should a smart city design build upon?

  • Urban Environment, the physical spaces comprised of geodata means, register of cadastre (real-estate), roads and other things in the landscape.
  • Movable Objects, with mobile sensing platforms capturing things like vehicles, traffic and more, in short, the dynamics of a city environment.
  • Human actor networks, the social economic mobility, culture and community in the habitat
  • Virtual Urban Systems augmented and immersive platforms to model the present or envision future states of the city environment

Each of these organising systems and categories holds many different types of data, but the data flows also intertwine. Many of the things described in the geospatial and urban environment domain, might be enveloped in a set of building information models (BIM) and geographical information systems (GIS). The resource descriptions link the objects, moving from one building to a city block or area. Similar behaviour will be found in the movable object’s domain because the agents moving around will by nature do so in the physical spaces. So when building information infrastructures, the design has to be able to cross-boundaries with linked-models for all useful concepts. One way to express this is through a city information model (CIM).

When you add the human actor networks layer to your data, things will become messy. In an urban system, there are many organisations and some of these act as public agencies to serve the citizens all through the life and business events. This socially knitted interaction model, use the urban environment and in many cases moveble objects. The social life of information when people work together, co-act and collaborate, become the shared content continuum.
Lastly, data from all the above-mentioned categories also feeds into the virtual urban system, that either augment the perceived city real environment, or the city information modelling used to create instrumental scenarios of the future state of the complex system.

Everything is deeply intertwingled

Connect people and things using semantics and artificial intelligence (AI) companions. There will be no useful AI without a sustainable information architecture (IA). Interoperability on all levels is the prerequisite; systemic (technical and semantic),  organisational (process and climate).

Only when we follow the approach of integration and the use of a semantic layer to glue together all the different types and models – thereby linking heterogeneous information and data from several sources to solve the data variety problem – are we able to develop an interoperable and sustainable City Information Model (CIM).

Such model can not only be used inside one city or municipality – it should be used also to interlink and exchange data and information between cities as well as between cities and provinces, regions, countries and societal digitalisation transformation.

A semantic layer completes the four-layered Data & Content Architecture that usual systems have in place:

semantic-layer

Fig.: Four layered content & data architecture

Use standards (as ISA2), and meld them into contextual schemas and models (ontologies), disambiguate concepts and link these with verbatim thesauri and taxonomies (i.e SKOS). Start making sense and let AI co-act as companions (Deep-learning AI) in the real and virtual smart city, applying semantic search technologies over various sources to provide new insights. Participation and engagement from all actor-networks will be the default value-chain, the drivers being new and cheaper, more efficient smart services, the building block for the city innovation platform.

The recorded webinar and also the slides presented

 

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View Sebastian Gabler's LinkedIn profileSebastian Gabler

Digital recycling & knowledge growth

How do we prevent the digital debris of human clutter and mess? And to what extent will future digital platforms guide us in knowledge creation and use?

Start making sense, and the art of making sense!

People and the Post, Postal History from the Smithsonian's  National Postal Museum

People and the Post, Postal History from the Smithsonian’s National Postal Museum

Mankind’s preoccupation for much of this century has to become fully digitalized. Utilities, software, services and platforms are all becoming an ‘intertwingled’ reality for all of us. Being mobile, the blurring of the borders between the workplace and recreational life plus the ease of digital creation are creating information overloads and (out-of-sight) digital landfills. While digital content creation is cheaper to create and store, its volume and its uncared for status makes it harder for everyone else to find and consume the bits they really need (and have some provenance for peace of mind).

Fear not. A collection of emerging digital technologies exist that can both support and maintain future sustainable digital recycling – things like: Cognitive Computing, Artificial Intelligence; Natural Language Processing; Machine Learning and the like, Semantics adding meaning to shared concepts, and Graphs linking our content and information resources. With good information management practice and having the appropriate supporting tools to tinker with, there is a great opportunity to not only automate knowledge digitization but to augment it.

