Toward data-centric solutions with Knowledge graphs

Posted on December 17, 2019 by Fredric Landqvist

In the last blog posts [1, 2] in this series by Fredric Landqvist and Peter Voisey we have outlined for you, at a high level, about the benefits of making data smarter and F.A.I.R., ideally made findable through a shareable, but controlled, type of Information Commons. In this post, we introduce you to Knowledge Graphs (based on Semantic Web Technologies), the source for the magic of smart and FAIR data automation. Data that is findable, accessible, interoperable and reusable. They can help tackle a range of problems, from the data tsunami to the scarcity of (quality) data for that next AI project.

What is a Knowledge Graph?

There are several different types of graph and certainly many have been many attempted definitions of a Knowledge Graph. Here’s ours:

A Knowledge Graph is the structural representation of explicit knowledge for a domain, encoded in such a way that both humans and machines can read (process) it.

Ultimately, we are wanting to exploit data and their connections or relationships within the graph format in order to surface important and relevant data and information. Without these relationships, the understandings, the stories and the searches around our data tend to dry up fairly quickly. Our world is increasingly connected. So we hope, from an organisational perspective, you are asking: Why isn’t our data connected?!

Where does the term “Knowledge Graph” come from?

The term Knowledge Graph was coined by Google on the release of its own Knowledge Graph in 2012. More recently, organisations have been cottoning on to the collective benefits of employing a Knowledge Graph, so much so, that many refer to the Enterprise Knowledge Graph today.

What are the technologies behind the Enterprise Knowledge Graph?

The Enterprise Knowledge Graph is based on a stack of W3C-ratified Semantic Web Technologies. As their name alludes to, they form the basis of the Semantic Web. Their formulation began in 2001 with Sir Tim Berners-Lee. Sir Tim, not content with giving us the World Wide Web for free, pictured a web of connected data and concepts, besides the web of linked documents, so that machines would be able to understand our requests by virtue of known connections and relationships.

Why Enterprise Knowledge Graphs now?

These technologies are complex to the layperson and some of them are nearly 20 years old. What’s changed to make Enterprises take note of them now? Well worsening internal data management problems, the need for some knowledge input for most sustainable AI projects and the fact that Knowledge Graph building tools have improved to become collaborative and more user-friendly for the knowledge engineer, domain expert and business executive. The underlying technologies in new tools are more hidden from the end user’s perspective, allowing them to concentrate on encoding their knowledge so that it can be used across enterprise systems and applications. In essence, linking enterprise data.

Thanks to Google’s success in using their Knowledge Graph with their search, Enterprise Knowledge Graphs are becoming recognised as the difference between “googling” and using the sometimes-less-than-satisfying enterprise consumer-facing or intranet search.

The key takeaway here though is that real power of any knowledge graph is in its relationships/connections between concepts. We’ll look into this in more detail next.

RDF, at the heart of the Enterprise Knowledge Graphs (EKGs)

EKGs use the simple RDF graph data model at their base. RDF stands for Resource Description Framework – a framework for the way resources or things are described so that we can recognise more easily plus understand more about them.

An aside: We’re talking RDF (namespace) Knowledge Graphs here, rather than their sister graph type, Property Graphs, which we will cover in a future post. It is important to note that there are advantages with both types of graph and indeed new technologies are being developed, so processes can straddle both types.

The RDF graph data model describes a thing or a resource in terms of “triples”: Subject – predicate – Object. The diagram below illustrates this more clearly with an example.

*Figure 1. What does a Knowledge Graph look like? The RDF elements of a Knowledge Graph*

The graph consists of nodes (vertices) that represent entities (a.k.a. concepts both concrete and abstract, terms, phrases, but now think things, not strings), and edges (lines or arrows) representing the relationships between nodes. Each concept and each relationship have their own URI (a kind of ID), that helps a search engine or application understand their meaning to spot differences (disambiguation) e.g. homonyms (words spelt or pronounced similarly, but that have different meaning) or similarities e.g. alternative labels, synonyms, acronyms, misspellings, foreign language term equivalents etc.

Google uses its Knowledge Graph when it crawls websites to recognise entities like: People, Places, Products, Organisations and more recently Topics, plus all their known relationships between them. There is often a dire need within most organisations for readily available knowledge about People and their related Roles, Skills/Competencies, Projects, Organisations/Departments and Locations.

There are of course many other well-known Knowledge Graphs now including IBM’s Watson, Microsoft’s Academic Knowledge Graph, Amazon’s Cortex Knowledge Graph, the Bing Knowledge Graph etc.

