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

Enterprise Graph Search

Facebook will soon launch their new Graph Search to the general public, and it has received a lot of interest lately.

With graph search, the users will be able to query the social graph that millions of people have constructed over the years when friending each other and putting in more and more personal information about themselves and their friends in the vast Facebook database. It will be possible to query for friends of friends who have similar interests as you, and invite them to a party, or to query for companies where people with similar beliefs as you work, and so on and so forth. The information that is already available, will all the sudden become much more accessible through the power of graph search.

How can we bring this to an enterprise search environment? Well, there are lots of graphs in the enterprise as well to query, both social and other types. For example, how about being able to query for people that have been members of a project in the last three years that involved putting a new product successfully to the market. This would be an interesting list of people to know about, if you’re a marketing director that want to assemble a team in the company, to create a new product and make sure it succeeds in the market.

If we dissect graph search, we will find three important concepts:

  1. The information we want to query against don’t only need to be indexed into one central search engine, but also the relations and attributes of all information objects need to be normalized to create the relational graph and have standard attributes to query against. We could use the Open Graph Protocol as the foundation.
  2. We need a parser that take human language and converts it to a formal query language that a search engine understands. We might want to query in different human languages as well.
  3. The presentation of results should be adapted to the kind of information sought for. In Facebook’s example, if you query for people you will get a list of people with their pictures and some relevant personal information in the result list, and if you query for pictures you will get a collage of pictures (similar to the Google image search).

So the recipe to success is to give the information management part of the project a big focus, making sure to create a unified information model of the content to be indexed. Then create a query parser for natural language based on actual user behavior, and the same user studies would also give us information on how to visualize the different result set types.

I believe we will see more of these kind of solutions in the coming years in the enterprise search market, and look forward exploring the possibilities together with our clients.

Tutorial: Optimising Your Content for Findability

This tutorial was done on the 6th of November at J. Boye 2012 conference in Aarhus Denmark. Tutorial was done by Kristian Norling.

Findability and Your Content

As the amount of content continues to increase, new approaches are required to provide good user experiences. Findability has been introduced as a new term among content strategists and information architects and is most easily explained as:

“A state where all information is findable and an approach to reaching that state.”

Search technology is readily used to make information findable, but as many have realized technology alone is unfortunately not enough. To achieve findability additional activities across several important dimensions such as business, user, information and organisation are needed.

Search engine optimisation is one aspect of findability and many of the principles from SEO works in a intranet or website search context. This is sometimes called Enterprise Search Engine Optimisation (ESEO). Getting findability to work well for your website or intranet is a difficult task, that needs continuos work. It requires stamina, persistence, endurance, patience and of course time and money (resources).

Tutorial Topics

In this tutorial you will take a deep dive into the many aspects of findability, with some good practices on how to improve findability:

  • Enterprise Search Engines vs Web Search
  • Governance
  • Organisation
  • User involvement
  • Optimise content for findability
  • Metadata
  • Search Analytics

Brief Outline

We will start some very brief theory and then use real examples and also talk about what organisations that are most satisfied with their findability do.

Experience level

Participants should have some intranet/website experience. A basic understanding of HTML, with some previous work with content management will make your tutorial experience even better. A bonus if you have done some Search Engine Optimisation (SEO) for public websites.

Approaches for Building a Business Case for Enterprise Search

Approaches for Identifying Information Access Needs and to Build a Business Case for Enterprise Search and Findability

We have defined a number of alternative approaches to identify the need and value of search-driven findability to support an organisation or a specific process. In other words, different methods to build a business case for enterprise search in a specific organization or process.

Task oriented

Analysing information access needs in relation to specific work task within a business process (by utilizing e.g. the method developed by Byström/Strindberg or the Customer Carewords method).

Process oriented

Mapping the process flow of sequential and dependent (value-adding) activities and the related information access needs, Analysing the dependencies/accessibility of information systems in the different activities (e.g. by using some kind of Business Process Modeling, like the Astrakan-method).

Decision oriented

Identifying and analysing the decision points and the related information access needs within a process.