Automation

In the content continuum (from its creation to its disposal) there is a great need for automating processes as much as possible in order to reduce the amount of obsolete or hidden (currently value-less) digital content. Digital knowledge recycling is difficult as nearly every document or content creator is, by nature, reluctant to add further digital tags (a.k.a. metadata) describing their content or documents once they have been created. What’s more experience shows this is inefficient on a number of accounts, one of which is inconsistency.

Most digital documents (and most digital content, unless intended to sell something publicly) therefore lack the proper recycling resource descriptors that can help with e.g. classification, topic description or annotation with domain specific (shared, consistent) concepts. Such descriptions add appropriate meaning or context to content, aiding its further digital reuse (consumption). Without them, the problem of findability is likely to remain omnipresent across many intranets and searched resources.

Smartphones generate content automatically, often without the user thinking or realizing. All kinds of resource descriptors (time, place etc.) are created automatically through movement and mobile usage. With the addition of further machine learning and algorithms, online services such as Google Photos use these descriptors (and some automatic annotation of their own) to add more contextual data before classifying pictures into collections. This improved data quality (read: metadata addition and improved findability) allows us to find the pictures or timeline we want more easily.

In the very same manner, workplace content or documents can now have this same type of supporting technical platform that automatically adds additional business specific context and meaning. This could include data from users: their profiles, departments or their system user behaviour patterns.

For real organizational agility though a further extra layer of automatic annotation (tagging) and classification is needed – achieved using shared models of the business. These models can be expressed through a combination of various controlled vocabularies (taxonomies) that can be further joined through relationships (ontologies) and finally published (publicly or privately) as domain models as linked data (in graphs). Within this layer exist not just synonyms, but alternative and preferred labels, and more importantly relationships can be expressed between concepts – hence the graph: concepts being the dots (nodes) with relationships the joining lines (vertices). Using certain tools, the certain relationships between concepts can be further given a weighting.

This added layer generates a higher quality of automated context, meaning and consistency for the annotation (tagging) of content and documents alike. The very same layer feeds information architecture in the navigation of resources (e.g. websites). In Search, it helps to disambiguate between queries (e.g. apple the fruit, or apple the organization?).

This digital helper application layer works very much in the same smooth manner as e.g. Google Photos, i.e. in the background, without troubling the user.

This automation however, will not work without sustainable organizing principles, applied in information management practices and tools. We still need a bit of human touch! (Just as Google Photos added theirs behind the scenes earlier, as a work in progress)

Augmentation

This codification or digitalization of knowledge allows content to be annotated, classified and navigated more efficiently. We are all becoming more aware of the Google Knowledge Graph or the Microsoft Graph that can connect content and people. The analogy of connecting the dots in a graph is like linking digital concepts and their known relationships or values.

Augmentation can take shape in a number of forms. A user searching for a particular query can be presented not only with the most appropriate search results (via the sense-making connections and relationships) but also can be presented with related ideas they had not thought of or were unaware of – new knowledge and serendipity!

Search, semantic, and cognitive platforms have now reached a much more useful level than in earlier days of AI. Through further techniques new knowledge can also be discovered by inference, using the known relationships within the graph to fill in missing knowledge.

Key to all of this though is the building of a supporting back-end platform for continuous improvement in the content continuum. Technically, something that is easier to start than one may first suspect.

Sustainable Organising Principles to the Digital Workplace

 


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Digital wizardry for customers & employees – the next elements

A reflection on Mobile World Congress topics mobility, digitalisation, IoT, the Fourth Industrial Revolution and sustainability

MWC2017Commerce has always had a conversational, today it is digital. Organisations are asking how to organise clean effective data for an open digital conversation. 

Digitalization’s aim is to answer customer/consumer-centric demands effectively (with relevant and related data) and in an efficient manner. [for the remainder of the article read consumer and customer interchangeably]

This essentially means Joining the dots between clean data and information and being able to recognise the main and most consumer-valuable use cases, be it common transaction behaviour or negating their most painful user experiences.