One thing to note about Google is that the space devoted to their organic (non-paid for) search results has reduced dramatically over the last ten years. In place, they have used their Knowledge Graph to better understand the end user’s query and context. Information too is served automatically based on query concept relationships, either within an Information Panel or as commonly known Questions and Answers (Q&As). Your employees (as consumers) of course are at home with this intuitive, easy-click user experience. While Google’s supply of information has become sharper, so has its automatic assessment of all webpage content, relying increasingly on websites to provide it with semantic information e.g. declaring their “aboutness” by using schema.org or other microformats in their markup rather than relying on SEO keywords.

How does Knowledge Graph engineering differ from traditional KM/IM processes?

In reality, not that much. We still want the same governing principles that can give data good structure, metadata, context and meaning.

Constructing a Knowledge Graph can still be likened to the development of taxonomy or thesaurus with their concepts and an ontology (the relationships between concepts). Here the relationships include firstly: poly-hierarchical relationships (in terms of the taxonomy): a concept may have several broader concepts meaning that the concept itself (with its own URI) can appear in multiple times within a taxonomy. This polyhierarchy can be exploited later for example in both search filtering and website navigation.

Secondly, relationships can also be associative/relational with regards to meaning and context – your organisation’s own made +/or industry-adopted concepts and the key relationships that define your business, and even its goals, strategy and workflows.

A key difference though is the way in which you can think about your data and its organisation. It is no longer flat or 2-D, but rather think 3-D and 360-degree concept- or consumer-centric views to see how they connect to other concepts.

A semantic layer for Automatic Annotation, smarter data & semantic search

We will look at the many different benefits of a Knowledge Graph and further use cases in the next post, but for now, we go with the magic that an EKG can sit virtually on top of any or all your data sources (with different formats and metadata) without the need to move or copy any data. Any data source or data catalogue then consumed via a processing pipeline can be automatically and consistently be annotated (“tagged”) and classified according to declared industry or in-house standards, thus becoming more structured and its meaning more readily “understood,” ready to be found and consumed in accordance with any known or stated conditions.

The classification may also extend to including levels of data security and sensitivity, provenance or trust or location, device and time accessibility.

Figure 2 The automatic annotation & classification process for making data/content smart by using an Enterprise Knowledge Graph

It’s often assumed, incorrectly, that there is only one Enterprise Knowledge Graph. Essentially an enterprise can have one or many, perhaps overlapping graphs for different purposes, subject domains or applications. The importance is that knowledge becomes encoded and readily usable for humans and machines.

What’s wrong with Relational Databases?

There’s nothing wrong with relational databases per se and Knowledge Graphs will not necessarily replace them any time soon. It’s good to note though that data in tabular format can be converted to RDF graph data (triples/tuples) relatively easily and stored in a triple store (Graph Database) or some equivalent.

In relational databases, references to other rows and tables are indicated by referring to primary key attributes via foreign key columns. Joins are computed at query time by matching primary and foreign keys of all rows in the connected tables.

Understanding the connections or relations is usually very cumbersome, and those types of costly join operations are often addressed by denormalizing the data to reduce the number of joins necessary, therefore breaking the data integrity of a relational database.

The data models for relational versus graph are different. If you are used to modelling with relational databases, remember the ease and beauty of a well-designed, normalized entity-relationship diagram (i.e using UML) – a graph is exactly that – a clear model of the domain. Each node (entity or attribute) in the graph model directly and physically contains a list of relationship records that represent the relationships to other nodes. These relationship records are organized by type and direction and may hold additional attributes.

Querying relational databases is easy with SQL. The graph has something similar by using SPARQL, a query language for RDF. If you have ever tried to write a SQL statement with a large number of joins, you know that you quickly lose sight of what the query actually does. In SPARQL, the syntax remains concise and focused on domain components and the connections among them.

Toward data-centric solutions with RDF

With enterprise-linked-data, as with knowledge graphs, one is able to connect many different schemas (data models) and formats in different relational databases and build a connected worldview, domain of discourse. Herein lays the strengths with linking-data, and liberating data from lock-in mechanisms either by schemas (data models) or vendor (software). To do queries and inferencing to find new knowledge and insights that were not possible before due to time or human computation factors. Semantics support this reasoning!

Of course, having interoperable graph data means could well mean fewer code patches on individual systems and more sustainable and agile data-centric solutions in the future.

In conclusion

The expression “in the right place, at the right time” is generally associated with luck. We’ve been talking in our enterprises about “the right information, in the right place, at the right time” for ages, unfortunately sometimes with similar fortune attached. The opportunity is here now to embark on a journey to take back control of your data if you haven’t already, and make it an asset again in achieving your enterprise aims and goals.

More reading on graphs and linked enterprise data:

Everything you need to know about graph technology and deep-learning
Linking Enterprise Data, ed. David Wood 2011 (book)

Next up in the series: Knowledge Graphs: The collective Why?

Fredric Landqvist research blog

Peter Voisey

Making your data F.A.I.R and smart

Posted on November 20, 2019 by Fredric Landqvist

This is the second post in a new series by Fredric Landqvist & Peter Voisey, explaining how your organisation could best shape its data landscape for the future.

