Building a chatbot – that actually works

In the era of artificial intelligence and machine learning, chatbots have gained a lot of attention. Chatbots can for example help a user to book restaurants or schedule flights. But why should organizations use chatbots instead of simple user interaction (UI) systems? Considering that chatbots are both easier and more natural to interact with -compared to that of a UI system – endorses the implementation of chatbots in certain use cases. Additionally, a chatbot can engage a user for a longer time which can result in a company increasing its business. A chatbot needs to understand the natural language as there can be multiple ways to express one’s intention with language ambiguity. Natural Language Processing (NLP) helps us to achieve this to some extent.

Natural language processing – the foundation for a chatbot

Compared to rule-based solutions, chatbots using machine learning and language understanding are much more efficient. After years and new waves of statistical models, such as deep learning RNN, LSTM, transformers etc., these algorithms have now become market standards.

NLP is a part of linguistics and artificial intelligence, where algorithms are used to understand, analyze, manipulate and potentially generate human readable text. Usually, it contains two components: Natural Language Understanding (NLU) and Natural Language Generation (NLG).

To start with, the natural language input is mapped into useful representation for machine reading comprehension. This is achieved through using basics like: tokenization, stemming / lemmatization or tagging part of speech. There are also more advanced elements such as recognizing named entities or chunking. The latter is a processing method which organizes the individual terms found previously into a more prominent structure. For example: ’South Africa’ – is more useful as a chunk than the individual words ‘South’ and ‘Africa’.



From the other side, NLG is the process of producing meaningful phrases and sentences in natural language from an internal structural representation using e.g. content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation or linguistic realization.

Open-domain and Goal-Driven Chatbot

Chatbots can be classified into two categories: Goal-driven and Open-domain. Goal-driven chatbots are built to solve specific problems such as a flight bookings or restaurant reservations. On the other hand, the Open-domain dialogue system attempts to establish a long-term connection with the user, such as psychological support and language learning.

Goal-driven chatbots are based on slot filling and handcrafted rules, which are reliable but restrictive in conversation. A user has to go through a predefined dialogue flow to accomplish a task.



Open domain chatbots are intended to converse coherently and engagingly with humans and maintain a long dialog flow with a user. However, we need to have big amounts of data to train these chatbots.



Knowledge graphs bring connections and data structures to information

Knowledge graphs provides a semantic layer on the top of your database which provides you with all possible entities and the relationships between them. There are a number of representation and modeling instruments available for building a knowledge graph, ontologies being one of them.

Ontology comprises of classes, relationships and attributes as shown in Figure 9. This offers a robust way to store information and concepts – similar to how humans store information.



A chatbot based on ontology can help to clarify the user’s context and intent – and it can dynamically suggest related topics. Knowledge graphs represent the knowledge of an organization,  as depicted in the following Figure 10. Consider a knowledge graph based on an organization (as shown on the right image in Figure 10) and a chatbot (as shown on the left image in Figure 10) which is based on the ontology of this knowledge graph. In the chatbot example in Figure 10, the user asks a question about a specific employee. The NLP detects the employee as an entity and also detects the intent behind asking a question about this entity. The chatbot matches the employee entity in the ontology and navigates to the node in the graph. From that node we now know all possible relationships of that entity and the chatbot will ask back for possible options, such as co-workers and projects, to navigate further.



Moreover, the knowledge graph also improves the NLU in a chatbot. For example, if a user asks the following;

  • ‘Which assignments was employee A part of?’. To navigate further in the knowledge graph, a rank system can be created for possible connections from the employee node. This rank system might be based on word vector space and a similarity score.
  • In this scenario, ‘worked in, projects’ will have the highest rank when calculating the score with ‘part of, assignments’. So, the chatbot would know it needs to return the list of corresponding projects.

Virtual assistants with Lucidworks Fusion

Lucidworks Fusion is an example of a platform that supports building conversation interfaces. Fusion includes NLP features to understand the meaning of content and user intent. In the end, it’s all about retrieving the right answer at the right time. Virtual assistants, with a more human level of understanding, goes beyond static rules and profiles. It uses machine learning to predict user intention and provides insights. Customers and employees can locate critical insights to help them move to their next best action.



