Louise McGregor Digital

Welcome to 2018

One sure prediction is that the new year will bring a slew of predictions, some glowingly optimistic and some confidently pessimistic. I’ve sifted through the predictions in the digital world, and here’s my summary, plus a New Year’s gift.

Artificial Intelligence

As the use of big data, algorithms and the digital technology has evolved artificial intelligence has moved from the esoteric into real world.

It turns up in marketing, with a series of caveats. It’s behind an app to identifying snakes, with caveats about what that does for our relationship with nature. It’s contributing to how we brew beer and AI is what makes chatbots smart enough to be helpful.

The Webby Awards Trend report notes that we still trust humans over AI, but I suspect we’re not always aware of where AI impacts our lives. We will see more practical adaptations of AI in 2018.

Virtual Reality

I like the idea of virtual reality, but my experience so far has been that it doesn’t add enough to my experience to compensate for the awfulness of the headset. Frankly I’d rather read a book and imagine the worlds. So the story-telling in VR needs to improve, and the devices need to get better.

So far the biggest use seems to be in gaming but even there users are underwhelmed, the Economist reported in December that VR has failed to live up to it’s hype, and added that there is a “distinct whiff of urgency in the air” as VR struggles with poor equipment and unsatisfactory content.

The devices are starting to get lighter and prettier, however they’re still relatively expensive.


Will 2018 be the year that the devices and the experiences improve?

Blockchain

Blockchain is the technology behind bitcoin and other crypto-currencies, it has other uses in making digital information exchange more trustworthy.

We’ll see these tests scale more widely along with more novel uses, I’m sure there are smart people out there looking at how blockchain could be used more broadly to securing our online identity.

Bots

Bots get a lot of bad press, they were exploited in the 2016 election and throughout 2017 to deliver false information to a screen near you, eroding reasonable debate and internet freedom according to Freedom House reports.

However due to advances in AI bots are starting to get better at customer service than humans. Will 2018 be the year we pass the Turing Test on a help-desk call?

Cybersecurity

This remained a big issue for business last year with major breaches in a range of industries from food retail, email, healthcare and governments.

Companies spend increasing proportions of their IT budget on cybersecurity and introduce restrictive measure to protect their data (USB sticks are frequently banned for example), 2018 will be the year of innovation in cybersecurity as companies struggle to reassure customers that their data is safe.

Social Media

Social media will become even more commercialised, expect more of those ads on Instagram, and more promoted posts – and more ways to promote content – on all platforms as the pressure increases for the platforms to be profitable and for company use of social media to demonstrate a return on investment. This won’t be pretty.

On the plus side we’ll see more tweaks on the platforms to encourage engagement; expect more platforms to adapt the Facebook emoticon model, and more uses of video and live-streaming.

My New Year’s Gift

In an attempt to be more consistent with my blog posting I developed a content calendar, I’ve added the various “International Day Of…” dates that might be useful along with a few significant birthdays and events. I’ve added a few content ideas, and I’m sharing the framework so far in case anyone else finds it useful.

2017

As we say farewell to 2017, here’s a reminder of what the world thought was worth searching for – it’s a two minute film from Google based on search data. It’s like a time capsule of the year.

 

Image: New Year’s Day  |  geralt on Pixabay  |  CC0 1.0

Louise McGregor Business

Believe Data

We were sailing back to our home port and a dense fog descended. Suddenly we couldn’t see more than a boat length ahead. My father, a mariner by profession, plotted a course and steered by it, sending my brother and me forward as lookouts.

My mother was convinced we were sailing in the wrong direction, that we’d steered off course (and this was before the reassurance of GPS). “No,” said my father “you must trust your instruments”.

We made it safely home; it was an early lesson in believing data.

The amount of data produced and collected every day continues to grow. “Big Data” is a well-known, although poorly understood term. In many companies we’ve moved on to “data-driven decisions”. But we’re not always good at believing the data.