How to create a smart data framework for your organisation

In our last post for you, we presented the benefits of F.A.I.R data, how to make data smarter for search engines and the potentials of an Information Commons. In this post, we’re giving you the pragmatic steps to make your data FAIR by creating and applying your own smart data framework. Your data-sharing dream, internally and externally, is possible.

A smart data framework, using FAIR data principles, encompasses the tooling, models and standards that govern datasets and the different context-specific information systems (registers, catalogues). The data is then ingested and processed (enriched/refined) into smart data, datasets and data catalogues. It can then be used and reused by different applications and e-services via open APIs. In this ecosystem, all actors and information behaviours (personas) interplay: provision agents, owners, builders, enrichers, end-user searchers and referrers.

A smart data & metadata catalogue

A smart data & metadata catalogue (illustrated below), provides an organisational capability that aligns data management with the FAIR data principles. View it not so much as one system to rule them all, but rather an ecosystem that is smart and sustainable. In order to simplify your complex and heterogeneous information environment, this set-up can be instantiated, as one overarching mechanism. Although we are describing a data and metadata catalogue here, the exact same framework and set up would of course apply also to your organisation’s content, making it smarter and more findable (i.e. it gets the sustainable stamp).

Smart Data Catalogue — The necessary services and component of a smart data catalogue

The above picture illustrates the services and components that, together, build smart data and metadata catalogue capabilities. We now describe each one of them for you:

Processing (Ingestion & Enrichment) for great Findability & Interoperability

(A) Ingest, harvest and operate. Here you connect the heterogeneous data sources for ingestion.

The configured input mechanisms describe each of the data sources, with their data, datasets and metadata ready for your catalogue search. Hopefully, at the dataset upload stage, you have provided a good system/form that now provides your search engine with great metadata (i.e. we recommend you use the open data catalogue standard DCAT-AP). The concept upload is interchangeable with either machine-to-machine harvester mechanisms, as with open-data, traditional data integration, or manual provision by human upload effort. (D) Enterprise Metadata Repository: here is the persistent storage of data in both data catalogue, index and graph. All things get a persistent ID (how to design persistent URI) and rich metadata.

(B) Enrich, refine analyze, and curate. This is the AI part (NLP, Semantics, ML) that enriches the data and datasets, making them smarter.

Concepts (read also entities, terms, phrases, synonyms, acronyms etc.) from the data sources are found using named entity extraction (NER). By referring to a Knowledge Graph in the Enricher, the appropriate resources are annotated (“tagged”) with the said concept. It does not end here, however. The concept also takes with it from the Knowledge Graph all of the known relationships it has with other concepts.

Essentially a Knowledge Graph is your encoded domain knowledge in a connected graph format. It is by reading these encoded relationships that the machine “understands” the meaning or aboutness of data.

This opens up a very nice Pandora’s box for your search (understanding query intent) and for your Graphical User Interface (GUI) as your data becomes smarter now through your ability to exploit the relationships and connections (semantics and context) between concepts.

You and AI can have a symbiotic relationship in the development of your Knowledge Graph. AI can suggest new concepts and relationships as new data is added. It is, however, you and your colleagues who determine the of concepts/relationships in the Knowledge Graph – concepts/relationships that are important to your department or business. Remember you can utilise more than one knowledge graph, or part of one, for a particular business need(s) or data source(s). The Knowledge Graph is a flexible expression of your business/information models that give structure to all your data and its access.

Extra optional step: If you can manage not only to index the dataset metadata but the datasets themselves, you can make your Pandora’s box even nicer. Those cryptic/nonsensical field names that your traditional database experts love to create can also be incorporated and mapped (one time only!) into your Knowledge Graph, thus increasing the machine “understanding” of the data. Thus, there is a better chance of the data asset being used more widely.

The configuration of processing with your Knowledge Graph can take care of dataset versioning, lineage and add further specific classifications e.g. data sensitivity, user access and personal information.

Lastly on Processing, your cultural and system interoperability is immensely improved. We’re not talking everyone speaking the same language here, rather everyone talking their language (/culture) and still being able to find the same thing. In this open and FAIR vocabularies further, enrich the meaning to data and your metadata is linked. System interoperability is partially achieved by exploiting the graph of connections that now “sit over” your various data sources.

Controlled Access (Accessible and Reusable)

(C) Access, search and visualize APIs. These tools control and influence the delivery, representation, exploration and consumption/use of datasets and data catalogues via a smarter search (made so by smarter data) and a more intuitive Graphical User interface (GUI).

This means your search can now “understand” user intent from just one or two keyword queries (through known relationship connections in the Knowledge Graph).