Risk oriented

Analysing situations within a process or for decision points where the right information was not available. Or even worse if there only was old and unvalid information available? What would have been the outcome of the situation if the desired/needed information had been available? How can we avoid for this scenario to be repeated? Inspired by Lynda Moulton at LWM Technology Services and Martin White of IntranetFocus.

Effect oriented

Determine the desired effects from search-driven findability and define measuring point to follow up the effects over time. Includes also identification of the related target groups/personas and their information access needs to be fulfilled for the effects to be reached (based on the InUse method and previous work at Ericsson (Case Study) and Forsmark (Case Study). An enhanced variant of this method is currently being developed in a project at Chalmers.

Our ambition is to use these methods to help organisations identify information access needs and findability barriers and to help motivate search investments. The analysis could for example be performed by our Findability Business Consultants as part of an in-depth findability review focusing on either an existing application or a specific business process.

Presentation: The Why and How of Findability

“The Why and How of Findability” presented by Kristian Norling at the ScanJour Kundeseminar in Copenhagen, 6 September 2012. We can make information findable with good metadata. The metadata makes it possible to create browsable, structured and highly findable information. We can make findability (and enterprise search) better by looking at findability in five different dimensions.

Five dimensions of Findability

1. BUSINESS – Build solutions to support your business processes and goals

2. INFORMATION – Prepare information to make it findable

3. USERS – Build usable solutions based on user needs

4. ORGANISATION – Govern and improve your solution over time

5. SEARCH TECHNOLOGY – Build solutions based on state-of-the-art search technology

Information Flow in VGR

The previous week Kristian Norling from VGR (Västra Götaland Regional Council) posted a really interesting and important blog post about information flow. Those of you who doesn’t know what VGR has been up to previously, here is a short background.

For a number of years VGR has been working to give reality to a model for how information is created, managed, stored and distributed. And perhaps the most important part – integrated.

Information flow in VGR

Why is Information Flow Important?

In order to give your users access to the right information it is essential to get control of the whole information flow i.e. from the time it is created until it reaches the end user. If we lack knowledge about this, it is almost impossible to ensure quality and accuracy.

The fact that we have control also gives us endless possibilities when it comes to distributing the right information at the right time (an old cliché that is finally becoming reality). To sum up: that is what search is all about!

When information is being created VGR uses a Metadata service which helps the editors to tag their content by giving keyword suggestions.

In reality this means that the information can be distributed in the way it is intended. News are for example tagged with subject, target group and organizational info (apart from dates, author, expiring date etc which is automated) – meaning that the people belonging to specific groups with certain roles will get the news that are important to them.

Once the information is tagged correctly and published it is indexed by search. This is done in a number of different ways: by HTML-crawling, through RSS, by feeding the search engine or through direct indexing.

The information is after this available through search and ready to be distributed to the right target groups. Portlets are used to give single sign-on access to a number of information systems and template pages in the WCM (Web Content Management system) uses search alerts to give updated information.

Simply put: a search alert for e.g. meeting minutes that contains your department’s name will give you an overview of all information that concerns this when it is published, regardless of in which system it resides.

Furthermore, the blog post describes VGRs work with creating short and persistent URL:s (through an URL-service) and how to ”monitor” and “listen to” the information flow (for real-time indexing and distribution) – areas where we all have things to learn. Over time Kristian will describe the different parts of the model in detail, be sure to keep an eye on the blog.

What are your thoughts on how to get control of the information flow? Have you been developing similar solutions for part of this?

Enterprise Search and Business Intelligence?

Business Intelligence (BI) and Enterprise Search is a never ending story

A number of years ago Gartner coined “Biggle” – which was an expression for BI meeting Google. Back then a number of BI vendors, among them Cognos and SAS, claimed that they were working with enterprise search strategically (e.g. became Google One-box partners). Search vendors, like FAST, Autonomy and IBM also started to cooperate with companies such as Cognos. “The Adaptive Warehouse” and “BI for the masses” soon became buzzwords that spread in the industry.

The skeptics claimed that enterprise search never would be good at numbers and that BI would never be good with text.

Since then a lot a lot has happened and today the major vendors within Enterprise Search all claim to have BI solutions that can be fully integrated (and the other way around – BI solutions that can integrate with enterprise search).

