Categories
development leadership

Sometimes the greatest challenge of leadership is making your boss understand what you do all day

Everyone loves a maverick. The one who bends the rules, who gets the job done. Who leaves fireworks in his wake (it’s always a man) and doesn’t mind breaking a few chickens to make an omelette. The people who rescue the projects in a tailspin, who shout loud and carry a big stick.

What happens to the projects without fireworks? The sysadmins that don’t have to explain why they got breached? The project managers who don’t have to explain why they went over budget? The developers who aren’t in the office past midnight fixing bugs? The people who aren’t visible because they fix the problems before they become visible?

I grew up watching Formula 1 with my dad, in the era of Senna and Mansell. Senna was fiery, pushed the car to the limits, exciting to watch. Mansell was controlled, steady, hitting the right line, and easing the car in as if it was on rails. Senna looked like the hardest worker, but Mansell broke his run of world championships.

If you’re competent, if you get the job done, then people believe that your job must be easy. It’s not easy to beat a triple world champion. Sometimes you need to anchor your achievements in advance, so people understand the challenge in hindsight, especially if you are marginalized in the workforce such that your achievements are minimised.

The Difficulty Anchor

It’s even harder once you move into any management role, success is due to your team (and be damn proud of that success), but failure falls squarely on your shoulders. If you don’t have a strong sense of your own self worth, it can hollow you out, and leave you with nothing but a thick skin.

Take on extra responsibilities, be the consistency for the team when the powers above you are a maelstrom of confusion and musical chairs. And nothing happens.

Remove obstacles and no-one sees them.

Anchor your team’s success. Quiet, dependable successes make everyone on the team happy : no drama, no overtime. But it can be hard to show the work behind the scenes that makes it look so smooth.

It’s not just a dev problem, a good sys admin is invisible, designers struggle to prove their worth (How to Prove Your Design’s Value – Hack Design
https://hackdesign.org/lessons/57-how-to-prove-your-design-s-value?utm_source=newsletter&utm_medium=email&utm_campaign=howtoproveyourdesignsvalue ).

Market yourself. I know you hate sales and marketing, you’re happy to leave it to others, but you need to give yourself the confidence to be proud of your achievements and make sure people understand what you did. Share it with your boss and your peers (you’ll need some recognition when it comes to your appraisal). Promoting yourself is not someone else’s job. Practice it. Embrace it. Be proud.

Categories
development security

Flatter Data

I was watching The Verge summary of The Selfish Ledger, Google X’s thought experiment on what your personal data could do in the future. I started to think about Flatland.

Flatland is a book by Edwin A Abbott about dimensions. In the book, A Square lives in a 2D world, with other 2D shapes, and tries to comprehend the universe when 3D shapes start turning up, but A Square can only comprehend them in slices or shadows/projections.

See this video by Carl Sagan if you want to know more.

The personal data organisations see of us is like the circles projected in Flatland. Google sees the videos I like and the technologies I search for help on. HMRC sees my income, savings, and charitable giving. NHS sees my health.

Companies make decisions on this data, and, like the flatlanders, generalise from the pink circles they see. Sometimes that accurately reflects the brown circles, oftentimes, not. Sometimes what looks like 2 circles is a pair of legs, and what looks like one circle is actually a group hug.

I don’t want companies to disambiguate that. I endorse the spirit of GDPR, that data should only be given up in informed consent (absent the usual rights exemptions for criminals who who violate the rights of others.)

For those of us who work in tech, we need to embrace the ambiguity, and help users and other data subjects understand how they have been categorised. Let them embrace anonymity via randomisation, such as number variance data masking.

You never own someone else’s data, you merely look after it for as long as they let you. It’s not about privacy. It’s not about data. It’s about trust. It’s about ethics.

Categories
data security

Privacy is not your only currency 

If you’re not paying, you’re the product.

But you’re not. In security, we talk about 2-factor authentication, where 2 factor is 2 out of 3 : who you are, what do you know, and what do you have. Who you are is the product, a subset of a target market for advertising, or a data point in a data collection scoop. The former requires giving up privacy, the latter less so.

Advertising is about segmenting audiences and focusing campaigns, so views and clicks both matter, to feed into demographics and success measures. Ad blocking is a double whammy – no ads displayed, and no data on you. Websites tend to argue that the former deprives them of revenue, many users argue that the latter deprives them of privacy.

What you have is money, and who you are is part of a demographic than can be monetised in order to advertise to you to get your money.

But what else do you have? If you’re on the web you have a CPU that can be used to compute something, whether it’s looking for aliens or looking for cancerous cells. If you’re happy to give up your CPU time.

Who else are you? You might be an influencer. You might be a data point in a freemium model that makes the premium model more valuable (hello, LinkedIn).

What do you know? If you’re a human you know how to read a CAPTCHA (maybe), you could know multiple languages. Maybe you know everything about porpoises and you can tell Wikipedia.

Your worth to a website isn’t always about the money you give them, or the money they can make from selling your data. It’s the way we’ve been trained to think, but there’s so much else we can do for value.

Categories
code data programming ux

#dunddd Analyse This : The dangers of big data

Thanks to everyone who came to my DunDDD talk. Lots of interesting questions, although I’m not a lawyer so couldn’t answer them all.

If you want the slides, with references in the notes, you’ll find them here. All the images are creative commons, and you can use the sides yourself under CC by Attribution. Link to slides : Dunddd Analyse This – The Dangers Of Big Data (Google Drive)

If you missed the talk, the arguments I made and the references, apart from the privacy sections, are in this

Link to previous post

If you want the references for the Personal Data and anonymisation parts, have a look at these :

AOL searches are not private

IBM privacy-preserving data mining