Lucidworks recently announced Smart Answers – new Fusion’s feature. Smart Answers enhances the intelligence of chatbots and virtual assistants by using deep learning to understand natural language questions. It uses deep learning models and mathematical logic to match the similarity of a question (which can be asked in many different ways) to the most relevant answer. As users interact with the system, Smart Answers continues to rank all answers and improve relevancy.

Fusion is focused on understanding a user’s intent. Smart Answers includes model training and serving methods for different scenarios:

  • When FAQs or question-answer pairs exist, they can be easily integrated into Smart Answers’ model training framework,
  • When there are no FAQ or question-answer pairs, knowledge base documents can be used to train deep learning models and match existing knowledge for the best answers to incoming queries. Once users click on documents returned for specific queries, they become question-answers pairs signals and can enrich the FAQ model training framework,
  • When there are no documents internally, Smart Answers uses cold-start models trained on large online sources, available in multiple languages. Once it goes live, the models begin training on actual user signals.

Smart Answers’ API enables easy integration with any platform, knowledge base, adding value to existing applications. One of the strengths of Fusion Smart Answers is integration with Rasa, an open-source conversation engine. It’s a framework that helps with understanding user intention and maintaining dialogue flow. It also has prebuilt NLP components such as word vectors, tokenizers, intent classifiers and entity extractor. Rasa allows to configure the pipeline that processes a user’s message and analyze human language. Another part of this engine enables modeling dialogues, so chatbot knows what the next action or response should be.

- Hi 
- Hey 
- Hi bot 
- Hey bot 
## intent:request_restaurant 
- im looking for a restaurant 
- can i get [swedish](cuisine) food in any area. 
- a restaurant that serves [caribbean](cuisine) food. 
- id like a restaurant 
- im looking for a restaurant that serves [mediterranean](cuisine) food 
- can i find a restaurant that serves [chinese](cuisine)

Building chatbots requires a lot of training examples for every intent and entity to make them understand the user intention, domain knowledge and to improve NLU of the chatbot. When building a simple chatbot, using prebuilt trained models can be useful and requires less training data. For example: If we build a chatbot where we only need to detect the common location entity, few examples and spaCy models can be enough. However, there might be cases when you need to build a chatbot for an organization where you need different contextual entities – which might not be available in the pretrained models. Knowledge graphs can then be helpful to have a domain knowledge for a chatbot and can balance the amount of work related to training data.


Two main chatbot usages are: 1/solving employee frustration in accessing e.g. corporate information and 2/providing customers with answers to support questions. Both examples above are looking for a solution to reduce time spent on finding information. Especially for online commerce, key performance indicators are clear and can relate to e.g. decreasing call center traffic or call deflection from web and email – examples of situations where ontology based chatbots can be very helpful. From a short-term perspective creating a knowledge graph can initially require a lot of effort – but from a long-term perspective it can also create a lot of value. Companies rely on digital portals to provide information to users; employees search for HR or organization policies documents. Online retailers try to increase customers’ self-service in solving their problems or simply want to improve discovery of their products and services. With solutions like e.g. Fusion Smart Answers, we are able to cut down time-to-resolution, increase customer retention and take knowledge sharing to the next level. It helps employees and customers resolve issues more quickly and empowers users to find the right answer immediately without seeking out additional, digital channels.

Authors: Pragya Singh, Pedro Custodio, Tomasz Sobczak

To read more:

  1. Ehud Reiter and Robert Dale. 1997. Building applied natural language generation systems. Nat. Lang. Eng. 3, 1 (March 1997), 57–87. DOI:
  2. Challenges in Building Intelligent Open-domain Dialog Systems by Huang, M.; Zhu, X.; Gao, J.
  3. A Novel Approach for Ontology-Driven Information Retrieving Chatbot for Fashion Brands by Aisha Nazir, Muhammad Yaseen Khan, Tafseer Ahmed, Syed Imran Jami, Shaukat Wasi

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.


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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Elastic{ON} 2017 – breaking all the records!

Elastic{ON} 2017 draws 2200 participants to Pier 48 during these somewhat chilly San Francisco days in March. It’s a 40% increase from the 1600 or so participants last year, in line with the growing interest for the Elastic Stack and the successes of Elastic commercially.

From Findwise – we are a team of 4 Findwizards, networking, learning and reporting.

Shay Banon, the creator of Elasticsearch and Elastic CTO, is doing both the opening and closing keynote. It is apparent that the transition of the CEO role from Steven Schuurman has already started.