This includes treading the fine line between being able to offer “intelligent” information (intelligent in terms of relevance and context)  to the consumer effectively and not seeming to be freaky or stalker-like. The latter is dealt with by forming a digital conversation where the consumer understands the use of their information only being used for their end needs or wants.

While clean, related data from the many various multi-channel customer touch-points forms the basis of an agile digital organisation, it is the combination of significant data analysis insight of user demand & behaviour (clicks, log analysis etc), machine learning and sensible prediction that forms the basis of artificial Intelligence. Artificial intelligence broken down is essentially resultant action based on the inferences of knowing certain information, i.e. the elementary Dr Watson, but done by computers.

This new digital data basis means being able to take data from what were previous data silos and combine it effectively in a meaningful way, for a valuable purpose. While the tag of Big Data becomes weary in a generalised context, key is the picking of data/information to get relevant answers to the mosts valuable questions, or in consumer speak, to get a question answered or a job done effectively.

Digitalisation (and then the following artificial intelligence) relies obviously on computer automation, but it still requires some thoughtful human-related input. Important steps in the move towards digitalization include:

  • Content and Data Inventory, to clean data/ the cleansing of data and information;
  • Its architecture (information modelling, content analysis, automatic classification and annotation/tagging);
  • Data analysis in combination with text analysis (or NLP: natural language processing for the more abundant unstructured data, content), the latter to put flesh on the bone as it were, or adding meaning and context
  • Information Governance: the process of being responsible for the collection, proper storage and use of important digital information (now made less ignorable with new citizen-centric data laws (GDPR) and the need for data agility or anonymization of data)
  • Data/system Interoperability: which data formats, structures, and standards, are most appropriate for you? What data collections are most Relational databases, Linked/graph data, data lakes etc.?); 
  • Language/cultural interoperability: letting people with different perspectives accessing the same information topics using their own terminology.
  • Interoperability for the future also means being able to link everything in your business ecosystem for collaboration, in- and outbound conversations, endless innovation and sustainability.
  • IoT or the Internet of Things is making the physical world digital and adding further to the interlinked network, soon to be superseded by the AoT (the analysis of things)
  • Newer steps of Machine learning (learning about consumer preferences and behaviour etc.) and artificial intelligence (being able to provide seemingly impossible relevant information, clever decision-making and/or seamless user experience).

The fusion of technologies continues further as the lines between the physical, digital, and biological spheres with developments in immersive Internet, as with Augmented Reality (AR) and Virtual Reality (VR).

The next elements are here already: semantic (‘intelligent’) search, virtual assistants, robots, chat bots… with 5G around the corner to move more data, faster.

Progress within mobility paves the way for a more sustainable world for all of us (UN Sustainable Development), with a future based on participation. In emerging markets we are seeing giant leaps in societal change. Rural areas now have access to the vast human resources of knowledge to service innovation e.g. through free-access to Wikipedia on cheap mobile devices and Open Campuses. Gender equality with changed monetary and mobile financial practices and blockchain means to raise to the challenge with interoperability. We have to address the open paradigm (e.g Open Data) and the participation economy, building the next elements. Shared experience and information commons. This also falls back to the intertwingled digital workplace, and practices to move into new cloud based arenas.

Some last remarks on the telecom industry, it is loaded with acronyms and for laymen in the area sometimes a maze to navigate and to build some sensemaking.

So are these steps straightforward, or is the reality still a potential headache for your organisation? 

Contact Findwise now to ease the process, before your competitor does 😉
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Sensemaking or Digital Despair

Finding our way in the bright, futuristic, data-driven & intertwined world, often taxes us and our digital-hungry senses. Fast rewind to the recent FindabilityDay 2015 and the parade of brilliant speaker talents on stage. Starting of with our dear friend and peer, Martin White, on the topic the future of search.

