Will my car pass the NCT?

Will my car pass the NCT?

EDIT 7/8/18: Our NCT work featured in the Sunday Independent: https://www.independent.ie/life/motoring/car-reviews/which-car-is-best-of-the-test-37185356.html 

In Ireland, every car over 4 years old requires a roadworthiness certificate, which it gets by passing the National Car Test (NCT). If you're buying a used car, it's important to know how likely that make and model is to pass the NCT - and if it fails, on what part of the test.

To help you find out this information, Idiro has analysed the results of the last 5 years' NCT tests - and we offer you two tools:

The NCT checker

Idiro has created a simple NCT car checker tool, available online. You’ll find it at www.Idiro.com/NCTchecker.

Just enter the make, model and year of the car in question to learn all about how these cars perform in the NCT. If your car is quite rare, like the Mazda MX-5, then we recommend that you select all the years of NCT tests. Otherwise, just leave 2017 ticked.

As you can see, Mazda MX-5s from the year 2000 have a 66.3% failure rate across 5 years of tests - just slightly worse than the average of 65.7% for all cars of that age. However, look at the detail - the MX-5 does much better than average for some elements (no failures for suspension!) but much worse for others (four times worse for emissions). That will help you know what to look for when buying a used car, and help you prepare your car to maximise its chance of passing the NCT.

Idiro's NCT checker - input form and results

Our handy NCT checker works on phones, tablets and PCs. Kudos to my colleague John Grant for building it.

Exploring all of this year's NCT results

For people who would like to dig deeper into the NCT results, we have produced an interactive dashboard as a demonstration of our data analytics skills.

CLICK HERE TO OPEN THE 2017 NCT DASHBOARD

Again, it uses data published by the Road Safety Authority covering 2017 NCT tests. Data has not been published on retests, so our dashboard covers the first NCT test that each car underwent in 2017.

For practical purposes, the data is filtered to show only the twenty most popular makes of vehicle tested in 2017, and for each of these, only models with at least 1000 tests in the year. This was necessary because to show every make and model of car tested would make the dashboard so complex as to be unusable. As a result, from the total 1.4 million tests carried out in 2016, 1.1 million tests are represented in this dashboard.

However, if you do want to look at makes and models of cars that are not shown in this analysis, you can download the full dataset from the RSA.

How to use this dashboard

Pro tip: To reset all your filters and return to the original screen, click your browser’s refresh button.

The RSA provides the ‘Year of Car’ of each car tested, which we understand to mean the year of first registration. You can filter by the age of cars using this slider. For example, if you want to see test results for all cars registered in 2010 or before, you simply drag the end buttons in the slider over to the desired year.

This is an interactive dashboard, so as you change one parameter, all of the graphs adjust to match your selection. For example, this bubble table shows the top 20 most popular makes of car with a 2010 or older registration. The bubble's size indicates how popular that make is, and its colour indicates the make's pass rate - from deep blue (high pass rate) to deep red (low pass rate).

Test volume and pass rate per make

In this example, we can see below that for vehicles registered in 2010 and older, Toyota is the most popular make and has a high pass rate.

Now let's click on 'Toyota'. As you can see, this changes all the charts in the dashboard - they now only show the details of Toyota models.

Toyota selected

Pro tip: To compare different car makes, hold down the CTRL key while you click on each make that you want to filter in the bubble chart “Overall Popularity & Passing % of Model”.

Now let's examine the different Toyota models. In the next graphic to the right, ‘First time pass rate by model’, you’ll see the pass rate of each Toyota model.

First time pass rate for Toyotas

In the table on the far right entitled ‘Most Popular Model & Age’, you’ll see each model in the Toyota range. The Prius is the Toyota with the highest pass rate, but it isn’t the most popular Toyota - as you can see, the Corolla is the most popular (as you will see, Corollas have been around since 1980).

As you scroll down, you can see the ‘First Time Failure By Year’ graph which shows the number of cars tested (blue bars) and failure rates (red line) for each year of registration. As you can see, younger cars are much more likely to pass the NCT. To look at failure rates over time in each category within the test, you can filter by category in the drop-down menu.

To the right you'll see the ‘First Time Failure By Category’ table, which shows the percentage of cars that fail each category within the NCT test. This image displays what caused Toyota cars to fail their NCT.

