Every couple of months, Shelley presents at the Women In Tech meetup in Christchurch. These two minute presentations provide a summary of a couple of concepts related to the topic of the night, and provide a few take-homes for the attendees. This content was presented July 19, 2017 at SIGNAL ICT Graduate School.

A lot of articles say that a photo on Linked In will get you 21 times more profile views and 9 times more connections requests.

While I couldn’t verify the source, I did see some research where the eyes were tracked on users while they interacted with LinkedIn, and the tracking definitely focused on the photo first.

And interestingly, while recruiters self-reported spending 4 to 5 minutes per resume, tracking showed that they actually spend an average of 6 secs.

So, let's talk about the photo. It’s going to distract the recruiter from consuming details about you, but without it, they may not even click.

Is it crisp, clear, and less than 5 years old? Are you using it to establish your professional brand? That means no beer or skis in the photo unless you are a master brewer or skiing instructor. No dogs or kids or crazy outfits. You need to look like you are ready to be interviewed or work.

Oh, and don’t forget squinching. Apparently big eyes look less capable.

Other things to consider are the cropping (head and shoulders is good) and the back ground (a plain background but in a bright colour is good).

So basically, make sure your photo validates you, while not being a distraction. You should absolutely consider getting a professional photo taken too. The views go up for those.

Which leads me to connections. On LinkedIn, what is the mean number of connections, for example, and who should you connect with or not connect with?

48.9% of users have less than 300 connections, while 9.3% have more than a thousand. So it is reasonable to focus on quality over quantity.

In general, avoid connecting to people that you don’t respect. Your connections will become a reflection of your values.

It’s reasonable that you will start with fellow classmates and alumni that you like or admire.

Only connect with family who are super supportive of you, or not at all; removing those connections later might be difficult.

Have a rule that you will apply more scrutiny to any connection requests from people you don’t know in real life.

After that, connect with purpose! Subject matter experts in your field, and people whose careers and values match yours, people who will offer a positive review of you if asked by a mutual friend are all good bets. Plan on nurturing authentic relationships.

Don’t be a pest. Don’t go sending out lots of connections to people you don’t know. But equally don’t be afraid to connect to people you consider above your paygrade or outside of your fraternal groups. But do it by changing the default connection message to something specific and personal.

And my best advise? If sprucing up your profile is the first clue you are looking for a new job, don’t let your profile get out of date. Consistent and incremental is best. Consider setting a monthly reminder to update your profile. Plus, it’s way hard to remember what the name of that team building exercise was in 99. (FYI that was a wee shout out to Flight of the Concords and their song Business Time)

When I read these words again in 5 or maybe 10 years’ time, what will I think? What will have happened? Will my assessment of what the future holds be close to the mark or miles off?

Recently I had the privilege of attending the AI meetup where Ronan Whitteker presented on digital art and the growing role AI is playing in generating digital Art. This included graphic art, videos, screenplays and music. Ronan is both a digital artist and passioniate about AI.

As an artist, I entered with a sceptical mind. As a technologist, I was looking to be amazed by possibilities. As a rationalist I wondered - why are we obessesed with trying to get computers to do things that humans are already good at and like doing? As an entrepreneur I thought – why not?

An artist is two things.

Firstly, an artist (musician, songwriter, actor, director, painter, sculptor etc) is someone who tells a story. They then use their media to direct the user’s attention to the story; to hear or see what the artist wants them notice, and, generate the emotion that the artist wanted them to experience. The effect may be beautiful or jarring, comforting or provocative. The artist attempts to engage the audience to think, without preaching at them.

Secondly, we admire artists for their excellence. We admire them for their astonishing ability to play a guitar or piano, or sing in a way that we could never. We admire their ability to remember lines and recite them with expression and timing while moving across a stage. We admire them for their ability to control paint and colour and produce likenesses that we can recognise.

Google

My overall impression was that my sceptical side won out on the night. AI inspired digital art still has an awful long way to go to even catch up, let alone threaten the existence of artists. On both the counts above the digital art falls a long way short. The AI generated screen play contained coherent sentences but no sign of a story and it still required actors directors and costumes for any sign of excellence.