The aim is the same now as back then:  to provide unified access to both structured (database) and unstructured (content) corporate information. As FAST wrote in a number of ‘Special Focus’:

“Users should have access to a wide variety of data from just one, simple search interface, covering reports, analysis, scorecards, dashboards and other information from the BI side, along with documents, e-mail and other forms of unstructured information.”

And of course, this seems appealing to customers. But does access to all information really make us more likely to take the right decisions in terms of Business Intelligence. Gartner is in doubt.

Nigel Rayner, research vice president at Gartner Inc, says that:

”The problem isn’t that they (users) don’t have access to information or tools; they already have too much information, and that’s just in the structured BI world. Now you want to couple it with unstructured data? That’s a whole load of garbage coming from the outside world”.

But he also states that search can be used as one part of BI:

“Part of the problem with traditional BI is that it’s very focused on structured information. Search can help with getting access to the vast amount of structured information you have”

Looking at the discussions going on in forums, in blogs and in the research domain most people seem to agree with Gartner’s view: enterprise search and business intelligence makes a powerful combination, but the integrations needs to be made with a number of things in mind:

Data quality

As mentioned before, if one wants to make unstructured and structured information available as a complement to BI it needs to be of a good quality. Knowing that the information found is the latest copy and written by someone with knowledge of the area is essential. Bad information quality is a threat to an Enterprise Search solution, to a combined BI- and search solution it can be devastating. Having Content Lifecycles in place (reviewing, deleting, archiving etc) is a fundamental prerequisite.

Data analysis

Business Intelligence in traditionally built on pre-thought ideas of what data the users need, whereas search gives access to all information in an ad-hoc manner. To combine these two requires a structured way of analyzing the data. If the unstructured information is taken out of its context there is a risk that decisions are built on assumptions and not fact.

BI for the masses?

The old buzzwords are still alive, but the question mark remains. If one wants to give everyone access to BI-data it has to be clear what the purpose is. Giving people a context, for example combining the latest sales statistics with searches for information about the ongoing marketing activities serves a purpose and improves findability. Just making numbers available does not.

enterprise search and business intelligence dashboard

Business intelligence and enterprise search in a combined dashboard – vision or reality within a near future?

So, to conclude: Gartner’s vision of “Biggle” is not yet fulfilled. There are a number of interesting opportunities for the business to create findability solutions that combines business intelligence and enterprise search, but the strategies for adopting it needs to be developed in order to create the really interesting cases.

Have you come across any successful enterprise search and business intelligence integrations? What is your vision? Do you think the integration between the two is a likely scenario?

Please let us know by posting your comments.

It’s soon time for us to go on summer vacation.

If you are Swedish, Nicklas Lundblad from Google had an interesting program about search (Sommar i P1) the other day, which is available as a podcast.

Have a nice summer all of you!

Wisdom Comes With Knowledge – Let’s Have it at Our Fingertips

Wisdom comes with knowledge – this is our payoff, a generic statement, easy to agree with. But what do we mean with these few words….

The management guru Peter Drucker (www.peter-drucker.com) stated already 2001 that the next society will be a “knowledge society”. Knowledge will be its key resource, and knowledge workers will be the dominant group in its workforce. Its three main characteristics will be:

  • Borderlessness because knowledge travels even more effortlessly than money.
  • Upward mobility, available to everyone through easily acquired formal education.
  • The potential for failure as well as success. Anyone can acquire the “means of production”, i.e. the knowledge required for the job, but not everyone can win.

Together, those three characteristics will make the knowledge society a highly competitive one, for organisations and individuals alike. Information technology, although only one of many new features of the next society, is already having one hugely important effect: it is allowing information to spread near-instantly, and making it accessible to everyone.

But the important thing in the knowledge society is of course to find the right information at the right time. This is the success factor, to turn information into knowledge that could be built up for each separate task or mission. This requires a good way to extract the useful information. A good search engine could give you this, the ability to dynamically find, extract, structure and understand information and to turn information into knowledge.

We call this “dynamic knowledge”, easy to achieve through search solutions, suitable for each situation. This is a way to feel wise and well informed in the knowledge society. The ability to get the right information for each and every situation will give you the ability to have “dynamic knowledge at your fingertips”.