As you can see, the dashboard allows you to dig deep into the 2017 NCT test results. Here again is the link to the dashboard:

https://public.tableau.com/profile/idiro.analytics#!/vizhome/NCT2017Top20Makes/NCT2017-20MostPopularMakes

This dashboard works best on PCs, rather than mobiles. Kudos to colleagues Paul Goldsberry and John Grant for building it.

 

We do hope you find these tools useful. To discuss how Idiro's analytics skills can help your business, drop us an email at info@idiro.com.  To download the source data from RSA.ie, click here.

 

Idiro shortlisted in TWO categories at the Technology Ireland software awards

Idiro shortlisted in TWO categories at the Technology Ireland software awards

We are delighted to announce that Idiro has been shortlisted for awards in two categories of the prestigious Technology Ireland software awards.  Our two categories are:

  • Digital Technology Services Project of the Year, for our analytics project in the South Pacific
  • Technology Innovation of the Year, for Red Sqirl, Idiro's advanced analytics platform for Big Data

Idiro's CEO, Aidan Connolly commented: "It is an honour to be shortlisted for these awards and it is testament to the ingenuity and hard work by the team". 

The awards ceremony is on Friday 24th November and our fingers are crossed.

Analysis of NCT test results helps car buyers choose wisely

Analysis of NCT test results helps car buyers choose wisely

 

Today Idiro has published a data visualisation dashboard (here) allowing you to explore the 2016 NCT test results. 

Update 21/8/17: The Sunday Independent ran a story yesterday on Idiro's dashboard - see here http://www.independent.ie/life/motoring/car-reviews/put-your-car-to-the-test-36049372.html 

Update 30/8/17: Our dashboard has been picked up by many other media including RTÉ: https://www.rte.ie/lifestyle/living/2017/0830/900977-is-this-the-worst-car-model-for-nct/

About this dashboard and the data

This interactive report is published by Idiro Analytics as a demonstration of our data visualisation capability. The data used is published by RSA.ie and covers NCT tests conducted in 2016. The data covers the first NCT test that each car underwent in 2016 - data has not been published on retests for cars that fail.

The data is filtered to show only the twenty most popular makes of vehicle tested in 2016, and for each of these, only models with at least 1000 tests carried out. This was necessary because to show every make and model of car tested would make the dashboard unusable. As a result, from the total 1.4 million test carried out in 2016, 1.1 million tests are represented in this dashboard.

If you wish to examine the data further or want to look at makes / models of cars that are not shown in this analysis, the full dataset is available for download at RSA.ie.

How to use this dashboard

Pro tip: To reset all your filters and return to the original screen, click your browser’s refresh button.

Pro tip: To compare different car brands, press CTRL + click on the type of makes you want to filter in the bubble chart “Overall Popularity & Passing % of Model”. 

 

The RSA provides the ‘Year of Car’ of each car tested.  You can filter by the age of cars using this slider. 

If you want to see cars from 2010 or before that were tested in 2016, you simply drag the end buttons in the slider (at the top) over to the desired year.

As the year changes, so do the interactive maps. This bubble table shows the top 20 most popular car models with a 2010 or older registration tested in 2016 that passed first time.

The bubbles indicate how popular that model is, and the colour of each circle indicates the pass rate - from deep blue (high pass rate) to deep red (low pass rate).

For example, we can see below that for vehicles registered in 2010 and older. Toyota is the most popular and has a high pass rate in 2016.

In the next graphic to the right, ‘First time pass rate by model’, you’ll see the different Toyota models that were tested.

In the next table ‘Model Popular Model & Age’, you’ll see the most popular models in the Toyota age range of the models tested.

Although the Prius has the highest pass rate in the Toyota model, it isn’t the most popular make, Corolla is the most popular.  As you scroll down the interactive map, you can see the failure rates of each car that was tested in the NCT and what the cause of the failure was.

 

You can filter what cars failed on by category or you can choose to see total which will show all the categories on the ‘First Time Failure By Year On’ drop-down menu.  The image below displays blue bars which indicate the units tested in each year and the red line graph shows the failure percentage rate. Remember that pass/fail thresholds can vary according to the age of the car.

As you scroll across, you’ll see the ‘First Time Failure By Category’ table. This table shows each category that the NCT test each car.

This graph displays what caused Toyota cars to fail their NCT. 

 

 

Here's the link to the dashboard: https://public.tableau.com/profile/idiro.analytics#!/vizhome/NCT2016Top20MakesResults/NCT2016-20MostPopularMakes

To contact Idiro about this blog post or about how Idiro's analytics can help your business, drop us an email at info@idiro.com.  To download the source data from RSA.ie, click here.  

