Pragmatic or spontaneous – What are the most common personal qualities in IT-job ads?

Open Data Analytics

At Findwise we regularly have companywide hackathons with different themes. The latest theme was insights in open data, which I personally find very interesting.

Our group chose to fetch data from the Arbetsförmedlingen (Swedish Employment Agency), where ten years of job ads are available. There are about 4 million job ads in total during this time-period, so there is some material to analyze.

To make it easier to enable ad hoc analysis, we started off by extracting competences and personal traits mentioned in the job ads. This would allow us to spot trends in competences over time, in different regions or correlate competences and trait. Lots of possibilities.

 

Personal qualities and IT competences

As an IT-ninja I find it more exciting to focus on jobs, competences and traits within the IT industry. A lot is happening, and it is easy for me to relate to this area, of course. A report from Almega suggests that there is a huge demand of competences within IT for the coming years and it brings up a lot of examples of lacking technical skills. What is rarely addressed is what personality types are connected to these specific competences. We’re able to answer this interesting question from our data:

 

What personal traits are common complementary to the competences that are in demand?

arbetsförmedlingen hack

Figure 1 – Relevant worktitles, competences and traits for the search term “big data”

 

The most wanted personal traits are in general “Social, driven, passionate, communicative”. All these results should of course be taken with a grain of salt, since a few staffing/general IT consulting companies are a big part of the number of job ads within IT. But we can also look at a single competence and answer the question:

 

What traits are more common with this competence than in general? (Making the question a bit more specific.)

Some examples of competences in demand are system architecture, support and JavaScript. The most outstanding traits for system architecture are sharp, quality orientated and experienced. It can always be discussed if experienced is a trait (although our model thoughts so) but it makes sense in any case since system architecture tend to be more common among senior roles. For support we find traits such as service orientated, happy and nice, which is not unexpected, Lastly, for job-ads needing javascript-competence, personal traits such as quality orientated, quality aware and creative are the most predominant.

 

Differences between Stockholm and Gothenburg

Or let’s have a look at geographical differences between Sweden’s two largest cities when it comes to personal qualities in IT-job ads. In Gothenburg there is a stronger correlation to the traits spontaneous, flexible and curious while Stockholm correlates with traits such as sharp, pragmatic and delivery-focused.

 

What is best suitable for your personality?

You could also look at it the other way around and start with the personal traits to see which jobs/competences are meant for you. If you are analytical then jobs as controller or accountant could be jobs for you. If you are an optimist, then job coach or guidance counselors seems to be a good fit. We created a small application where you can type in competences or personal traits and get suggested jobs in this way. Try it out here!

 

Lear more about Open Data Analytics

In addition, we’re hosting a breakfast seminar December 12th where we’ll use the open data from Arbetsförmedlingen to show a process of how to make more data driven decisions. More information and registration (the seminar will be held in Swedish)

 

Author: Henrik Alburg, Data Scientist

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

Predictive Analytics World 2012

At the end of November 2012 top predictive analytics experts, practitioners, authors and business thought leaders met in London at Predictive Analytics World conference. Cameral nature of the conference combined with great variety of experiences brought by over 60 attendees and speakers made a unique opportunity to dive into the topic from Findwise perspective.

Dive into Big Data

In the Opening Keynote, presented by Program Chairman PhD Geert Verstraeten, we could hear about ways to increase the impact of Predictive Analytics. Unsurprisingly a lot of fuzz is about embracing Big Data.  As analysts have more and more data to process, their need for new tools is obvious. But business will cherish Big Data platforms only if it sees value behind it. Thus in my opinion before everything else that has impact on successful Big Data Analytics we should consider improving business-oriented communication. Even the most valuable data has no value if you can’t convince decision makers that it’s worth digging it.

But beeing able to clearly present benefits is not everything. Analysts must strive to create specific indicators and variables that are empirically measurable. Choose the right battles. As Gregory Piatetsky (data mining and predictive analytics expert) said: more data beats better algorithms, but better questions beat more data.

Finally, aim for impact. If you have a call center and want to persuade customers not to resign from your services, then it’s not wise just to call everyone. But it might also not be wise to call everyone you predict to have high risk of leaving. Even if as a result you loose less clients, there might be a large group of customers that will leave only because of the call. Such customers may also be predicted. And as you split high risk of leaving clients into “persuadable” ones and “touchy” ones, you are able to fully leverage your analytics potencial.

Find it exciting

Greatest thing about Predictive Analytics World 2012 was how diverse the presentations were. Many successful business cases from a large variety of domains and a lot of inspiring speeches makes it hard not to get at least a bit excited about Predictive Analytics.

From banking and financial scenarios, through sport training and performance prediction in rugby team (if you like at least one of: baseball, Predictive Analytics or Brad Pitt, I recommend you watch Moneyball movie). Not to mention Case Study about reducing youth unemployment in England. But there are two particular presentations I would like to say a word about.

First of them was a Case Study on Predicting Investor Behavior in First Social Media Sentiment-Based Hedge Fund presented by Alexander Farfuła – Chief Data Scientist at MarketPsy Capital LLC. I find it very interesting because it shows how powerful Big Data can be. By using massive amount of social media data (e.g. Twitter), they managed to predict a lot of global market behavior in certain industries. That is the essence of Big Data – harness large amount of small information chunks that are useless alone, to get useful Big Picture.