ElasticON 2017

2016 in retrospective with the future in mind

Elastic reached 100 million downloads in 2016, and have managed to land approximately 4000 paying subscription customers out of this installed base to date. A lot of presentations during the conference is centered around new functionality that is developed and will be released to the open source community freely. Other functionality goes into the commercial X-pack subscriptions. Some X-pack functionality is available freely under the Basic subscription level that only requires registration.

Most presentations are centered around search powered analytics, and fewer around regular free text search. Elasticsearch and the Elastic Stack got its main use cases within logging, analytics and in various applications as a data platform or middle-layer with search use-cases as a strong sidekick.

A strong focus on analytics

There’s 22 sponsors at the event, and most of the companies are either offering cloud based monitoring or machine learning services. IBM, the platinum sponsor, are promoting the Bluemix cloud services for cognitive Watson functionality and uses the conference to reach out to the predominantly developer-focused audience.

Prelert was acquired in September last year, and is now being integrated into the Elastic Stack as the Machine Learning component and is used for unsupervised anomaly detection to give operation log insights. Together with the new modular Beats architecture and various Kibana improvements, it looks apparent that Elastic is chasing the huge market Splunk currently controls within logging and analytics.

Elasticsearch SQL – giving BI what it needs

Elasticsearch SQL will give the search engine SQL capability just like Solr got with their parallel SQL interface. Elasticsearch is becoming more and more a “data platform”. Increasingly becomming an competitor to HPE Vertica and Amazon RedShift as it hits a sweet spot use-case where a combination of faster data loading and extreme scalability is needed, and it is acceptable with the tradeoffs of limited functionality (such as the lack of JOIN operations). With SQL support the platform can use existing visualization tools such as Tableu and it expands the user base as many people in the Business Intelligence sector knows SQL by heart.

Fast and simple Beats is music to our ears

Beats will become modular in the next release, and more beats modules will be created either by Elastic or in the open source or commercial community. This increases simple connectivity to various data sources, and adds standardized dashboards for the data source, which will increase simplicity and speed in implementation.

Heartbeat is a new Beat (with a beautiful name!) that send pings to check that services are alive and functioning.

Kibana goes international

Kibana is maturing with some new key updates coming soon. A Time series visual builder that will give graphical guidance on how to build the dashboards, Kibana Canvas gives custom dynamic reports and enables slide show presentations with live data, and the GUI frontend is translated to various languages.

There’s a new tile service for maps, so instead of relying on external map services, Elastic now got control over the maps functionality. The service can be used free of charge but requires registration (Basic subscription) to use all 18 zoom levels.



To conclude, we’ve had three good days with exciting product news and lots of interesting meetings in what could very well be the biggest show for search and search-driven analytics right now! Be sure to see us at the next year’s Elastic{ON} again. If not before, see you then!


From San Francisco with love,

/Andreas, Christian, Joar and Peter

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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What will happen in the information sector in 2017?

As we look back at 2016, we can say that it has been an exciting and groundbreaking year that has changed how we handle information. Let’s look back at the major developments from 2016 and list key focus areas that will play an important role in 2017.

3 trends from 2016 that will lay basis for shaping the year 2017


There has been a massive shift towards cloud, not only using the cloud for hosting services but building on top of cloud-based services. This has affected all IT projects, especially the Enterprise Search market when Google decided to discontinue GSA and replace it with a cloud based Springboard. More official information on Springboard is still to be published in writing, but reach out to us if you are keen on hearing about the latest developments.

There are clear reasons why search is moving towards the cloud. Some of the main ones being machine learning and the amount of data. We have an astonishing amount of information available, and the cloud is simply the best way to handle this overflow. Development in the cloud is faster, the cloud gives practically unlimited processing power and the latest developments available in the cloud are at an affordable price.

Machine learning

One area that has taken huge steps forward has been machine learning. It is nowadays being used in everyday applications. Google wrote a very informative blog post about how they use Cloud machine learning in various scenarios. But Google is not alone in this space – today, everyone is doing machine learning. A very welcome development was the formation of Partnership on AI by Amazon, Google, Facebook, IBM and Microsoft.

We have seen how machine learning helps us in many areas. One good example is health care and IBM Watson managing to find a rare type of leukemia in 10 minutes. This type of expert assistance is becoming more common. While we know that it is still a long path to come before AI becomes smarter than human beings, we are taking leaps forward and this can be seen by DeepMind beating a human at the complex board game Go.