Your search now also caters for your searchers who are searching in an unfamiliar subject area or are just having a query off day. Besides offering the standard results page, the GUI can also present related information (again due to the Knowledge Graph), past related user queries, information and question-answer (Q&A) type material. So: search, discovery, learning, serendipity.

Your GUI can also now become more intuitive, changing its information presentation and facets/filters automatically, depending on the query itself (more sustainable front-end coding).

An alternative to complex scenario coding also includes the possibility for you to create rules (set in your Knowledge Graph) that can control what data users can access (when, how and where) based on their profile, their role, their location, the time and on the device they are using. This same Knowledge Graph can help push and recommend data for certain users proactively. Accessibility will be possible by using standard communication protocols, open access (when possible), authentication where necessary, and always with metadata at hand.

Reusable: your new smart data framework can help increase the time your Data Managers (/Scientists, Analysts) spend using data (and not trying to find it, the 80/20 data science dilemma). It can also help reduce the risk to your AI projects (50% failure rate) by helping searchers find the right data, with its meaning and context, more easily. Reuse will also be possible with the design that metadata multiple attributes, use licence and provenance in line with community standards

Users and information behaviour (personas)

Users and personas — User groups and services

From experience we have defined the following broad conceptual user-groups:

Data Managers, a.k.a. Data Op’s or Data Scientists
Data Managers are i.e. knowledge engineers, taxonomists and analysts.
Data Stewards
Data Stewards are responsible for Data Governance, such as data lineage.
Business Professionals/Business end-users
Business Users may have a diverse background. Hence Business end-users.
Actor System are different information systems and applications and services that integrate information via the rich open APIs from the Smart Data Catalogue

The outlined collaborative actors (E-H user groups) and their interplay as information behaviour (personas) with the data (repository) and services (components), together, build the foundation for a more FAIR data management within your organisation, providing for you at the same time, the option to contribute to an even broader shared open FAIR information commons.

(E) Data Op’s workplace and dashboard is a combination of tools supporting Data Op’s data management processes in the information behaviours: data provision agents, enrichers and developers.
(F) Data Governance workplace is the tools to support Data Stewards collaborative data governance work with Data Managers in the information behaviours: data owner.
(G) Access, search, visualize APIs, is the user experience to explore, find and interact with the catalogue and data in the information behaviours: searcher and referrer.
(H) API, is the set of open APIs to support access to catalogue data for consuming information systems in the information behaviours: referrer (a.k.a. data exchange).

Potential tooling for this smart data framework:

Search, integration and analytics: Findwise i3, Elastic, Fusion
Semantic tools: PoolParty, Synaptica, Smartlogic, TopQuadrant
Catalogue and integration: Entryscape, Informatica, Stibo, Talend, Marklogic

We hope you enjoyed this post and understand the potential benefits such a smart data framework incorporating FAIR data principles can have on your data catalogue, or for that matter, your organisational content or even your data swamps.

In the next post, Toward data-centric solutions with Knowledge Graphs, we talk about Knowledge Graphs (KG) and its non-proprietary RDF semantic web tech, how you can create your KG(s) and the benefits they can bring to your future data landscape.

Fredric Landqvist research blog

Peter Voisey

Data that really saves lives (and possibly your organisation)

Posted on November 12, 2019 by Fredric Landqvist

This is the first post in a new series by Fredric Landqvist and Peter Voisey, explaining how your organisation could best shape its data landscape for the future.

A Quest for a FAIR Information Commons

You might have heard recently of the phrase, “data that saves lives”. It certainly can, but just as you need to be in shape to do your work, so does data, to work its magic. Data too needs to be shaped by governing principles that we can apply along their life journey, in order that we can reap the consequential rewards and benefits that are there to be had. Data in shape, saves lives.

We all need to fix problems, usually quickly hence the presence of the closed model, data silos and data interoperability. It has had to happen this way, will continue to do so and there’s no shame in that. But if we can be part of a reliable data sharing community, whose data can help us to collaborate and solve better, well, we’d be foolish to turn it down.

So imagine a type of information commons. This isn’t so far-fetched, we just need to widen our horizons and collaborative ecosystem for it to happen, and perhaps take the same model advice internally for our own organisations.

The challenge of really saving lives with data requires new collaborators. As collaborators we require trust. In essence then to be part of this challenge we need to be willing to share data (we use the term data, content, information interchangeably here). Proof of that trust, is to sign an agreement to be part of an information commons, where data has certain principles (a.k.a. terms & conditions, T&Cs). In essence rules of engagement!

Declare interest
Sign a future rules of engagement to share and access data
Get ready to adhere to them

The T&Cs largely apply to the condition of the data being shared and the information about them. They match precisely how you would hope to find data in this new treasure trove. They may also be known as F.A.I.R. – data that is Findable, Accessible, Interoperable and Reusable. FAIR obviously alludes also to the fairness in collaboration and the F.A.I.R data principles originate from a good sharing place.
Here’s a great summary in image form, from Australian National Data Service [ANDS]:

How FAIR is your data today? Simply answer by following a brief checklist or later go for a more comprehensive description at Go FAIR.

