About Chris Love

data professional, runner, photographer; these are my hobbies at present, I make no guarantees to the future.

Comparing against Next Generation – it’s Tough

I want to take you back in time in my time machine, back to the 1900’s and the new age of the automobile. Henry Ford has not yet perfected the mass production on the motor car, they are still the preserve of the rich and the average car is expensive – putting them out the reach of an average family. Though the car market is booming like no time before, it is still very small.

We land the time machine and I give you a simple job, help me sell the modern car to the people of the 1900’s. Easy right? Let’s see how things might pan out for you….

Look what you’re up against, it’s archaic, a relic from a bygone age. You set up a stand advertising a new way; a cheaper, modern alternative to the old way of doing things, effectively democratising automobiles for everyone. It will allow longer, faster journeys and with the effects of (de)inflation your cars are cheap enough for anyone to afford, surely this will be a piece of cake.

A portly gentleman in a bowler hat, clearly of means, pulls up and climbs down from his motor car.

Sir”, you say, “ould you like to take a trip with me in the car of the future? I feel confident it will revolutionise how you think about driving”.

“I don’t think we need to do that”, he counters, “A motor car will take you from A to B, they’re all the same really aren’t they? I don’t need to see it to believe it will work, I’ve seen hundreds of cars.”

Okay so a test drive would have helped you show him what he was missing, but it’s not really necessary as you have a compelling argument.

“Well Sir, my modern car is slightly different. Mine will take you from 0 – 60mph in just a few seconds, and will go considerably faster if you want it to, and what’s more everyone is driving them where I come from.”

 

1918 Oakland Tribune - click to read the full article

1918 Oakland Tribune – click to read the full article

 “?!”, a look of absolute horror crosses your new friends face,“I don’t think we want that now do we, they’ll kill themselves. Anyway we have a 20 mph speed limit in the 1900’s. Whatever next? Ha, you’ll be telling me you let women drive the blasted things!”

This last statement takes you back, you’d forgotten the prejudice of this bygone age, but you try not to let it show. You give a nervous laugh and carry on unfazed.

“Sir, my car is easily afforded by even an average family, everyone should be able to go from A to B no matter what their social standing”

Another harrumph, “I doubt it can be as well made as Mr Benz’s machines in that case, his are expensive for a reason, they are quality machines, not just for anyone“, he’s not convinced by your arguments. 

“I assure you there’s no difference in quality sir, and in fact mine is easier to use. I mean take a look at enormous hand-crank you need to use to get yours started, it doesn’t look easy to get her going.”

The gentleman smiles, clearly proud, he leans back and pulls out his braces, now in his element “It isn’t, but I’ve become quite the master I can tell you, on a cold morning I can start her in under 15 minutes.“. He looks for your approval, but you frown, his smile wavers when you say “but I can start mine immediately, with a tiny key….” but his frown is fleeting.

Well that tiny thing won’t work old chap“, he’s enjoying himself now, clearly starting to think you’re a bit of a nutcase “You’ll never get the engine turned over with that will you?!

Your patience is wearing thin, “Well with our way of doing things in the future, we don’t physically turn over the engine, we simply…”, but your friend is clearly not listening, he interrupts: “Listen my friend, no cars going to get started unless you turn over the engine, I’m an expert on these things, that’s the way we do things here”.

You make to continue the conversation, but the gentleman stops you, “Listen sonny, I’ve heard it all before and its poppycock, look at what I was offered last week.” He shows you a picture:

As you look over the picture he continues, “That thing looked more like a car that that monstrosity you’re touting, and that didn’t work, why should yours be any different?”…he turns on his and walks away.

Okay, you get the picture, analogy over…

Clearly it’s difficult to imagine the next generation, especially when you compare it to the standard today. It’s also only too easy to translate the message above back into software and “Next Generation” Data and Analytics. The market is still only just beginning and we don’t know what’s around the corner, but here are some thoughts on how to open your eyes to the potential that might be there:

1. Make sure the first thing you do is take a demo. Comparing features, particularly against the “standard”, can only get you so far and your list of features will undoubtedly miss the point – you can’t add features you don’t know about. You’ll unduly bias towards the status quo.

2.  Be willing to change. Democratising data isn’t easy, no one said it would be. It involves turning things on their head and perhaps getting a little bit uncomfortable, you might not be ready to drive at 80 mph yet but you might want to get out of first gear (or even let the women drive!).

3. Take it for a test drive yourself, that propeller driven car might look great in the demo but can you, in fact everyone who has access to data, take it for a spin? Again don’t expect it to be all plain sailing, you might hit a few bumps, but it should be a considerably smoother ride than you’re used to.