Internet of Things

Another important area is IoT. In 2016 most IoT projects have, in addition to consumer solutions, touched industry, creating a smart city, energy utilization or connected cars. Companies have realized that they nowadays can track any physical object to the benefits of being able to serve machines before they break, streamline or build better services or even create completely new business based on data knowledge. On the consumer side, we’ve in 2016 seen how IoT has become mainstream with unfortunate effect of poorly secured devices being used for massive attacks.


3 predictions for key developments happening in 2017

As we move towards the year 2017, we see that these trends from 2016 have positive effects on how information will be handled. We will have even more data and even more effective ways to use it. Here are three predictions for how we will see the information space evolve in 2017.

Insight engine

The collaboration with computers are changing. For decades, we have been giving tasks to computers and waited for their answer. This is slowly changing so that we start to collaborate with computers and even expect computers to take the initiative. The developments behind this is in machine learning and human language understanding. We no longer only index information and search it with free text. Nowadays, we can build a computer understanding information. This information includes everything from IoT data points to human created documents and data from other AI systems. This enables building an insight engine that can help us formulate the right question or even giving us insight based on information to a question we never ask. This will revolutionize how we handle our information how we interact with our user interfaces.

We will see virtual private assistants that users will be happy to use and train so that they can help us to use information like never before in our daily live. Google Now, in its current form, is merely the first step of something like this, being active towards bringing information to the user.

Search-driven analytics

The way we use and interact with data is changing. With collected information about pretty much anything, we have almost any information right at our fingertips and need effective ways to learn from this – in real time. In 2017, we will see a shift away from classic BI systems towards search-driven evolutions of this. We already have Kibana Dashboards with TimeLion and ThoughtSpot but these are only the first examples of how search is revolutionizing how we interact with data. Advanced analytics available for anyone within the organization, to get answers and predictions directly in graphs and diagrams, is what 2017 insights will be all about.

Conversational UIs

We have seen the rise of Chatbots in 2016. In 2017, this trend will also take on how we interact with enterprise systems. A smart conversational user interface builds on top of the same foundations as an enterprise search platform. It is highly personalized, contextually smart and builds its answers from information in various systems and information in many forms.

Imagine discussing future business focus areas with a machine that challenges us in our ideas and backs everything with data based facts. Imagine your enterprise search responding to your search with a question asking you to detail what you actually are achieving.


What are your thoughts on the future developement?

How do you see the 2017 change the way we interact with our information? Comment or reach out in other ways to discuss this further and have a Happy Year 2017!


Written by: Ivar Ekman

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.

View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog

The Curator – how to cultivate the habitat

This is the fourth post in a series (1, 2, 3, 5, 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.

In the first post we set out the most common challenges you are likely to face and how you may overcome these.  In the second post we focused on how Office 365 and SharePoint can play a part in moving to the cloud.  In the third post we covered how they can help join up your organisation online using their collaboration tools and features.

In this post we will cover engagement and how sorting and categorisation of artifacts, according to a simple-to-understand and easy-to-use standard, will form the bits and parts of the curation and cultivation process.

CultivationAll document libraries should have one standard listing of all items – with two very distinct audiences: being either actors within the habitat or the people contributing, acting and joining the daily conversation; and secondly, those visitors who pass-by the habitat to collect, link and act upon the content presented within the habitats realm.

This makes it very easy for visitors to find their way around a habitat, if the visitors’ area (business lounge) is pretty much aligned to the overarching theme of the site… and all artifacts that the project team like to share wider, have been listed in a virtual bookshelf, with major versions only. The visitors’ area, has all the relevant data, presented upfront. Basically the answers to the questions set when starting the project. The visitors’ area shouldn’t be a backdrop, but rather a storefront. The content has to be of good quality. Then there should be options to engage with the inner-living-room of the habitat, and enter the messy on-going conversations, depending on access-rights. But the default setting, should always be open for unexpected “internal” (within the realm of the organisation) visitors. If the visitors’ area is compiled in a nice and easy to use manner, most visitors are just happy to pick the best-read from the bookshelf, or at least raise a questions for the team! The social construct for this is “welcoming a stranger”, since that visitor might link to your team’s content, cross-linking into his social-spaces.