Still here? Great! Let’s get started then with Findable!

Findable

We can only truly make data findable when we really think about the range of people who might want to find it and how they might want to use or reuse it (their need determines how they will ask for it). The reality of different data sources, formats, protocols and their possible attributes or descriptors, makes describing data for others problematic, plus, do you really have the time for tagging? Regardless of time, we’re not very good at putting ourselves in somebody else’s shoes (unless of course we’re selling something) and certainly not able to cover the variation in how people (with differing perspectives) search for data.

The best answer we have at the moment is to describe data or datasets by using agreed standards, perhaps that may vary a bit from domain to domain. Sharing or uploading data to the “ether” gives a different feeling to uploading data that matters to a known shared source and accessed by users who understand its value. In doing so it may inspire us to describe data according to a collective standard, with that feeling of having done something good for a bigger cause.

But hang on. Why is the onus on the end-user? We have the tech here now to automate much of this process. We just need a good sharing and upload design that can recognise the (hopefully changeable) standards of description (metadata). By processing data on upload, we can get a better understanding of data with reference to our standards and rules. Thus, according to what the machine recognises (pattern matching) or “understands” (by way of concept relationships in a knowledge graph) it can annotate the data, ready to serve the requests of data searchers and data applications, or at least be able to offer a related alternative.

Such processing is done using AI (NLP, ML etc.), but it’s not magic. We still have to teach our machines the agreed standards and rules in the first place. While that may sound cumbersome to some, it’s not like you keep having to teach them repeatedly. Conversely the student (AI) can also suggest new rules and annotations, keeping them current according to the data being processed. The beauty for most, is though, that we can employ more than one descriptive rule set for different data or datasets. Depending on data source, format and context, the machine can activate different metadata rule sets. The smart part for the uploader is the presentation of a semi-automated metadata form for their data, leaving them to confirm or alter it before hitting send. The “uploader” in this context, is a broad concept to address any agent that contributes with data to the shared information space, be they programmatic or human.

Let’s not forget we’re at the stage where we can use “search” not only for indexing and this automatic annotation, but for calculations on parsing to potentially annotate with even higher understanding. Such a solution fits well with the increasing demand for real-time data too.

So Findable, is really both about making data smarter, and findable.

Accessible

There’s nothing worse than finding something you want, only to be told you can’t use it.

While the premise of an Information Commons is sharing, it doesn’t necessarily have to mean that everything is accessible by everyone – the reason that why some readers left this page at the third paragraph.

Let’s be clever about this. There are lots of ways to automatically control accessibility and automatically police it. This could be technical, IP address, sign on, authorisation (classification of user) etc. But it could also be done by processing data on upload in determining the sensitivity level of data and/or the indicators of GDPR data.

Back to the end-user: they don’t want to see stuff they can’t use, but they also want to see from the go, if they need to get any new software to be able to access data that they are interested in.

Interoperable

Now for the hard part. The reality is that variety in data sources, protocols and formats ain’t going to go away any time soon. We have to accept that. We’ve just mentioned about the technical interoperability in Accessible. There’s also language interoperability (cultural and language) that can again be solved by using a knowledge graph with search (tinkering with knowledge graphs, just like Google does).

Lastly there’s data interoperability. Barriers preventing data and system interoperability are slowly being brought down through collaboration. In the meantime, it is possible for us to convert key data into the same data format, so AI and inferencing can be used on different (previously incompatible) datasets. The kind of thing that can lead to computation-derived insights that a human on their own couldn’t make. Converting data to RDF could be such a point in case, a real lingua franca of data, also connected to the Web.

Reusable

The “F.A.I.” part of FAIR, really already covers Reusable. We want to be able to find data that we can reuse. To do this, we need to be able to see related information on finding content, information, datasets and data catalogues as to the how, what, when, who, why, where of its potential usage. More: working on the shoulders of giants, less: reinventing the wheel. The information (rich metadata) associated with Reusable, also refers to its usefulness: value, age and provenance.

Healthcare Data Commons

There is an emerging FAIR Data paradigm shift within the health informatics and research professional communities, that has been sparked by those within the bio- and life science domains.

There are obvious regulatory constraints when speaking about patient data, or health data, that any data commons arena will have to nail, upfront.

Health Data: quality register data, EHRs data and the patient’s self-created data, together, would be a real gold mine in the pursuit of personalised medicine and health care. Patient-centric data and FAIR data governance will be key.