I was in a meeting recently where the most senior person in the room looked at a graph of twitter follower growth and said “I just don’t believe this data”. The data showed that goals for follower numbers would not be met. Leaving aside the argument on whether follower numbers is a good goal, the data don’t lie. If there’s a straight line of progress that won’t reach the goal then you need to change something or accept missing the goal.

It made me think about when we believe data and when we should be sceptical.

We tend to measure progress against an expected path, and in a large organisation invariably report that progress upwards in the organisation. In our plans and projections that progress follows a nice upward curve. But the reality is different, every project encounters setbacks, and the graph is more jagged than smooth.

In fact a smooth graph, where targets are always met should raise questions.

Years ago I was chatting to a guy who left his previous company after about four months. He left because the targets for the quarter were increased by 25%, and everyone met them. As an experienced business person he knew that a situation where every business unit met the stretch goal in the first quarter it was applied was very very unlikely. His suspicions were raised and he left as quickly has he could. A year later the company collapsed under its own lies. The company? Enron.

In his articles (and books) Ben Goldacre campaigns for greater journalistic care in reporting data, and better education on scientific method. He points to the dangerous habit of pharmaceutical companies in cherry-picking their data, choosing studies that support their product and ignoring those that don’t.

I said earlier that we should trust the data, but we also need to know how the data was collected, what errors might be inherent in the data collection methodology, and what limits there might be to interpreting the data. This should be part of everyone’s mental toolkit. It would help us evaluate all those advertising claims, refute 90% of the nonsense on the internet, be honest about progress to goals, and finally make data-driven decisions.

 

Image; Data via pixabay

 

 

Louise McGregor Business

Web Summit Highlights; Day Two

I headed to the marketing stage this morning, and ended up spending most of the day there.

Content is King

A discussion with Adam Singolda (Taboola) Ian White (Sailthru) Sean Moriarty (Demand Media) Michael Learmonth (IBT)

Brands are more important in digital; as the due to noise:signal ratio grows, branded content helps viewers/customers find quality.

Brands need a content strategy, it’s not enough to just push content out there. Need a strategy behind it, and to measure the value to readers. Keep the ROI high, this allows you to keep building quality content.

In conversation with Clara Shih

This was the most relevant to me today, and I found myself agreeing with everything Clara Shih said.

Social is normal for people in their personal lives, it will become the standard operating procedure for companies. It always takes a decade or more to operationalise these things for enterprise, it seems to take a while for the change management to kick in.

Must understand social throughout the company – it can’t just be a team sitting in marketing – but through the company including the C-suite.

Shih sees 3 trends for social;

  • social becomes a service layer on top of everything; IoT, wearables
  • more data, meta data = shift towards hyper targeted “segment of one”
  • customer will expect exchange of data to give them something in return

Connected World

Raced over to the Machine Summit to hear a colleague talk about the Connected World. There was a queue to enter to prevent overcrowding, I was about the last person they let in.

There was some discussion on the opportunities of a future connected world. More features on devices came up as one option, for example adding a camera to a Roomba so that you can document what happens if your house floods – all I could think of was put a camera on it and film your pets. I guess that’s why I’m not working in connected devices.

One of the biggest challenges in this area was data standards and privacy questions. If you extract data from a device how do you protect that?

  • Explain what you do with the data in a way people can understand
  • Do a better job of always making it “opt in”
  • Define and share best practices around privacy and security on collection, anonymising, use and re-use of data
  • Privacy and security seen as a base layer – beyond that let people choose what to share

The future of connected – in the next five years?

Move from thinking about discrete devices to infrastructure and embedding connection into our homes and workplaces.Move to network of devices, and move to connected services. Move to configuration of homes for different purposes, eg; your home office disappears when guests visits.

Joanne Bradford from Pinterest

Introduces Pinterest as “inspiration plus action”, people use it to design their homes, think about their wardrobe, get inspired about exercise, collect recipes. The engagement is high because people use their boards. (OK, I’m the exception).