4. Be prepared to accept something that looks a bit different than what you’re used to.

With that in mind I’m going to sign off, and hopefully see you at Tableau #data14. Make sure you check out Alteryx while you’re there…you might just be seeing the future…

 

 alteryxlogo_307123

For data’s sake have some fun

We live in times when data analysis as a career is very much in the limelight; Nate Silver, Data Science, Big Data have all helped glamorise data analysis. However data analysis has the perception of being a dull, rather lifeless job – hours pouring over spreadsheets looking at numbers, or creating complex models using lines and lines of code; whether or not this is true will largely depend on which tools you’re using.

I recently heard Tom Brown of The Information Lab, for whom I work, talk about his career and how he ended up using Tableau. Tom described his life before Tableau, using other BI products, as “dull” and this echoes what I hear from a lot of people I speak to who have started using the new breed of Data Analysis tools  and who have started to have fun with data for the first time. My own career started in SAS and SQL, I enjoyed my job but I don’t remember ever calling it fun, for me my career only started becoming fun when I picked up Alteryx.

So what makes these tools fun?

Tableau and Alteryx aren’t the only fun data analysis tools I’m sure, but they’re the ones I’m most familiar with and so from there I can speak more generally about what characteristics they share, and where other BI software manufacturers should look if they want to emulate some of the success of Tableau (and increasingly so Alteryx) at capturing users imagination and creativity.

1. Ease of use

First and foremost to be fun software has to be easy to learn and intuitive, it has to have a level of ease of use that means users can dive right in and start using the product
immediately. It has to have a clean fresh interface that removes the complexity from the data analysis and breaks down the analysis into a set of simple, repeatable steps. Tableau achieves this by giving the user just one screen to build visualisations on and a simple drag and drop interface. Alteryx on the other hand takes a modular approach by providing tools, which are all configured in the same way, that are dragged onto a canvas and joined together to form a data flow. Neither tool has any complex code for users to write, again increasing simplicity.

2. Remove the mundane

No one likes repetitive or mundane actions, and they can quickly take any fun away from using BI tools. I think everyone has experienced the frustration of using Excel and having to copy/paste cells to move them around, or having to write multiple formula repeating the same thing for several files. Alteryx and Tableau both contain several neat shortcuts that remove any mundaneness; simple things like using wildcards in Alteryx to bring in multiple files with the same structure in an input tool are a real blessing when needed.

3. Enable creativity

To really become fun though tools must go further than just be easy to use, they must give their users a freedom to create something. Tableau and Alteryx have this in spades; I could ask 10 Alteryx experts to write solve a problem and they would all use different tools and approaches, no module would look the same. This is part of the appeal for me, solving a problem isn’t about finding the right way, it’s about find a way. Similarly with Tableau, as the recent Iron Viz challenges have seen, a subject can be tackled in many different ways leading to some informative and visually stunning visualisations. User communities that share their work and grow together as they collaborate are also key to having some fun, and the Alteryx Gallery and Tableau Public both enable this. You only need to look at some of the apps and dashboards on their to know that users are really having fun with these products.

4. Mass appeal

Data analysis and BI has mass appeal, Excel is the most widely used BI tool and shows what mass appeal can provide. So to truly become fun for everyone tools must go beyond the niche of Data Scientists / Data Analysts and appeal to everyone with a data background, usability plays a part in this but also they must solve a range of problems across a wide range of industries. As people use a tool to solve a diverse set of problems their enjoyment grows.

Why is having fun with data important?

I’m talking about fun for a reason, not only because I think it’s important for people to feel a sense of worth in what they do and to go to work with a smile, but also because having fun
leads to innovation and growth. If people are having fun with data we’ll learn more about what it can offer, and build richer models and better insights. Part time data journalists,
working at the weekend, will explore public datasets and produce insight and intelligence to improve policy and inform the wider public about key issues. The universe of data is growing exponentially, every gadget and tech now includes an array of data tracking, but the skills to interpret and work with data are still catching up. BI and analytics companies have a responsibility to provide fun tools so that children don’t just experience Excel at school – the world is more fun than Excel. Believe me, I’ve seen and used the tools of the future and they’re fun.

Full disclosure: my love of data and analytics, and in particular Alteryx and Tableau, have led me to work for The Information Lab, an Alteryx and Tableau partner and reseller.

 

Marathon Scrapbook

As many of you will know it’s Quantified Self Month at Tableau and I thought I’d produce an entry for their Iron Viz contest.

My submission is based on my marathon run in April, I’m an avid user of RunKeeper and thought this would make a great subject for the Viz.

The data was collected via Alteryx and turned into a tde before building the Viz using a few internet resource for backgrounds etc.