Second one was presented by Martine George – Head of Marketing Analytics & Research at BNP Paribas Fortis in Belgium. She had a really great presentation about developing and growing teams of predictive analysts. As the topic is brisk at Findwise and probably in every company interested in analytics and Big Data, I was pleased to learn so much and talk about it later on in person.

Big (Data) Picture

Day after the conference John Elder from Elder Research led an excellent workshop. What was really nice is that we’ve concentrated on the concepts not the equations. It was like a semester in one day – a big picture that can be digested into technical knowledge over time. But most valuable general conclusion was twofold:

  • Leverage – an incremental improvement will matter! When your turnover can be counted in millions of dollars even half percent of saving mean large additional revenue.
  • Low hanging fruit – there is lot to gain what nobody else has tried yet. That includes reaching for new kinds of data (text data, social media data) and daring to make use of it in a new, cool way with tools that weren’t there couple of years ago.

Plateau of Productivity

As a conclusion, I would say that Predictive Analytics has become a mature, one of the most useful disciplines on the market. As in the famous Gartner Hype, Predictive Analytics reached has reached the Plateau of Productivity. Though often ungrateful, requiring lots of resources, money and time, it can offer your company a successful future.

Analyzing the Voice of Customers with Text Analytics

Understanding what your customer thinks about your company, your products and your service can be done in many different ways. Today companies regularly analyze sales statistics, customer surveys and conduct market analysis. But to get the whole picture of the voice of customer, we need to consider the information that is not captured in a structured way in databases or questionnaires.

I attended the Text Analytics Summit earlier this year in London and was introduced to several real-life implementations of how text analytics tools and techniques are used to analyze text in different ways. There were applications for text analytics within pharmaceutical industry, defense and intelligence as well as other industries, but most common at the conference were the case studies within customer analytics.

For a few years now, the social media space has boomed as platforms of all kinds of human interaction and communication, and analyzing this unstructured information found on Twitter and Facebook can give corporations deeper insight into how their customers experience their products and services. But there’s also plenty of text-based information within an organization, that holds valuable insights about their customers, for instance notes being taken in customer service centers, as well as emails sent from customers. By combining both social media information with the internally available information, a company can get a more detailed understanding of their customers.

In its most basic form, the text analytics tools can analyze how different products are perceived in different customer groups. With sentiment analysis a marketing or product development department can understand if the products are retrieved in a positive, negative or just neutral manner. But the analysis could also be combined with other data, such as marketing campaign data, where traditional structured analysis would be combined with the textual analysis.

At the text analytics conference, several exciting solutions where presented, for example an European telecom company that used voice of customer analysis to listen in on the customer ‘buzz’ about their broadband internet services, and would get early warnings when customers where annoyed with the performance of the service, before customers started phoning the customer service. This analysis had become a part of the Quality of Service work at the company.

With the emergence of social media, and where more and more communication is done digitally, the tools and techniques for text analytics has improved and we now start to see very real business cases outside the universities. This is very promising for the adaptation of text analytics within the commercial industries.

Video: Search Analytics in Practice

Search Analytics in Practice from Findwise on Vimeo.

This presentation is about how to use search analytics to improve the search experience. A small investment in time and effort can really improve the search on your intranet or website. You will get practical advice on what metrics to look at and what actions can be taken as a result of the analysis.

Video in swedish “Sökanalys i praktiken”.

The presentation was recorded in Gothenburg on the 4th of May 2012.

The presentation featured in the video:

Search Analytics in Practice

View more presentations from Findwise

Book Review: Search Analytics for Your Site

Lou Rosenfeld is the founder and publisher of Rosenfeld Media and also the co-author (with Peter Morville) of the best-selling book Information architecture for the World Wide Web, which is considered one of the best books about information management.

In Lou Rosenfeld’s latest book he lets us know how to successfully work with Site Search Analytics (SSA). With SSA you analyse the saved search logs of what your users are searching for to try to find emerging patterns. This information can be a great help to figure out what users want and need from your site.  The search terms used on your site will offer more clues to why the user is on your site compared to search queries from Google (which reveal how they get to your site).

So what’s in the book?

Part I – Introducing Site Search Analytics

In part one the reader gets a great example of why to use SSA and an introduction to what SSA is. In the first chapters you follow John Ferrara who worked at a company called Vanguard and how he analysed search logs to prove that a newly bought search engine performed poorly whilst using the same statistics to improve it. This is a great real world example of how to use SSA for measuring quality of search AND to set up goals for improvement.

a word cloud is one way to play with the data

Part II – Analysing the data

In this part Lou gets hands on with user logs and lets you how to analyse the data. He makes it fun and emphasizes the need to play with user data. Without emphasis on playing, the task to analyse user data may seem daunting. Also, with real world examples from different companies and institutions it is easy to understand the different methods for analysis. Personally, I feel the use of real data in the book makes the subject easier (and more interesting) to understand.

From which pages do users search?

Part III – Improving your site

In the third part of the book, Rosenfeld shows how to apply your findings during your analysis. If you’ve worked with SSA before most of it will be familiar (improving best bets, zero hits, query completion and synonyms) but even for experienced professionals there is good information about how to improve everything from site navigation to site content and even to connect your ssa to your site KPI’s.

ConclusionSearch Analytics For Your Site shows how easy it is to get started with SSA but also the depth and usefulness of it. This book is easy to read and also quite funny. The book is quite short which in this day and age isn’t negative. For me this book reminded me of the importance of search analytics and I really hope more companies and sites takes the lessons in this book to heart and focuses on search analytics.