It was a platform created as a series of communities, starting with mum bloggers, and that meant it was under the radar in Silicon valley to start with.

They still see that community matters and arrange events for pinners, and invite them to press events.

Some stats;

  • 750M boards
  • 300B items
  • best pins have great image + useful link + good description
  • #1 category = comedy

Future of Media with John Ridding (Financial Times)

Always believed in the value of quality journalism, even when others saw a crisis of print media and declared that no-one wanted to pay for content. The mantra was “internet wants to be free”, but the internet is a channel so has no desires of it’s own.

More than half of their revenue now comes from Digital, and it’s the subscription model, not the ad revenue that’s winning it for them.

Innovation Divide

The challenge of getting some “start up” innovation fire into large enterprises, and an inside peek into the fantastic Unilever Foundry, which is great way of bringing fresh ideas and working them into something practical.

Pointing out the dangers of perfection mindset this presentation gave me the quote of the day

every day that a good idea sits in a powerpoint presentation is another day that the idea dies.

Keith Weed, CMO, Unilever

They’re one of the world’s big spenders when it comes to marketing, and they continued spending through the crisis although the breakdown of where that spend goes is shifting.

Marketing spend winners in general are social, search and increasingly mobile. But in terms of social media spend there isn’t a “winner takes all” platform as it makes sense to use multiple platforms depending on your purpose.

For consumers in social there are ongoing privacy and trust issues, right now technology is ahead of regulation, there are things being developed in technology that legislators don’t understand. I’d add that customers understanding is also often behind – even as their expectations grow.

As the Dutch saying goes “trust arrives on foot and leaves on a horse”

Brands have an opportunity to solve this ahead of regulation, and build trust with customers.

It was another packed day – lots of great speakers. The agenda on the marketing stage was rather random, as adjustments had to be made for speaker availability. So it was a bit of a surprising day, the other – less happy – surprise was the wifi. It wasn’t keeping up with demand, so my “life tweeting” was all over the place. OK, for an attendance of 20,000 people I guess it’s a challenge.

I’d still like an answer to my sheep tweet though.

Louise McGregor Buzzword

Big Data

Big Data is often touted as a solution to all our problems, a panacea for all ills often by people who struggle to define it. So what is big data and what kind of problems has it solved?

Big data refers to sets of data so big and complex that they cannot be analysed by traditional methods and tools, but which release new value when analysis is achieved.

Google translate is an example of a problem solved by the use of big data. Although the translations are imperfect they are often good enough to have an understanding of what the writer intended whatever language it was written in. Google does this by statistically analysing millions of documents online that exist in multiple languages and figuring out what is most likely to be a correct translation. The more documents available that have been accurately translated by humans the more accurate the Google translation will be.

Big data analysis has been used in predicting maintenance needs for UPS, New York city council and various car manufacturers. It’s been used in healthcare to predict the onset of infections in newborns, and outbreaks of flu.

So it sounds like it could solve some tough business problems, and it can. But it has limits.

  • messiness of data means tricky to anaylse and interpret – google translate occasionally gets the translation between Dutch and English completely wrong, and this is a language pair that must have millions of documents, you need good analytical expertise and data governance to get the valuable insights out of the data.
  • hidden biases in data collection, for example if you’re relying on smart phone data  you are probably selecting against the lowest income earners.
  • identifies correlation, but that explain causality and doesn’t necessarily tell you what to do.
  • privacy concerns; relating to the collection, use and reuse of data. People may not realise that if enough anonymised data is combined it is possible to identify an individual.

And sometimes all that extra data may induce a sort of paralysis by analysis, a belief that you could make the perfect decision with just a little more data.

Right now we’re only beginning to unlock the value of big sets of data, and it’s still very much in the hands of the experts. It’s going to take some re-learning for managers/business leaders to ask questions that big data can answer, and to understand that correlation does not imply causation.

image: geralt via pixabay