Human factors, from idea inception to design and practical UX of our digital artifacts. The key has been make-do and ship. This is the reason the more technically-advanced mobiles fell by the wayside 8 years ago Apple’s iPhone.

The social life with information, shapes our daily lives, in a hyper-connected world. It’s still very hard to find that information needle in the haystack, and most days we feel despair when losing the scent of information nuggets. The results from the Findability Survey, spoke clearly. Without sound organising principles to information and data, and a pliable recorded vision, we won’t find anything of value.

Next, moving into an old business model, with Luna’s and Sara’s presentation, a great example, where we see that the orchestration and choreography of their data assets will determine their survival or demise – in conjunction with infused means to information management practices, processes and tools. They showed a new set of facets to delivering on their mission in their line-of business.

Regardless of the line of business, it becomes clear that our fragmented workplace setting now only partly “on tap”. It makes our daily lives a mess, since things do not interoperate. The vision should show the way to a shared information commons, where we all cultivate.

So finally, How do we make sense of any mess?

Answer: Architect a place where you can find comfort with social conventions shared on the information used. Abby Covert, laid out a beautiful tapestry of things we all need to take on, to make sense in everyday life, and life at work. With clear and distinct guardrails, and signposts we don’t feel so distracted or lost. Her talk was a true enlightenment for me, being of the same profession, Information Architect.

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Finding business values in the emerging digital workplace

How does one experience the promised business rewards of the emerging digital workplace (a.k.a the intranet)?

A group of renowned intranet professionals have taken on the task this question and offer sound practical advice as to how to achieve real business value in their new book “intranets that create business value” or in Swedish “intranät som skapar värde“,

intranat-som-skapar-varde-framsida

Today, in fact most days, end-users feel bewildered when using the intranet.It is to some extent impossible to navigate.There exists a hodgepodge of mixed user experiences, given that the intranet often serves as the access point to several tools. And findability too is low! With a coherent, smooth and interoperable workplace, users should be able to find information and data, peers and colleagues to solve their everyday tasks, in an efficient way…  anywhere, on any device and anytime.

The authors’ narrative describes how the intranet can best be used to produce beneficial business transformation, by including detailed chapters on: strategy, content & information architecture, search/findability, governance and stakeholder management, end-user engagement and adaptation. Measures and metrics are also included to qualify the sought after business values.

Findwise have contributed to the sections relating to organising principles. Put simply, it should be easy for a user to know where and how to contribute with information and content in a good manner, so that others are able to find and co-act on such codified knowledge.

Without sound and sustainable organising principles there will be no findability: shit in = shit out! Regardless of the technology platform employed for search or intranet

Buy the e-book today, in advance of the published printed version in May!

Stay Cleaning and moving boxes for cloud

This is the seventh post in a series (1, 2, 3, 4, 5, 6) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud.  We will help you to consider the options and guide on the steps you need to take.

Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example.  Planning for migration.

Moving Boxes

Do not even think about moving into the cloud apartment without a proper  cleaning of the content buckets. Moving from an architected household to a rented place, taxes a structured audit. Clean out all redundant, outdated and trivial matter (ROT). The very same habit you have cleaning up the attic when moving out from your old house.

It is also a good idea to decorate and add any features to your new cloud apartment before the content furniture is there.  It means the content will fit with any new design and adapt to any extra functionality with new features like windows and doors.  This can be done by reviewing and updating your publishing templates at the same time.  This will save time in the future.

Leaning upon the information governance standards, it should be easy to address the cleaning before moving, for all content owners who have been appointed to a set of collections or habitats. Most organisations could use a content vacuum cleaner, or rather use the search facilities and metric means to deliver up to date reports on:

  1. Active / in-Active habitats
  2. No clear ownership or the owner has left the building
  3. Metadata and link quality to content and collections to be moved across to the cloud apartments.
  4. Review publishing templates and update features or design to be used in the Cloud

When all active habitats and qualified content buckets have been revisited by their set of curators and information owners. The preparation and use of moving boxes, should be applied.