 

 

Twenty Numbers that Define Kenny’s Leadership in the Past Six Years

Twenty Numbers that Define Kenny’s Leadership in the Past Six Years

Google, homelessness and a shrinking unemployment rate: a look at the figures that will come to define Kenny’s legacy—for better and worse.

  • 2,277: days in power on 1st June 2017.
  • €197,000: Enda Kenny’s average salary between the 2011 election and the end of 2016. 
  • 14.4%: the unemployment rate in February 2011 when Enda Kenny was elected Taoiseach. 
  • 6.2%: the unemployment rate in April 2017. 
  • 2.59%: average inflation rate in 2011. 
  • 0.01%: average inflation rate in 2016. 
  • 7: words [the homeless] “don't want to come off the streets” - Enda Kenny’s opinion on the homeless in 2016.
  • 4,588,252: the population of Ireland in 2011. 
  • 4,761,865: the population of Ireland in 2016.
  • 3.8%: increase in the population of Ireland between 2011 and 2016. 
  • 173,613: increase in population from 2011 to 2016.
  • €13,000,000,000: Apple’s Irish Tax bill.
  • 2.7: Doctors per 1,000 population in 2013 
  • 20: Ireland’s rank in 2015 for disposable income within the 38 OECD countries. 
  • 3,808: the number of homeless people in Ireland as of April 2011.
  • 7,472: the number of homeless people in Ireland as of March 2017.
  • 679: drug related deaths in 2013.
  • 26: seats lost by Fine Gael in the general election 2016.
  • €22,600,000,000: Google’s EMEA revenue from controversial advertising sales business in Ireland in 2015.
  • €47,800,000: tax paid by Google in Ireland in 2015.

 

Big data – will it solve your marketing problems?

Big data – will it solve your marketing problems?

As ever, Tom Fishburne has a point.  Increasingly, organisations are turning to their data to improve decision-making and improve commercial results - but buying big data infrastructure won't solve your marketing problems.  In many ways, installing the big data infrastructure is the easy bit.  The real challenge, as Idiro has found time and time again, is turning all that data into money.  For this you need people with the BI and analytics skills to mine all that newly-available data for dashboards, insights and predictions.  And of course the organisation needs to be ready to change - to try new ways of using data to drive commercial activity - and it needs to be prepared to fail.  Samuel Beckett said:

'Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.'

With the right analytics partner, the journey to excellence in data-driven marketing should be a lot easier than Beckett paints it - but nevertheless, it takes skill and a ruthless focus on the results.  However, the results from using your organisation's data to drive its business are nearly always well worth the effort.

Analysis shows that cash is king in the Irish housing market

Analysis shows that cash is king in the Irish housing market

As the housing market in Ireland is heating up again, we examined the trends in cash purchases of housing from 2010 till 2016. There is a significant jump in non-mortgaged home purchases going from €347 million in 2010 to approximately €6 billion in 2016. This represents an increase of more than 1,600% over that period.

Housing sales 2010-2016: cash vs. mortgages

To see an interactive version of this graph, click on the image above

The proportion of cash transactions for housing in Ireland peaked in 2013 and has been on a downward trajectory since then. However, it is still 44 percentage points higher than where it was in 2010.

Cash sales as a percentage of housing transactions has increased massively since 2010, and recently declined slightly.

To see an interactive version of this graph, click on the image above

There can be significant externalities created by an influx of cash within the property sector. It can lead to an increase in home prices and displace the median income
home buyers out of the property market, leading to an increased affordability gap. It should be noted that although cash sales have been a feature of the Irish housing market, recently the proportion of institutional and international investors has increased. Finding data on investments by international property players such as Blackstone within the Irish real estate market would require further research. It is well understood that credit cannot compete with cash. As long as the housing market in Ireland is dominated by cash buyers, whole classes of renters are likely to be priced out of their dream of owning a home.

Idiro staff and friends launch Databeers Dublin with sell-out success first event

Idiro staff and friends launch Databeers Dublin with sell-out success first event

'Databeers' is a series of talks that started in 2014 in Madrid, and then spread to Barcelona, London and many other cities.  Databeers aims to bring together data experts (from industry or academia, at a level accessible to a wide audience), with short talks and beer, i.e. in a fun way.  The format of Databeers talks is unlike any other: three or four talks, each seven minutes (yes, seven minutes!) long, on any aspect of data, free beer, and of course free entry.  