The outlined scenario for a FAIR Data Commons

The illustration above shows a FAIR data commons. It will be the foundation framework for all information systems (register) in the data ecology. These information systems need to harmonise and align to become FAIR. There is a set of generic agent information behaviour patterns (user personas):

Data provision agent, is an information behaviour with either a human actor that upload (provision data) or machine to machine data integration contributing to the datasets in the register.
Data owner, is an information behaviour relating to governance and ownership, stewardship to the datasets in the register.
Application builder, is an information behaviour relating to building capabilities with the use and reused datasets in the register.
Data enricher, is an information behaviour relating to expanding the models, and enriching the datasets. With i.e. use of linked-data, semantics and more to create richer metadata.
Searcher, is an information behaviour relating to finding and acting upon data.
Referrer, is an information behaviour relating to using data in information flows and data exchange to support different kinds of processes, activities and actions with other actors in the ecology.

The business value realised (effect) using the FAIR Data Commons will be via different means to e-services, used in the scenarios for searcher and referrer, but also in improved efficiency and improved data quality in the other information behaviours.

Next post in the series: Making Your data smart and F.A.I.R. Further reading to help inspire you:

The FAIRsharing.org provides very useful resources as building blocks in the creation of any context-specific data commons.
The National Institute for Health (NIH) in the USA have a Data Commons programme, with on-going pilots.
The Nordic NordForsk recent report on Nordic Commons with HealthData
Similar in the Nordics there are initiatives (Finnish catalog, Swedish register [RUT], HelseData in Norway and Danish Healthdata) coordinated via EU funded research programmes.
The Life Science industry together with Healthcare have some impressive initiatives e.g. Electronic Health Records 4 Clinical Research [EHR4CR], with its information platform, InSite (by TriNetX), in line with FAIR data.

Thoughts collected from LDSV 2019, Semantics 2019 Karlsruhe, HealthData Copenhagen, “Dagar om Lagar” [SFMI] and more…

Fredric Landqvist research blog

Peter Voisey

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

Posted on November 7, 2018 by Fredric Landqvist

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?

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:

Semantic Tools (PoolParty, Semaphore)
Search and Analytics (i3, Elastic Stack)
Data Integration (Marklogic, Biztalk)
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.
Fredric Landqvist research blog
Agnes Molnar SearchExplained

Benevolent & sustainable smart city development

Posted on May 30, 2018 by Fredric Landqvist

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.

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:

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

Fredric Landqvist research blog
Peter Voisey
Martin Kaltenböck
Sebastian Gabler

Trials & Jubilations: the two sides of the GDPR coin

Posted on March 26, 2018 by Fredric Landqvist

We have all heard about the totally unhip GDPR and the potential wave of fines and lawsuits. The long arm of the law and it’s stick have been noted. Less talked about but infinitely more exciting is the other side. Turn over the coin and there’s a whole A-Z of organisational and employee carrots. How so?

Signal Tools

We all leave digital trails behind us, trails about us. Others that have access to these trails can use our data and information. The new European General Data Protection Regulation (GDPR) intends the usage of such Personal Identifiable Information (PII) to be correct and regulated, with the power to decide given to the individual.

Some organisations are wondering how on earth they can become GDPR compliant when they already have a business to run. But instead of a chore, setting a pathway to allow for some more principled digital organisational housekeeping can bring big organisational gains sooner rather than later.

Many enterprises are now beginning to realise the extra potential gains of having introduced new organisational principles to become compliant. The initial fear of painful change soon subsides when the better quality data comes along to make business life easier. With the further experience of new initiatives from new data analysis, NLP, deep learning, AI, comes the feeling: why we didn’t we just do this sooner?

Most organisations have a system(s) in place holding PII data, even if getting the right data out in the right format remains problematical. The organisation of data for GDPR compliance can be best achieved so that it becomes transformed to be part of a semantic data layer. With such a layer, knowing all the related data from different sources you have on Joe Bloggs becomes so much easier when he asks for a copy of the data you have about him. Such a semantic data layer will also bring other far-reaching and organisation-wide benefits.

Semantic Data Layer

For example, heterogeneous data in different formats and from different sources can become unified for all sorts of new smart applications, new insights and new innovation that would have been previously unthinkable. Data can stay where it is… no need to change that relational database yet again because of a new type of data. The same information principles and technologies involved in keeping an eye on PII use, can also be used to improve processes or efficiencies and detect consumer behaviour or market changes.

But it’s not just the business operations that benefit, empowered employees become happier having the right information at hand to do their job. Something that is often difficult to achieve, as in many organisations, no one area “owns” search, making it is usually somebody else’s problem to solve. For the Google-loving employee, not finding stuff at work to help them in their job can be downright frustrating. Well ordered data (better still in a semantic layer) can give them the empowering results page they need. It’s easy to forget that Google only deals with the best structured and linked documentation, why shouldn’t we do the same in our organisations?

