Time of intelligence: from Lucene/Solr revolution 2017

Lucene/Solr revolution 2017 has ended with me, Daniel Gómez Villanueva, and Tomasz Sobczak from Findwise on the spot.

First of all, I would like to thank LucidWorks for such a great conference, gathering this talented community and engaging companies all together. I would also like to thank all the companies reaching out to us. We will see you all very soon.

Some takeaways from Lucene/Solr revolution 2017

The conference basically met all of my expectations specially when it comes to the session talks. They gave ideas, inspired and reflected the capabilities of Solr and how competent platform it is when it comes to search and analytics.

So, what is the key take-away from this year’s conference? As usual the talks about relevance attract most audience showing that it is still a concern of search experts and companies out there. But what is different in this years relevance talks from previous years is that, if you want to achieve better result you need to add intelligent layers above/into your platform to achieve it. It is no longer lucrative nor profitable to spend time to tune field weights and boosts to satisfy the end users. Talk from Home Depo: “User Behaviour Driven Intelligent Query Re-ranking and Suggestion”, “Learning to rank with Apache Solr and bees“ from Bloomberg,  “An Intelligent, Personalized Information Retrieval Environment” from Sandia National Laboratories, are just a few examples of many talks where they show how intelligence comes to rescue and lets us achieve what is desired.

Get smarter with Solr

Even if we want to use what is provided out of the box by Solr, we need to be smarter. “A Multifaceted Look at Faceting – Using Facets ‘Under the Hood’ to Facilitate Relevant Search” by LucidWorks shows how they use faceting techniques to extract keywords, understand query language and rescore documents. “Art and Science Come Together When Mastering Relevance Ranking” by Wolters Kluwer is another example where they change/tune Solr default similarity model and apply advanced index time boost techniques to achieve better result. All of this shows that we need to be smarter when it comes to relevance engineering. The time of tuning and tweaking is over. It is the time of intelligence, human intelligence if I may call it.

Thanks again to LucidWorks and the amazing Solr Community. See you all next year. If not sooner.

Writer: Mohammad Shadab, search consultant / head of Solr and fusion solutions at Findwise

SharePoint Framework vs Sharepoint apps vs Sharepoint solutions

  • “I’m so confused with all this SharePoint Framework, apps, solutions…I just wanted to develop for Sharepoint!”
  • “What can I use SharePoint Framework (SPFx) for? Can I use it for branding?”
  • “When should I pick custom SharePoint solution over SharePoint Framework or SharePoint App?
  • “How can I make elevated privilages in sharepoint hosted app?”

During our years of experience in SharePoint development we’ve seen those questions many times. They were asked by IT devs of our clients, by users of tech blogs/forums and also by ourselves (yes, we’re learning all the time!). Since you’re here we assume that you are confused a little bit too but don’t worry. We know what you feel. That’s why we’ve created this post.

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Web crawling is the last resort

Data source analysis is one of the crucial parts of an enterprise search deployment project. Search engine results quality strongly depends on an indexed data quality. In case of web-based sources, there are two basic ways of reaching the data: internal and external. Internal method involves reading the data directly from its storage place, such as a database, filesystem files or API. Documents are read by some criteria or all documents are read, depending on requirements. External technique relies on reading a rendered HTML with content via HTTP, the same way as it is read by human users. Reaching further documents (so called content discovery) is achieved by following hyperlinks present in the content or with a sitemap. This method is called a web crawling.

The crawling, in contrary to a direct source reading, does not require particular preparations. In a minimal variant, just a starting URL is required and that’s it. Content encoding is detected automatically, off the shelf components extract text from the HTML. The web crawling may appear as a quick and easy way to collect a content to be indexed. But after deeper analysis, it turns out to have multiple serious drawbacks.

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Digital recycling & knowledge growth

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

Start making sense, and the art of making sense!

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

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

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

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

Automation

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

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

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

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

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

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

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

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

Augmentation

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

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

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

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

Sustainable Organising Principles to the Digital Workplace

 


View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog
View Peter Voisey's LinkedIn profilePeter Voisey

Choosing an Open Source search engine: Solr or Elasticsearch?

There has never been a better time to be a search and open source enthusiast than 2017. Far behind are the old days of information retrieval being a field only available for academia and experts.

Now we have plenty of search engines that allow us not only to search, but also navigate and discover our information. We are going to be focusing on two of the leading search engines that happed to be open source projects: Elasticsearch and Solr.

Comparison (Solr and Elasticsearch)

Organisations and Support

Solr is an Apache sub-project developed parallelly along Lucene. Thanks to this it has synchronized releases and benefits directly from any new Lucene feature.