Click the picture below to open the Viz.

2014-05-15_15-49-33

Scotch Anyone?

I have a passion for nice Scotch whisky, I haven’t tried lots but I try and get a new one every so often. A friend recently asked me for a recommendation and so I decided that, as my geekness knows no bounds, I’d build him a dynamic one rather than recommending something I like.

The resulting Tableau dashboard is below – click the image to access – any comments let me know here or on Twitter, what was your preference? My tipple over Christmas is currently a nice BenRiach 12 Year old.

scotch

The Art(isan) of Data Analysis

Firstly an announcement – I’m moving jobs, from the start of January I’m very pleased to say I’ll be working at The Information Lab, one of the longest standing Tableau Partners in the UK and Tableau’s EMEA Partner of the Year they also very recently became Alteryx partners. I approached Tom, Craig and the team because they have clearly demonstrated a passion with Tableau that mirrors my own passion for Alteryx and, having got to know the ethos of the company and their values, then I’m very excited for what the future holds – for me, my new colleagues and also for Tableau and Alteryx.

All this has got me thinking about our role and how we describe what we do. For their part Alteryx coined the term Data Artisan to describe the people using their software; often those people without analyst in their name but those who find themselves needing to solve problems without the need for coding or IT departments. To be honest I never really got it, but with my new role I started considering the name again and considering my own situation with Alteryx and Tableau and it started to make sense.

For starts let’s look at what those words mean and their origin:

Data, “facts and statistics collected together for reference or analysis”, is the nominative plural of datum, originally a Latin noun meaning “that is given”.

Artisan (according to www.oxforddictionaries.com/) is a worker in a skilled trade, especially one that involves making things by hand. It has it’s origins in the mid 16th century “from French, from Italian artigiano, based on Latin artitus, past participle of artire ‘instruct in the arts’, from ars, art- ‘art'”.

Okay, so technically yes, being in a skilled trade working on facts and statistics for analysis or reference I can call myself a Data Artisan. More specifically my new role will involve instructing others in “the arts” and so this will also ring true.

File:Mendel I 053 v.jpg

An artisan from the 15th century

So, I’m a Data Artisan technically – what about practically? Well let’s consider the tools of my trade:

Data - the raw materials / elements I work with

Alteryx – the tool of choice for data munging / data reshaping / data blending

Tableau – the tool of choice for data visualisation

The Dashboard -  a representation of how the analysis looks that helps people understand the overall story

What about an Artisan’s tools of choice? Let’s consider a painter:

Paint – the raw materials / elements (s)he works with

Palette – the tool of choice for paint blending

Canvas/Brushes – the tool of choice for paint visualisation

The Painting – a representation of how the scene looked that helps people understand the overall story

…and like an artist a “Data Artisan” their skill in telling the story means the result becomes greater than the sum of it’s parts, and they can represent analysis in very different ways by skewing their visualisation towards their own view or political bias.

So looking at it this way then I’m left to think perhaps I am a Data Artisan after all…

As a final, perhaps fatal, push on the metaphor I’d like to ask…would an artist mix his paints directly on the canvas? Would an artist paint his picture on his palette? If you’re a Tableau or Alteryx user then there’s no need to compromise on the end result – make sure you’re being true to your art because Alteryx and Tableau used together are the only way to true masterpieces. [okay I got a tiny bit cheesy there but you get the idea!]

Having said all that I don’t think I’ll be calling myself a Data Artisan too often, I think Paul Banoub (The VizNinja!) said it best when he said:

“… call yourself whatever you want. Call yourself a Ninja, or a Jedi or a Yeti or a data rockstar. I don’t care. Just keep on pushing the boundaries and discovering. You should be proud of yourself for trying.” – Paul Banoub

In future my blogging efforts will be mainly on The Information Lab Blog but I will continue to add things to this blog on a less frequent basis, and will be reviewing the best of the Alteryx and Tableau community in regular posts here.

Thanks for reading.

Appendum

As a tease, here’s the kind of thing you can create in Tableau if you mix your data in Alteryx first. Check in with the Info Lab in the New Year to find out how.

Embedded image permalink

 

Alteryx – Debunking the myths

I spend a lot of time with potential users of Alteryx and there are often several myths or issues that come up time and time again. I can well understand where these come from, I was sceptical on my first use of the software, but these are often easy to debunk and so I thought I’d share some of the common ones with you.

Myth #1: Screen real estate: Alteryx is less efficient than a programming language because the icons take up more room

Often this myth comes from people who haven’t used the software. It’s very easy to look at Alteryx as a new user and equate the strange icons and lines to complexity. So it’s very easy to debunk this myth, once people start using the tool they very quickly start to build up an understanding of how quick it is to build out modules – usually over the course of just an hours usage.