Just as the combination of (previously heterogeneous) datasets can give us new insights for innovation, we also observe that innovation increasingly comes in the form of external collaboration. Such collaboration of course increases the potential GDPR risk through data sharing, Facebook being a very current point in case. This brings in the need for organisational policy covering data access, the use and handling of existing data and any new (extra) data created through its use. Such policy should for example cover newly created personal data from statistical inference analysis.

While having a semantic layer may in fact make human error in data usage potentially more possible through increased access, it also provides a better potential solution to prevent misuse as metadata can be baked into the data to classify both information “sensitivity” and control user accessibility rights.

So how does one start?

The first step is to apply some organising principles to any digital domain, be it in or outside the corporate walls [the discipline of organising, Robert Gluschko] and to ask the key questions:

What is being organised?
Why is it being organised?
How much of it is being organised?
When is it being organised?
Where is it being organised?

Secondly start small, apply organising principles by focusing on the low-hanging fruit: the already structured data within systems. The creation of quality data with added metadata in a semantic layer can have a magnetic effect within an organisation (build that semantic platform and they will come).

Step three: start being creative and agile.

A case story

A recent case, within the insurance industry reveals some cues to why these set of tools will improve signals and attention for becoming more compliant with regulations dealing with PII. Our client knew about a set of collections (file shares) where PII might be found. Adding search, and NLP/ML opened up the pandoras box with visual analytic tools. This is the simple starting point, finding i.e names or personal number concepts in the text. Second to this will be to add semantics, where industry standard terminologies and ontologies can further help define the meaning of things.

In all corporate settings, there exist both well-cultivated and governed collections of information resources, but usually also a massive unmapped terrain of content collections, where no one has a clue if there might be PII hidden amongst it. The strategy using a semantic data layer should always be combined with operations to narrowing down the collections to become part of the signalling system – it is generally not a good idea to boil the whole-data-ocean in the enterprise information environment. Rather through such work practices, workers are aware of the data hot-spots, the well-cultivated collections of information and that unmapped terrain. Having the additional notion of PII to contend with will make it that just bit easier to recognise those places where semantic enhancement is needed.

not a good idea to boil the whole-data-ocean

Running with the same pipeline (with the option of further models to refine and improve certain data) will not only allow for the discovery of multiple occurrences of named entities (individuals) but also the narrative and context in which they appear.
Having a targeted model & terminology for the insurance industry will only go to improve this semantic process further. This process can certainly ease what may be currently manual processes or processes that don’t exist because of their manual pain: for example, finding sensitive textual information from documents within applications or from online textual chats. Developing such a smart information platform enables the smarter linking of other things from the model, such as service packages, service units / or organisational entities, spatial data as named places or timelines, or medical treatments, things perhaps currently you have less control over.

There’s not much time before the 25th May and the new GDPR, but we’ll still be here afterwards to help you with a compliance burden or a creative pathway, depending on your outlook.

Alternatively sign up to the joint webinar the 11th of April 3PM CET with Smartlogic & Findwise, to find out more.

Fredric Landqvist research blog
Peter Voisey
James Morris

Digital recycling & knowledge growth

Posted on August 10, 2017 by Fredric Landqvist

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

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

Fredric Landqvist research blog
Peter Voisey

What will happen in the information sector in 2017?

Posted on December 13, 2016 by Webteam

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

Cloud

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

PIM is for storage

Posted on May 18, 2016 by

– Add search for distribution, customization and seamless multichannel experiences.

Retailers, e-commerce and product data
Having met a number of retailers to discuss information management, we’ve noticed they all experience the same problem. Products are (obviously) central and information is typically stored in a PIM or DAM system. So far so good, these systems do the trick when it comes to storing and managing fundamental product data. However, when trying to embrace current trends¹ of e-commerce, such as mobile friendliness, multi-channel selling and connecting products to other content, PIM systems are not really helping. As it turns out, PIM is great for storage but not for distribution.

Retailers need to distribute product information across various channels – online stores, mobile and desktop, spreadsheet exports, subsets of data with adjustments for different markets and industries. They also need connecting products to availability, campaigns, user generated content and fast changing business rules. Add to this the need for closing the analytics feedback loop, and the IT department realises that PIM (or DAM) is not the answer.

Adding search technology for distribution
Whereas PIM is great for storage, search technology is the champ not only for searching but also for distribution. You may have heard the popular Create Once Publish Everywhere? Well, search technology actually gives meaning to the saying. Gather any data (PIM, DAM, ERP, CMS), connect it to other data and display it across multiple channels and contexts.

Also, with the i3²package of components you can add information (metadata) or logic that is not available in the PIM system. This whilst source data stay intact – there is no altering, copying or moving.