All moving boxes do need proper tagging, so that any moving company will be able to sort out where about the stuff should be placed in the new house, or building. For collections, and habitats, this means using the very same set of questions stated for adding a new habitat or collection to the cloud apartment house. Who, why, where and so forth, through the use of a structured workflow and form. When this first cleaning steps have been addressed, there should be automatic metadata enhancement, aligned with the information management processes to be used in the new cloud.

With decent resource descriptions and cleaned up content through the audit (ROT), this last step will auto-tag content based upon the business rules applied for the collection or habitat. Then been loaded into the content moving truck, or loading dock. Ready to added to the cloud.

All content that neither have proper assigned information ownership, or are in such a shape that migration can’t be done should persist on the estate or be archived or purged. This means that all metadata and links to either content bucket or habitat that won’t be moved in the first instances, should at least have correct and unique uri:s, address, to this content. And in the case a bucket or habitat have been run down by a demolition firm, purged. All inter-linkage to that piece of content or collection have to be changed.

This is typically a perfect quality report, to the information owners and content editors, that they need to work through prior to actually loading the content on the content dock.

Rubbish and Weed
Finally when all rotten data, deserted habitats and unmanageable buckets have been weeded out. It is time to prepare the moving truck, sending the content into its new destination.

Our final thread will cover how will the organisation and it habitants will be able to find content in this mix of clouds, and things left behind on the old estate? Cloud Search and Enterprise Search, seamless or a nightmare?

Please join our Live Stream on YouTube the 20th November 8.30AM – 10AM Central European Time
View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog
View Mark Morrell's LinkedIn profileMark Morell intranet-pioneer

Placemaking, wayfinding and game rules in the Clouds

This is the sixth post in a series (1, 2, 3, 4, 5, 7) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud.  We will help you to consider the options and guide on the steps you need to take.

Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example.  We will cover more about SharePoint in this post, and placemaking in the cloud.
Funky Village
In SharePoint there are a set of logic chunks. One could decompose the digital workplace into intranet sites, as departmental and organisational buckets; team sites where groups collaborate, and lastly your personal domain being the my site collection. Navigating between these, is a mix of traditional information architecture and search driven content.  When being within a such a habitat as a teamsite, it is not always obvious how to cross-link or navigate to other domains within the digital workplace hosted in Sharepoint.

One way to overcome this, is to render different forms of portals, based upon dynamic navigation. These intersections and aggregates help users to move around the maze of buckets and collections of the content. Sharepoint have very good features, and options to create search-based content delivery mechanisms.

 A metadata and search-based content model, gives us cues for the future design of the digital workplace, with connected habitats and sustainable information architecture. Where people don’t get lost, and have wayfinding means to survive everyday work practices.

This is where how you manage the content in SharePoint and Office 365 is critical.  As we said in our first post it is important you have a good information architecture combined with a good governance framework that helps you to transform your buckets of content from the estate into the cloud.  We have covered information architecture so we now move more towards how governance completes the picture for you.

There are three approaches to the governance your organisation needs to have with SharePoint and Office 365.  You don’t have to use just one.  You can combine some of each to find the right blend for your organisation.  What works best for you will depend on a number of different factors.  Among them:

  • Restricting use – stopping some features from being used e.g. SharePoint Designer
  • Encouraging best practice – guidance and training available
  • Preventing problems – checking content before it is published

Each of these approaches can support your governance strategy.  The key is to understand what you need to use.

Restricting use

You need to be clear why your organisation is using SharePoint and Office 365 and the benefits expected.  This will shape how tight or loose your governance needs to be.

Once you are clear on this, you then need to consider the strategic benefits and drawbacks such as SharePoint Designer and site collection administration rights.