Lucidworks (previously Lucid Imagination) is the main company supporting Solr. They provide development resources for Solr, commercial support, consulting services, technical training and commercial software around Solr. Lucidworks is based in San Francisco and offer their services in the USA and all rest of the world through strategic partners. Lucidworks have historically employed around a third of the most active Solr and Lucene committers, contributing most of the Solr code base and organizing the Lucene/Revolution conference every year.

Elasticsearch is an open-source product driven by the company Elastic (formerly known as Elasticsearch). This approach creates a good balance between the open-source community contributing to the product and the company making long term plans for future functionality as well as ensuring transparency and quality.

Elastic comprehends not only Elasticsearch but a set of open-source products called the Elastic stack: Elassticsearch, Kibana, Logstash, and Beats. The company offers support over the whole Elastic stack and a set of commercial products called X-Pack, all included, in different tiers of subscriptions. They offer trainings every second week around the world and organize the ElasticON user conferences.

Ecosystem

Solr is an Apache project and by being so it benefits from a large variety of apache projects that can be used along with it. The first and foremost example is its Lucene core (http://lucene.apache.org/core/) that is released on the same schedule and from which it receives all its main functionalities. The other main project is Zookeper that handles SolrCloud clusters configuration and distribution.

On the information gathering side there is Apache Nutch, a web crawler, and Flume , a distributed log collector.

When it comes to process information, there are no end to Apache projects, the most commonly used alongside Solr are Mahout for machine learning, Tika for document text and metadata extraction and Spark for data processing.

The big advantage lies in the big data management and storage, with the highly popular Hadoop  library as well as Hive, HBase, and Cassandra databases. Solr has support to store the index in a Hadoop Highly Distributed File System for high resilience.

Elasticsearch is owned by the Elastic company that drives and develops all the products on its ecosystem, which makes it very easy to use together.

The main open-source products of the Elastic stack along Elasticsearch are Beats, Logstash and Kibana. Beats is a modular platform to build different lightweight data collectors. Logstash is a data processing pipeline. Kibana is a visualization platform where you can build your own data visualization, but already has many build-in tools to create dashboards over your Elasticsearch data.

Elastic also develop a set of products that are available under subscription: X-Pack. Right now, X-Pack includes five producs: Security, Alerting, Monitoring, Reporting, and Graph. They all deliver a layer of functionality over the Elastic Stack that is described by its name. Most of them are included as a part of Elasticsearch and Kibana.

Strengths

Solr

  • Many interfaces, many clients, many languages.
  • A query is as simple as solr/select?q=query.
  • Easy to preconfigure.
  • Base product will always be complete in functionality, commercial is an addon.

Elasticsearch

  • Everything can be done with a JSON HTTP request.
  • Optimized for time-based information.
  • Tightly coupled ecosystem.

Base product will contain the base and is expandable, commercial are additional features.

solr vs elasticsearch comparison open source search engine

Conclusion – Solr or Elasticsearch?

If you are already using one of them and do not explicitly need a feature exclusive of the other, there is no big incentive in making a migration.

In any case, as the common answer when it comes to hardware sizing recommendations for any of them: “It depends.” It depends on the amount of data, the expected growth, the type of data, the available software ecosystem around each, and mostly the features that your requirements and ambitions demand; just to name a few.

 

At Findwise we can help you make a Platform evaluation study to find the perfect match for your organization and your information.

 

Written by: Daniel Gómez Villanueva – Findability and Search Expert

Using search technologies to create apps that even leaves Apple impressed

At Findwise we love to see how we can use the power of search technologies in ways that goes beyond the typical search box application.

One thing that has exploded the last few years is of course apps in smartphones and tablets. It’s no longer enough to store your knowledge in databases that are kept behind locked doors. Professionals of today want to have instant access to knowledge and information right where they are. Whether if it’s working at the factory floor or when showcasing new products for customers.

When you think of enterprise search today, you should consider it as a central hub of knowledge rather than just a classical search page on the intranet. Because when an enterprise search solution is in place, when information from different places have been normalized and indexed in one place, then there really are no limits for what you can do with the information.

By building this central hub of knowledge it’s simple to make that knowledge available for other applications and services within or outside of the organization. Smartphone and tablet applications is one great example.

Integrating mobile apps with search engine technologies works really well because of four reasons:

  • It’s fast. Search engines can find the right information using advanced queries or filtering options in a very short time, almost regardless of how big the index is.
  • It’s lightweight. The information handled by the device should only be what is needed by the device, no more, no less.
  • It’s easy to work with. Most search engine technologies provides a simple REST interface that’s easy to integrate with.
  • A unified interface for any content. If the content already is indexed by the enterprise search solution, then you use the same interface to access any kind of information.

 

We are working together with SKF, a company that has transformed itself from a traditional industry company into a knowledge engineering company over the last years. I think it’s safe to say that Findwise have been a big part of that journey by helping SKF create their enterprise search solution.