A group of friends working at Idiro (Julie, Davide & Simon) heard about Databeers, and thought it would be good to bring Databeers to Dublin.   We recruited three more people - FrancescaAonghus and Stefano - and formed the organising team.  The team found a fine venue (thank you Bank of Ireland), a beer sponsor (many thanks, Estrella Damm), three great speakers (thanks Krithika, Cathal and Patricia).  We launched the event and sold out in 3 days!  On the night we had a full house, three great talks and we managed to avoid the hassle of undrunk beer to keep till next time...

The Databeers Dublin team: L-R: Francesca, Stefano, Simon, Davide, Julie and Aonghus.

The next event is already being planned, so keep an eye on @DatabeersDub for updates!

 

The data analytics landscape in Ireland

The data analytics landscape in Ireland

What does the data analytics landscape look like in Ireland?

Check out the results of our survey and see whether you won the €100 voucher.

 

A few weeks ago, we here at Idiro Analytics decided to run a survey on the data analytics landscape of Ireland. Our hope was to get a better insight into the data analytics field here in Ireland and gain a better understanding of where Ireland stands in the global analytics industry.

As with any field, it can be too easy to only focus on your own area and what works for you, but having an overview of what other people in your industry are doing can be extremely valuable. Even something as simple as seeing tools being used by others in your industry could lead you to explore options you might not be aware of.

With data analytics and Big Data being such popular buzzwords for marketers to throw around, we thought it would make sense to gather facts directly from those working in the industry.

We received a great response to our survey from the Irish-based data analytics community and we would like to thank everyone who completed it.

Here is the summary of the survey results:

We’ll start with the job title, and it’s interesting to see here that there are a lot of people working with data who don’t categorise themselves as being in a traditional data analyst / scientist role.

When asking the types of business problems you were solving with data, we found that a huge 69% of people were using data for marketing and sales activities.

Although marketing and sales analytics might not be the only focus, it’s clear that a lot of companies in Ireland are finding value in that area of analytics. That reflects Idiro’s own experience - most of the analytics work we do for our own customers is around helping our customers do better marketing or sales.

So are some industries doing more advanced analytics than others? It seems so - we found that the highest number of analysts working on predictive analytics projects are in these three industries:

Now let’s look at the analytics tools. As you’d expect, there’s a massive number of different software tools in use by the analytics community in Ireland.

And we can see, the top 3 analytics software tools in Ireland are Excel, R & MS SQL Server.

There are big differences according to the industry the analyst works in, for example:

Finance/Accounting

Utilities

We have an interesting breakdown by industry, but the table is far too big to show here, so contact us if you’d like to see it.

Next, we looked at the type of work being done (reporting, insights / analysis, modelling), and split by the different job titles:


 

 

 

Idiro researcher invited to speak at MACSI 10

Idiro researcher invited to speak at MACSI 10

Idiro researcher invited to speak at MACSI 10

 

The importance of academic research has never been underestimated here at Idiro Analytics. Encouraging our analysts to explore new and innovative technologies and techniques when solving data problems has always been a part of Idiro’s company culture.

Bridging the gap between academic research and industry is an area Idiro are very proud to be involved in. With this in mind, we're happy to report that our colleague Davide Cellai was invited to be a speaker in the workshop to celebrate 10 years of MACSI.

This is Davide’s report from the event:

"Last week I participated, as an invited speaker, in the workshop to celebrate the 10 years of MACSI. MACSI (Mathematics Applications Consortium for Science and Industry) is a consortium based at the University of Limerick that promotes collaboration between applied mathematicians and industry.

MACSI was founded in 2006 by the largest single grant ever awarded to mathematics in Ireland, and since then it has been quite a special point of reference in the country for mathematicians interested in industrial applications. I had been working in MACSI for more than four years and I knew the people over there very well. MACSI engages in industrial collaborations both at the national and international level. People in the consortium work hard to improve products and processes for the industrial partners and provide advanced training in mathematical modelling to students and researchers.

Davide Cellai speaking at MACSI

Idiro and MACSI have a long-standing collaboration. Indeed, Idiro is always keen to collaborate with academia. As we continuously improve our models and expand our range of services, we often employ cutting-edge research to meet those challenges. In 2014, when I was still in MACSI, I won a Science Foundation Ireland Industry Fellowship, a grant that gave me the opportunity to move to Idiro and apply Network Science models to the problem of predicting telecommunication churn, using one of the datasets available in the company. This work was so successful that I was later hired by Idiro as a Senior Data & Analytics Architect.