Benefits

  • You control what is being used.
  • You decide who uses a feature e.g. SharePoint Designer.
  • You manage the level of autonomy each site owner has.
  • You find out why someone needs to use a feature.
  • You monitor costs for licences, users, servers, etc.
  • You measure who is using what and why for reporting.

Drawbacks

  • You stifle innovation by not allowing people to test out ideas.
  • You stop legitimate use by asking for permission to use features.
  • You prevent people being able to share knowledge how they wish to.
  • You may be unable to realise the maximum potential of SharePoint.
  • You create unnecessary administration.
  • You risk adding costs without any value to offset them with.

You need to get the balance right with governance that gives you maximum value for the effort needed managing SharePoint and Office 365.

Encourage best practice

The goal from implementing SharePoint and Office 365 is to have an environment that enables employees to publish, share, find and use information easily to help with their work.  They are confident the information is reliable and appropriate, whatever their need for it is.  People also feel comfortable using these tools rather than alternative methods like calling helpdesks or emailing other employees for help.

Encouraging best practice by giving them the opportunity to test to meet their needs is one approach to achieving this.  There are factors you need to consider that can help or hinder the success of using this approach.

Benefits

  • You inform employees of all the benefits to be gained.
  • You train people to use the right tools.
  • You design a registration process to direct people to the right tools.
  • You point employees to guidance on how to follow best practice.
  • You encourage innovation by giving everyone freedom of use.

Drawbacks

  • You can’t prevent people using different tools to those you recommend.
  • You risk confusing employees using content unsure of its integrity.
  • You can’t prevent everyone ignoring best practice when publishing.
  • You may make it difficult for people to share knowledge effectively.
  • Your governance model may be ineffective and need improving.

Getting the balance right between encouraging best practice and the level of governance to deter behaviour which can destroy the value from using SharePoint and Office 365 is critical.

Preventing problems

As well as encouraging best practice, preventing problems helps to reduce time and costs wasted on sorting out unnecessary issues.  While that is the aim of most organisations the practical realities as it is rolled out can divert plans from achieving this.

You need to get the right level of governance in place to prevent problems.  Is it encouraging innovation and keeping governance light touch?  Is it a heavier touch to prevent the ‘wrong’ behaviour and minimise risk of your brand and reputation being damaged?  How much do you want to spend preventing problems?  What does your cost/benefit analysis show?

Benefits

  • People using SharePoint and Office 365 have a great experience (especially the first time they use it).
  • Everyone is confident they can use it for what they need it for without experience problems.
  • Employees don’t waste time calling the helpdesk because many problems have been prevented.
  • Effective governance encourages early adoption and increased knowledge sharing.
  • Costs spent preventing problems are justified by increased productivity and reduced risk of errors.

Drawbacks

  • People find registering difficult and lengthy because of extra steps taken to prevent problems and don’t bother.
  • People find it too restrictive for their needs and it stifles innovation.
  • People turn to other tools (maybe not approved) to meet their needs and ask other people for help to use them.
  • Too restrictive governance prevents most beneficial use by raising the barrier too high for people to use.
  • Costs of preventing problems are higher than benefits to be gained and not justified.

You need to consider the potential benefits and drawbacks before deciding on the level of governance that is right for your organisation.

Remember, it is possible and probably desirable to have different levels of governance for each feature.  It may be lighter for personal views and opinions expressed in MyProfile and MySite but tighter for policies and formal news items in TeamSites.

That is the challenge!  You have so much flexibility to configure the tools to meet your organisation’s needs.  Don’t be afraid to test out on part of your intranet to see what effect it has and involve employees to feed back on their experience before launching it.

The way forward is to create a sustainable information architecture, that supports an information environment that is available on any platform, everywhere, anytime and on any device.  A governance  framework can show roles and responsibilities, how they fit with a strategy and plan with publishing standards as the foundation to a consistently good user experience.