It’s easy to be critical of artists at the cutting edge. Monet, Picasso and Warhol were all roundly criticised early in their careers and they all went on the change the art world.

When I read these words again in 5 or maybe 10 years’ time, what will I think? What will have happened? Will my assessment of what the future holds be close to the mark or miles off?

https://www.amazon.com/Leap-Science-Trust-Why-Matters/dp/0544262018

Like much of my reading lately, this book was selected by Amazon’s machine learning algorithms and all I had to do was click.

Was that trust or laziness on my part? I don’t know.

Trust is the way we collaborate to get things done – it’s how we operate and are successful as a species. Trust is earned over time, but is also applied instantaneously. We frequently have to make ourselves vulnerable and trust someone that we have never met before. Mostly it is a rewarding experience. Occasionally it is painful.

The book is a straightforward read but does deal with topics like Rwanda’s recovery for the Tutsi/Hutu genocide. The concepts were entirely psychological, and while it didn’t deal with current technological trends like Blockchain, it did cover the American political system, highlighting the people’s lack of trust in government that Donald Trump exploited (it was written prior to the elections).

A thought provoking finale reads:

When we think about trust, we need to think about trustworthiness. We need to focus on ourselves. If we want the faith of others, we need to ask: Am I honest? Am I dependable? Do I deliver results? For individuals, the trust building process doesn’t so much begin with faith. It begins with reliability and performance, and we often overestimate how much others believe that we are trustworthy.

For those who want to challenge their assumptions around trust, and its importance in all things that we do, this is a worthwhile read. And, regarding Amazon’s algorithm choosing this particular title for me? A good fit.

One of the biggest misconceptions in business is believing your data is accurate and useful for basing decisions on.

In reality you are looking at a set which has been collated from a range of inputs that have been collected together.

The 'data' is never right or wrong – it is the set that you have assembled (or that someone else has assembled for you) and it is only as good as the assembly process.

Set theory was first developed by Geog Cantor in 1874 but languished without a practical application. The first such application was provided by Ted Codd at the IBM Research lab in 1970 with his seminal paper that described the rules for how relational databases would work.

Some far-sighted educationalists started introducing set theory into the math curriculum as early as 1967, which was controversial at the time with most parents failing to understand how important and pervasive set theory would become.

There are many things that can go wrong when joining data components together. Having a healthy database will ensure fully connected data will allow for the creation of robust sets. The content of the set is controlled by filtering, the assumptions of the collator, and the presence of conditional data - did the person constructing understand all the nuances hidden in the data?

Data can become unhealthy in many ways for example where data entry forms allow ambiguous data entry or allow data that should be stored in one field to be stored in another. Does your system allow records to be saved with incomplete data? How often do your business processes and incentives discourage front line staff from entering all the data that you will need to construct robust sets for reporting and analysis?

Do your report writers and designers ever calculate what the final set should look like before constructing their sets and then compare actual vs predicted, or do they just build a report and hope that it's 'right'?

Does your system store spatial data in a poor relational schema which encourages unhealthy set building? How valid are the datasets that you are relying on? Data only makes it through if it is fully connected - how much of your data is simply hidden from you when you are making your decisions?

There is plenty that can go wrong when compiling a set of data even when the data is in a healthy condition. Much more can and will go wrong when it is not. To make sound decisions for your business you need to have healthy data.

What processes do you have in place to understand the health of your data? Chances are, if you haven't got these processes in place already, then it's not going to happen without outside help. You need to be able to quantify the health of the data in your system. You need to be able to identify problem data areas so that preventative strategies can be put in place. This stops the problem growing. You then have the option of rectifying existing problems should you choose, prioritising on data that affects critical decisions and processes.

Finally you need to be able to measure whether the health of your data is improving or deteriorating over time. By being able to link unhealthy data to the actual costs, losses and poor KPI performance that the business is exposed to, you can demonstrate a clear business case to take positive action.