In my talk, I illustrated the model (called m-exposure model) that I developed during the Fellowship and the outcome of this work. While the m-exposure model was designed for portout churn, we then developed a similar model for expiry churn. Both models are now part of Idiro's suite of SNA tools for churn prediction.

Finally, I presented some new ideas and challenges that we would like to pursue in the near future.

There was a lot of interest in my talk. I got both great questions and great feedback (and lots of compliments) in the following hours. Some of the scientists in the audience were particularly interested in Idiro's future projects. Hopefully, we will get some good ideas for designing our next products.

The workshop was also very interesting in its own way. I listened to some great envisioning talks. In one of them, Professor Wil Schilders was comparing the benefits of faster computer hardware with faster algorithms. His point was that the latter was actually more interesting. In other words, it's often better to have a new algorithm running on an old computer than an old algorithm running on a new computer. Some other talks were also speaking about how science can improve society, from the elimination of tropical diseases to exact calculation of delay time after a road incident. I was delighted to be invited to such a great event."

Davide Cellai at MACSI

Major International Telco chooses Red Sqirl

Major International Telco chooses Red Sqirl

Major International Telco chooses Red Sqirl

 

One of the first commercial users of the Red Sqirl analytics platform for Big Data is a multinational telco, which has deployed Red Sqirl in two countries and is using it to deliver analytics on its Hadoop platform. This customer has asked to remain anonymous.  

Red Sqirl is a flexible drag-and-drop Big Data analytics platform with a unique open architecture. Red Sqirl makes it easy for analysts and data scientists to analyse the data on your Hadoop platform.  

The problem

 

This multinational telco operates on four continents worldwide. Unfortunately, for historical reasons these operating companies use a wide variety of different database systems and analytical platforms.  

As data becomes an increasingly important asset for organisations, many companies are taking steps to maximise the value of their data. In addition, most large companies now have access to many new types of data - for example, social media posts.

To exploit synergies across the worldwide organisation, this multinational telco decided to standardise database platforms across the group. In order to best meet the challenges of storing and using ‘big data’, the company chose Hadoop as its standard database platform.  Hadoop is currently being rolled out across the company’s operations.  

The company now faces the challenge of migrating existing code onto Hadoop, and allowing asset re-use and swapping across business units who have multiple historic platforms and assets.  Moreover, the Hadoop ecosystem does not contain a ready-made data analytics module.  The market leading traditional data analytics software platforms are not designed for the Hadoop ecosystem and tend to be inefficient when analysing Hadoop data. The telco searched for a native Hadoop analytics platform with an easy-to-use graphical interface.  In addition, because of the wide variety of requirements, the platform had to be highly flexible and cost-effective for a worldwide rollout.

Solution chosen: Red Sqirl

 

Following a thorough technical / usability evaluation of a number of analytics platforms, the company agreed a contract to trial Red Sqirl - initially in the company’s head office and in one operating company.  

Red Sqirl exceeds the telco’s requirements as set out above. Of particular interest to this company is Red Sqirl's unique capability for sharing, via the Red Sqirl Analytics Store.  Moreover, the Red Sqirl development has deep experience of telco analytics.  Red Sqirl is already proven in predicting telco churn, as shown in the workflow below.

A Red Sqirl screenshot showing a telco churn modelling workflow
Telco churn modelling in Red Sqirl - a workflow


This telecoms operator now has the confidence that their analytics solutions are scalable, deployable and easy to use.

Implementation

 

Red Sqirl was installed in both the head office and the country telco, which took a matter of minutes in each case. Training workshops were held in both locations, to ensure that analysts could get the most out of the Red Sqirl platform. Red Sqirl’s drag-and-drop interface is similar to that of most GUI analytical tools, so the training was quickly completed.  Feedback from students was very positive - as the graph below shows, students scored the training very highly.

Student feedback on Red Sqirl training course
Student feedback on Red Sqirl training course


The company then needed to migrate analytical assets from legacy infrastructure to Red Sqirl.  The Red Sqirl development team showed the way by taking a particular analytical model that had been developed in another language and quickly porting it onto Red Sqirl.

The company is now using Red Sqirl as its primary analytics platform in the country in question - and early results are promising.