Combining a governance framework and information architecture with the same scope avoids any gaps in your buckets of content being managed or not being found.  It helps you transform from your estate to the cloud successfully.

In our last concluding posts we will dive into more design oriented topics with a helping hand from findability experts and developers. Adding migration thoughts in next post. But first navigating the social graph being people centric, leaving some outstanding questions. How will the graph interoperate if your business runs several clouds, and still have buckets of content elsewhere?

Please join our Live Stream on YouTube the 20th November 8.30AM – 10AM Central European Time
View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog
View Mark Morrell's LinkedIn profileMark Morell intranet-pioneer

Content Governance – life cycle and reach

This is the fifth post in a series (1, 2, 3, 4, 6, 7 ) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud.  We will help you to consider the options and guide on the steps you need to take.

 Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example.  We will cover governance and how content should be managed in the cloud in this post.

content buckets

Content created within a context, as either a departmental site, or team habitat has usually only reach and bearing for the local context of fellow members of staff within this unit. Other pieces of content have a coverage that stretches all parts of the business. One simple example, is the bucket of content that makes up the management system, with governing principles, strategies, policies and guidelines that describes the core processes, activities, roles and so forth within an organisation.

Yet other content, as the outcome from a project, will build a bucket of content that either lives in a new context, improves a bucket of content or feeds into yet another following project.

From an information management perspective, it is vital that you have organising principles to all your content, where all these layers have been covered. Both reach, and the life cycle to the set of content.

You need a governance framework that reaches out to every bucket of content.  This covers what is still on your estate as well as the growing amount in the cloud.  All content needs to be managed to remove risks of leakage of sensitive information and prevent people having an inconsistent user experience as they move from one bucket of content in the cloud to another content bucket still on the estate.

You need to make sure people do not see the difference between buckets of content on the estate from content buckets in the cloud.  People using your content to help with their work don’t need to know where the content is kept.  They need to find it as easily as before, preferably even easier!  Content in the cloud  should feel the same and be a natural extension to the digital environment people are already used to.  Manage it with a governance framework that covers every bucket of content and make it more easy to adopt quicker and use more often without caution or delay.

Part of your governance needs to cover publishing standards based on business needs so it is easy to access from any device e.g laptops, tablets and smartphones, and to view without unnecessary authentication levels.  This helps to create that consistent good user experience that encourages people to use your content whether the bucket is in the cloud or not.

A professional team from group HR, might work in their local teamsite, with on-going conversations, work-in-progress documents and so forth. Pieces of their content production leads to governing policies that have a global reach within the organisation, and needs to be linked from the corporate intranet spaces. with versioning and good quality to resource descriptions (meta data). This practice and professional network of HR people, do also share content on a departmental site. With links and resources, that have direct impact on their internal processes. The group of people, have outreaching triggers, and in-bound conversations. And have to balance these two states.

When it comes to temporal content buckets, like a project team site. There are several considerations one have to capture. First where will the outcome and result be stored, when the project is finished. In which context will these content pieces contribute. Second, what should be captured from all on-going conversations (social elements) and work-in-progress and drafts developed during the projects lifecycle? Should a project habitat, be searchable after closing down? Or do the habitat change status, hence all documentation stay within the collection, but the overarching state to the habitat changes? Within Sharepoint these temporal states, versions, workflow and properties. All sum up the organising principles.

If these principles haven’t been ironed out, and been described and decided. Inevitable there will be emerging ghost towns, of dead habitats and lost collections of content. With no governance or ownership whatsoever. All this will become a digital landfill.

We will cover more about SharePoint in our next post in this series. Please visit Michael Sampson‘s recent slides where he takes you through strategy, planning, governance and user adoption for collaboration!
Please join our Live Stream on YouTube the 20th November 8.30AM – 10AM Central European Time
View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog
View Mark Morrell's LinkedIn profileMark Morell intranet-pioneer