I can’t share specifics but I’ve also seen and heard of head-to-head challenges vs other languages and Alteryx has always come out the most efficient tool, even with a new user at the controls.

Myth #2: Hard to read: The Alteryx flow is harder to read than , I can’t tell what it’s doing at a glance

This is hard to debunk as often it comes from, say a seasoned SAS user, looking at Alteryx for the first time. Yes, those icons take a little time to learn, but as a tool it is completely self documenting and add the annotation available in Alteryx and the modules are immediately self contained process flows which any Alteryx user can immediately look at and interpret visually. Programming languages don’t offer that.

From my own experience, I used to spend time telling the SAS analysts I worked with to document their code to make it easier to read, I’ve never had to do this since I’ve worked with a team of Alteryx developers.

It can take a few module builds to learn what the tools do, compare that to the time taken to learn a new programming language and I think that speaks for itself.

Myth #3: Dumbing down: Alteryx takes away the Analyst’s skill and is too black box. 

If the skill is in writing the program / workflow then yes correct, Alteryx removes all that effort. If you want to build a team of people who can code all day then go ahead, SAS is the right tool for you. If you measure success by pages of code written then Alteryx isn’t for you.

However, if like me you’d rather judge success by the results, and you’d rather your Analysts spent time doing what you paid them for – Analysis – then use Alteryx. It allows people to spend time analysing and interpreting the data and results rather than writing the process.

Also, for the record, Alteryx isn’t black-box – it allows users to delve right into the R code if they need to. However I rarely choose to as the tools are their that I need – and an R expert has written them, so I can benefit from all his experience.

Myth #4: Skills: My analysts want transferable skills, Alteryx is too niche and isn’t transferable.

I refer you to my earlier point, analytical skills are transferable, the language is just the delivery method of those skills. As an analyst I know it’s my analytics  I’d rather invest in.

Also Alteryx isn’t niche any more, their annual conference grows every time I go and there are more and more users globally. The Alteryx job pages speak for themselves. I’ve bet my own career on the fact Alteryx will continue to spread and I have no doubt that it will.

Myth #5: Expensive: I can already do everything Alteryx can do in other tools, I don’t need another piece of software.

This is often the situation I face when talking to people about Alteryx, however I find that Alteryx typically replaces not one tool but several. I no longer need a GIS tool, an ETL tool, a predictive tool, a development tool, etc, Alteryx cuts across all that and provides a single, central tool that can provide instant results. The value of that vs moving data between several systems, and the inherent delays therein is vast.

Alteryx isn’t expensive compared to the value and opportunity it offers, the efficiencies I’ve seen from people using Alteryx over the years are testimony to this. Likewise the stories I’ve heard from people who move jobs and lose Alteryx: “I spent all day doing something that in Alteryx would take me ten minutes – it’s so frustrating, can my new manager come and speak to you about us getting Alteryx”.

Myth #6: Too quick: Alteryx is too quick and flexible and means people won’t spend the time building the problem and so they’ll lose insight

I had this one the other day and nearly laughed out loud. If people seriously want to argue that a tool can be too efficient and that somehow by spending a few days writing a process it allows the analyst to “connect” with the data then there’s something wrong somewhere.

In my experience Alteryx is an eye opener for people, it’s the first time they see their data opened up and they can start to work with it in ways they want. Far from introducing error this gives more opportunity to focus on data and remove error.

I can’t be specific but anecdotally I’ve heard stories of Alteryx being awarded Employee of the Month, I’ve seen results from head to head competitions, I’ve seen Alteryx use cases grow from automating a few reports into a production system automating tens of thousands. I’ll leave you to make up your mind whether that is offering too much flexibility.

To try Alteryx for yourself for free download the Project Edition: http://www.alteryx.com/download

Flights of fancy with Alteryx and Tableau

I’ve created a new visualisation on Tableau Public based on OpenFlights data, partly inspired by this post by Nathan Yao.

The hard work was done in Alteryx, using the R tool:

library(geosphere)
orig <- read.Alteryx("#1")
dest <- read.Alteryx("#2")
inter <- gcIntermediate(orig, dest)
write.Alteryx(inter, 1)

Feeding in two ordered lists of flight data from the OpenFLights routes then gave me a path of fifty points per route, which I could work with and “polish” in Alteryx before visualising in Tableau.

Here’s the result, the full dataset worked perfectly in Alteryx and Tableau but there were too many records for Tableau Public (1 million limit) so I had to cut it down. This just goes to show what an amazing combination Alteryx and Tableau are.

click to interact

click to interact