Combined with a taxonomy for categorising information you’re good to go. You can now enrich products and connect them to other products and information (processing service). Categorise content according to product taxonomy and be done. Performance will be super high, as content is denormalised and stored in the search engine, ready for multi channel distribution. Also, with this setup you can easily also add new sources to enrich products or modify relevance. Who knows what information will be relevant for products in the future?

To summarise

PIM for input, search for output. Design for distribution!
Use PIM for managing products, not for managing business rules.
Add metadata and taxonomies to tailor product information for different channels.
Connect products to related content.
Use stand-alone components based on open source for strong TCO and flexibility.

References
¹ Gartner for marketers
²The Findwise i3 package of components (for indexing, processing, searching and analysing data) is compatible with the open source search engines Apache Solr and Elasticsearch.

Generational renewal at work – a search challenge

Posted on January 20, 2016 by Webteam

The big generational shift

There have been discussions surrounding the great generational renewal in the workplace for a while. The 50’s generation, who have spent a large part of their working lives within the same company, are being replaced by an agile bunch born in the 90’s. We are not taken by tabloid claims that this new generation does not want to work, or that companies do not know how to attract them. What we are concerned with is that businesses are not adapting fast enough to the way the new generation handle information to enable the transfer of knowledge within the organisation.

Working for the same employer for decades

Think about it for a while, for how long have the 50’s generation been allowed to learn everything they know? We see it all the time, large groups of employees ready to retire, after spending their whole working lives within the same organisation. They began their careers as teenagers working on the factory floor or in a similar role, step by step growing within the company, together with the company. These employees have tended to carry a deep understanding of how their organisation work and after years of training, they possess a great deal of knowledge and experience. How many companies nowadays are willing to offer the 90’s workers the same kind of journey? Or should they even?

2016 – It’s all about constant accessibility

The world is different today, than 50 years ago. A number of key factors are shaping the change in knowledge-intense professions:

Information overload – we produce more and more information. Thanks to the Internet and the World Wide Web, the amount of information available is greater than ever.
Education has changed. Employees of the 50’s grew up during a time when education was about learning facts by rote. The schools of today focus more on teaching how to learn through experience, to find information and how to assess its reliability.
Ownership is less important. We used to think it was important to own music albums, have them in our collection for display. Nowadays it’s all about accessibility, to be able to stream Spotify, Netflix or an online game or e-book on demand. Similarly we can see the increasing trend of leasing cars over owning them. Younger generations take these services and the accessibility they offer for granted and they treat information the same way, of course. Why wouldn’t they? It is no longer a competitive advantage to know something by heart, since that information is soon outdated. A smarter approach of course is to be able to access the latest information. Knowing how to search for information – when you need it.

Factors supporting the need for organising the free flow of the right information:

Employees don’t stay as long as they used to in the same workplace anymore, which for example, requires a more efficient on boarding process. It’s no longer feasible to invest the same amount of time and effort on training one individual since he/she might be changing workplace soon enough anyway.
It is much debated whether it is possible to transfer knowledge or not. Current information on the other hand is relatively easy to make available to others.
Access to information does not automatically mean that the quality of information is high and the benefits great.

Organisations lack the right tools

Knowing a lot of facts and knowledge about a gradually evolving industry was once a competitive advantage. Companies and organisations have naturally built their entire IT infrastructure around this way of working. A lot of IT applications used today were built for a previous generation with another way of working and thinking. Today most challenges involve knowing where and how to find information. This is something we experience in our daily work with clients. Organisations more or less lack the necessary tools to support the needs of the newer generation in their daily work.

To summarize the challenge: organisations need to be able to supply their new workforce with the right tools to constantly find (and also manipulate) the latest and best information required for them to shine.

Success depends on finding the right information

In order for the new generation to succeed, companies must regularly review how information is handled plus the tools supporting information-heavy work tasks.

New employees need to be able to access the information and knowledge left by retiring employees, while creating and finding new content and information in such a way that information realises its true value as an asset.

Efficiency, automation… And Information Management!

There are several ways of improving efficiency, the first step is often to investigate if parts, or perhaps the entire creating and finding process can be automated. Secondly, attack the information challenges.

What kind of information is it?
Where is the information located?
What is important, the information objects in their entirety or the subsets?
How will the information be consumed?
What prior knowledge is needed to interpret the information?
How much information is out dated or distorting? (Only about 30% of the information within a company is believed to be of actual importance.) (http://www.forbes.com/sites/ciocentral/2012/07/17/defensible-disposal-you-cant-keep-all-your-data-forever/)

When we get a grip of the information we are to handle, it’s time to look into the supporting IT systems. How are employees supposed to find what they are looking for? How do they want to?

We have gotten used to find answers by searching online. This is in the DNA of the 90’s employee. By investing in a great search platform and developing processes to ensure high information quality within the organisation, we are certain the organisation will not only manage the generational renewal but excel in continuously developing new information centric services.

Written by: Maria “Ia” Björk & Joar Svensson