And of course, since we love new challenges, we have also helped them create a few mobile apps. In particular there are two different apps that we have helped out with:

  • SKF Shelf – A portable product brochures archive. The main use case is quick and easy access to product information for sales reps when visiting customers.
  • MPLP (mobile product landing page) – A mobile web app that you get to if you scan QR-codes printed on the package.

 

And even more recently the tech giant Apple has noticed how the apps makes the day to day work of employees easier.


SKF Shelf3

Read more about Enterprise Search at Findwise

Elastic{ON} 2017 – breaking all the records!

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

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

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

ElasticON 2017

2016 in retrospective with the future in mind

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

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

A strong focus on analytics

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

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

Elasticsearch SQL – giving BI what it needs

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

Fast and simple Beats is music to our ears

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

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

Kibana goes international

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

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

kibana-int

 

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

 

From San Francisco with love,

/Andreas, Christian, Joar and Peter

Digital wizardry for customers & employees – the next elements

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Contact Findwise now to ease the process, before your competitor does 😉
View Fredric Landqvist's LinkedIn profileFredric Landqvist research blog
View Peter Voisey's LinkedIn profilePeter Voisey

Alfresco CMS permission hooking

Integration between Alfresco and Findwise i3

In Findwise we have been working on creating a custom AMP (Alfresco Module Package… essentially a nicely packeted extension to Alfresco) for quite a while making a nice integration between Alfresco and Findwise i3.

Alfresco is essentially an open source SharePoint written in Java. It’s a CMS (Content Management System), where the basic functionality is to allow end users to import his/her documents, to make them easy to manage and find later on.

Findwise i3 is built on top of a number of open source products, like Solr, Elastic Search etc. and its purpose is to provide a nice, clean, pluggable pipelined framework for loading in data in one end, making them searchable and applying search aspects to the documents, to allow for searching intelligently on subsets of data. Further it’s also a pluggable framework for presenting a nice web based search frontend, where the defined search aspects can be selected, along with all other criteria’s and nicely formatted and organized search result sets.

So why integrate the two?

There are many users of Alfresco around the world, and there are also many users of Findwise search functionality. As Findwise framework allows for making lots of different data searchable across an organization, it makes sense also to be able to search data stored in Alfresco, as just one of these many sources. That’s the background for the project and why we entered integrating the two.

Plugging into Alfresco’s repository (which stores documents) is fairly easy, because the developers of Alfresco have prepared hooks one can use. Thus it’s possible to get informed about whenever a document is added into Alfresco, when its content is changed, or when it’s deleted. On each of these occasions, we want to let Findwise i3 know about it, so it can live update its databases, and thus always provide relevant, current data for the end-users. As said, this is fairly straightforward and documented in the Alfresco documentation.

However, there is another scenario, for which no nice solution exists in Alfresco. Namely hooking into when permissions are changed on documents stored in Alfresco.

Why do we need information about permission changes?

Because Findwise i3 of course respects the IT security roles and settings defined in a company’s infrastructure. It wouldn’t be cool to have a document protected by ownership or security roles within Alfresco, but at the same time be fully accessible via the Findwise search API. So we need to be informed about all permission changes made to each and every document hosted within Alfresco. All permission requests and settings within Alfresco happens via the PermissionService interface (permissionService implementation).

So one could choose to replace or override that class with one of our own. That would result in having to dig deep into Alfresco’s configuration, with the risk that when a minor update of Alfresco is installed, our overridden class will have to be reconfigured into the Alfresco configuration once again. That’s basically a mess and should be avoided at all costs. We would much rather prefer to have the nicely AMP package, for which exists simple to use tools to reinstall into an Alfresco installation, be able to hook also the permission events.

The preferred way to do that is to create a method interceptor. That’s actually a Spring thing, and as Alfresco is built upon Spring, it makes sense to use one of those. The interceptor does exactly that, intercepts all method calls for a particular class or interface for which it has been registered. The easy part is to make the interceptor, the difficult part (due to its not very well described) is to have it registered from within the AMP itself

The following is skeleton code for a method interceptor:

screen-shot-2016-12-21-at-09-49-07

And this is how to register it in your AMP’s service-context.xml

screen-shot-2016-12-21-at-10-17-21

When you register your AMP with Alfresco using the usual apply_amps script, your new interceptor class will spring into life when a method on the PermissionService interface is called. It’s actually pretty simple, but the hard part is to figure out the registration process, and provide the correct case and naming for the PermissionService. We have spent quite allot of time searching for this on Google, reading the Alfresco documentation and only found fragments that didn’t show the whole picture.

Now we hope our findings were helpful to others.

Written by: Kim Bo Madsen, Consultant Findwise

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

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