The habitat’s livingroom and social conversations, will address new context-specific organising principles. A team might want to add new list-items, sort categories or introduce very local what-goes-where themes. This may be especially so when the team consists of actors who have different roles and responsibilities with regard to the overall outcome. And because of this, there may be a certain mix of tools or services in this one habitat of many, where they hang-out for project tasks.

The contextual adjustment is where the curator has to work on a cultivation process that glues the team together. The shared terminology within a group conversation, is what match their practices together. At inception, the curator picks a bouquet of on-topic terms from the controlled vocabularies. Mixing this with everyday use, and contributions from all members, this can be the fruitful and semantically-enhanced conversations with end-user generated tags or “folksonomies”. The same goes for interior design of links, tools, chosen content types and other forms of artifacts that the team will be needing to fulfill their goals and outcome.

The governance of the habitat, leans very much on the shared experiences in the group, and assigned responsibilities for stewardship and curation – where publishing standards, guidelines and training should be part of the mix.

We will cover more on governance and how content should be managed in the cloud in our next post.
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Housekeeping rules within the Habitat

This is the third post in a series (1, 2, 4, 5, 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.

 In the first post we set out the most common challenges you are likely to face and how you may overcome these.  In the second post we focused on how Office 365 and SharePoint can play a part in moving to the cloud.  Here we cover how they can help join up your organisation online using their collaboration tools and features.


When arranging the habitat, it is key to address the theme of collaboration. Since each of these themes, derives different feature settings of artifacts and services. In many cases, teamwork is situated in the context of a project. Other themes for collaboration are the line of business unit teamwork, or the more learning networks a.k.a communities of practice. I will leave these later themes for now.

Most enterprises have some project management process (i.e. PMP) that all projects do have to adhere to, with added complementary documentation, and reporting mechanisms. This is so the leadership within the organisation will be able to align resources, govern the change portfolio across different business units. Given this structure, it is very easy to depict measurable outcomes, as project documents have to be produced, regardless of what the project is supposed to contribute towards.

The construction of a habitat, or design of a joint workplace, all boils down to pragmatic steps that are aligned with the overarching project framework at hand. Answering a few simple Questions (Inverted Pyramid):

  • Who? will be participating, who will own (organisation) the outcome from the joint effort pulling together a project (dc.contributor ; dc.creator ; dc.provenance ) and reach ( dc.coverage ; dc.audience )
  • What? is the project all about, topic and theme (dc.subject ; dc.title ; dc.description, dc.type )
  • When? will this project be running, and timeline for ending the project. All temporal themes around the life of a project. (
  • Where? will participants contribute. What goes where and why? (dc.source ; dc.format ; dc.identifier )
  • Why? usually defined in project description, setting common ground for the goals and expected outcome. ( dc.description )
  • How? defines used processes, practices and tools to create the expected outcome for the project, with links to common resources as the PMP framework, but also links to other key data-sets. Like ERP record keeping and masterdata, for project number and other measures not stored in the habitat, but still pillars to align to the overarching model. (dc.relation)

When these questions have been answered, the resource description for the habitat is set. In Sharepoint the properties bag (code) feature. During the lifespan of the on-going project, all contribution, conversations and creation of things can inherit rule-based metadata for the artifacts from the collections resource description. This reduces the burden weighing on the actors building the content, by enabling automagic metadata completion where applicable. And from the wayfinding, and findability within and between habitats, these resource descriptions will be the building blocks for a sustainable information architecture.

In our next post we will cover how to encourage employee engagement with your content.

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
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Wagon Trains to the Cloud

This is the first post in a series(2, 3, 4, 5, 6, 7) on the challenges organisations face when 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 you on the steps you need to take.

In this first post we show you  the most common challenges that you are likely to face and how you may overcome these.

A fast migration path, to become tenants in a cloud apartment housing unfolds a set of business critical issues that have to be mitigated:

  • Wayfinding in a maze of content buckets and social habitats.
  • Emerging digital Ghost Towns due to lack of information governance.
  • Digital Landfills without organising principles for information and data.
  • Digital Litter with little or no governance or principles for ownership, with redundant, outdated and trivial (ROT) content.
  • With no strategy or plan, erodes any possibility to positive business outcome from moving to the clouds.

WagonTrn” by Tillman at en.wikipedia – Transferred from en.wikipedia by SreeBot. Licensed under Public domain via Wikimedia Commons.

The way forward is to settle a sustainable information architecture, that supports an information environment in constant flux. With information and data interoperable on any platform, everywhere, anytime and on any device.

You need to show how everything is managed and everyone fits together.  A governance framework can help do this.  It can show who is responsible for the intranet, what their responsibilities are and fit with the strategy and plan.  Making it available to everyone on the intranet helps their understanding of how it is managed and supports the business.

The main point is to have a governance framework and information architecture with the same scope to avoid gaps in content being managed or not being found.

Both need to be in harmony and included in any digital strategy.  This avoids competing information architectures and governance frameworks being created by different people that causes people to have inconsistent experiences not finding that they need and using alternative, less efficient, ways in future to find what they need to help with their work.


Building huts, houses and villages is an emerging social construction. As humans we coordinate our common resources, tools and practices. A habitat populated by people needs housekeeping rules with available resources for cooking, cleaning, social life and so on. Routines that defines who does what task and by when in order to keep everything ok.

A framework with governing principles that set out roles and responsibilities along with standards that set out the expected level of quality and quantity of each task that everyone is engaged and complies with, is similar to how the best intranets and digital workplaces are managed.

In the early stages with a small number of habitats the rules for coordination are pretty simple, both for shared resources between the groups and pathways to connect them. The bigger a village gets, it taxes the new structures to keep things smooth. When we move ahead into mega cities with 20+ million people living close, it boils down to a general overarching plan and common infrastructures, but you also need local networked communities, in order to find feasible solutions for living together.

Like villages and mega cities there is a need for consistency that helps everyone to work and live together.  Whenever you go out you know that there are pavements to walk on, roads for driving, traffic lights that we stop at when they turn red and signs to help us show the easiest way to get to our destination.

Sustainable architecture and governance creates a consistent user experience. A well structured information architecture that is aligned with a clear governance framework sets out roles and responsibilities. Publishing standards based on business needs that supports the publishers follow them. This means wherever content is published, whether it is accredited or collaborative, it will appear to be consistent to people and located where they expect it to be.  This encourages a normal way to move through a digital environment with recognizable headings and consistently placed search and other features.

This allegori, fits like a glove when moving into large enterprise-wide shared spaces for collaboration. Whether it is cloud based, on-premises or a mix thereof. The social constructions and constraints still remain the same. As an IT-services on tap, cloud, has certainly constraints for a flexible and adjustable habitual construction to be able to host as many similar habitats as possible. But offers a key solutions to instantly move into! Tenants share the same apartment building (Sharepoint online).

When the set of habitats grow, navigation in this maze becomes a hazard for most of us. Wayfinding in a digital mega city, is extremely difficult. To a large extent, enterprises moving into collaboration suites suffer from the same stigma. Regardless if it is SharePoint, IBM Connections, Google Apps for Work, or a similar setting. It is not a discussion of which type of house to choose, but rather which architecture and plan that work in the emerging environment.

Information Architecture for Digital Habitats

If one leans upon linked-data,  linked-open-data, and emerging semantic web and web of data standards, there are a set of very simple guidelines that one should adhere to when building a Digital Village or Mega City. The 5 stars, our beacon of light!

All collections and shared spaces, should have persistent URI:s, which is the fourth star in the ladder. When it comes to the third star of non-proprietary formats it obviously becomes a bit tricky, since i.e. MS Sharepoint and MS Office like to encourage their own format to things. But if one add resource descriptions to collections and artifacts using Dublin Core elements, it will be possible to connect different types of matter. With feasible and standardised resource descriptions it will be possible to add schemas and structures, that can tell us a little bit more about the artifacts or collection thereof. Hence the option to adhere to the second star. The first star, will inside the corporate setting become key to connect different business units, areas with open licenses and with restrictions to internal use only and in some cases open for other external parties.

Linking data-sets, that is collections or habitats, with different artifacts is the fifth star. This is where it all starts to make sense, enabling a connected digital workplace. Building a city plan, with pathways, traffic signals and rules, highways, roads, neighborhoods  and infrastructural services and more. In other words, placemaking!

Placemaking is a multi-faceted approach to the planning, design and management of public spaces. Placemaking capitalizes on a local community’s assets, inspiration, and potential, with the intention of creating public spaces that promote people’s health, happiness, and well being.

We will cover more about how this applies to Office 365 and SharePoint in our next post.

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