Algorithms and AI — who owns the knowledge

A few months ago, I was doing some research for the team and came across an article about the top algorithms teams use. What was interesting beyond the use of algorithms was the history of some of them and how old they are. For example, Naïve Bayesian Classifier is based on Baye's Theorem (https://en.wikipedia.org/wiki/Bayes%27_theorem) who was alive from 1701–1761. Consider that a theorem that is over 200 years old is now being used in predictive analysis on data produced by technology that did not exist then. I think it’s pretty amazing. Of course, there are many examples of solutions and innovations from the past providing answers to today’s challenges. But it’s easy to get caught up in the pace of today’s innovation: new technologies, new ways of doing things and forget that innovation didn’t start with Uber or Twitter.

This also means that the development and use of new algorithms continues.  Of course, companies like Google and Facebook continue to develop and refine their algorithms. But there is a market for actually selling algorithms, which seems somewhat in defiance the spirit of the whole idea in some ways. But then Google and Netflix have made huge amounts of money based off their algorithms so maybe it’s the right way to be thinking about them.

Another niche that took me by surprise and I don’t know why is the degree to which AI modeling depends upon humans tagging/categorizing things. Sometimes AI or machine learning is portrayed as this sort of this all-powerful autonomous aggregator, able to identify and categorize huge amounts of data with a single input. When in actual fact there have been 100s or 1000s of people tagging photographs, reviewing legal documents or medical results, and getting paid for their efforts. All of this work they’re is doing is feeding these vast AI models with the goal of identifying what…I guess everything. Totally unnerving. I really don’t think I want my mammograms made anonymous, then reviewed, tagged, given a result and then dropped into a system to allow matching of clear scans.

But then maybe I do, if I’m compensated.

I think we, the public, need to start taking ownership of the data we create and disseminate if it’s too be reused and leveraged to create something that businesses in turn sell back to us. If we benefited from the public availability of 200 year old algorithm, it seems we should also not be made to pay for the data we in fact created. 

Everywhere you go there, there's empathy

I just finished reading Satya Nadella’s book “Hit Refresh”. It was an easy read with lots of stories about his journey from India to CEO of Microsoft, his evolution as a leader and the impact of his life experience on how he is driving change at Microsoft. If there was one word that I think would sum up the purpose of the book, it is ‘empathy’. And how empathy can transform how we work so that our best work is driven by our need to help and serve.  It’s a word I’m hearing more and more.

I’m in the midst (or more accurately lagging behind) Seth Godin’s Marketing seminar, where he spends almost half the course talking about empathy. Seth mentions empathy, then reminds us of the importance of empathy and follows up with why empathy is key all within a 5 to 10 min video.

The impact of which is that when I look at an ad or watch a video, I wonder who the marketing person had in mind when they created it. Most of the time, I let ads and banners wash over me, almost oblivious to their presence but now I’m looking at them more critically and considering  what I and others put out there.

I’m also hopeful that this may be a turning point in how we do business. You can’t create with empathy and still chase more and more at the cost of the environment, communities and people. I mean you can but you’d be a bit of a jerk,  and also I don’t know how authentic your marketing can be if your empathy stops at the ad, or the brief.

Although, maybe you can take empathy too far. This ad from Timberland may accurately reflect what people are fearing but it’s not exactly offering a solution. 

 

Please. No more empathy.

Please. No more empathy.

Jamestown is mountain relaxed

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We were driving up to Jamestown last Thursday evening to hear some Iive music and eat a surprise dinner. Typically, the Merc has a menu except on Thursday nights, when you’ll eat what they are serving, which is the surprise.

We arrived in town around 7, dodging the furs who have free reign of the town and parked.  We wove through the groups hovering by the door, talking and laughing. Happy voices and swirls of smoke came from two of the picnic tables directly outside the door. They were overflowing with people; a couple of guys looked like they were hanging onto their seats with a single cheek.

A few fairy lights shone above the windows and below the rainbow flag draped. Jamestown is another thousand feet up or more above Boulder, so Autumn’s chill was present.

Most people who live in mountain towns have a particular style and look and Jamestown maybe the mountain style archetype factory for the Front Range.  There were the usual long hippie skirts, tie-dyes and a bit of fleeting fleece flashed, although it’s a bit early for that. But it’s less about what they are wearing and more related to this casual earthy vibe that some of the Jamestown residents  (+ surrounding area) appear to possess. Call it, mountain relaxed or maybe community.

There were so many people milling around the front I was briefly concerned that we might not find a table. But we did. We joined a couple of other people at one of the larger tables near the back. We sat across from two friends, who turned out to be long-time residents of Jamestown.  The music hadn’t started playing yet so we were able to chat: about the music, the flood and rebuilding and The Merc expansion. One of the residents we were talking with talked about how she had lost her home in the flood but had been able to relocate and rebuild.

Jamestown was left stranded by the 2013 flood and the drive up is a reminder of the force and wildness of that flood. They are still rebuilding the road accommodating the new, wider creek bed.  Driving up provides not only physical reminders but also draws up memories of our own home, neighborhood and surrounding communities. And as we travel from the paved part of the road to the under-construction portion, whispers of relief and thankfulness overlay those memories.  We were so lucky, in so many ways.

The music started at 8pm and it was a kinda of bluesy-jazz that had enough tempo to get people up and dancing. We finished up our meals, said our good-byes and found places on the couches in the extended area of the Merc. Several years ago, the Merc expanded by knocking down a wall. They added a couple of  tables and a sort of lounge-area, with a set of couches that wouldn’t have looked out of place in Victorian England yet were surprisingly comfortable.

It was a great spot for both listening to the music and people watching. Couples were dancing, tables full of people were laughing, kids wandered in and out. There was a strong sense of community and comfort. It was the music, the fun people were clearly having with neighbors and friends and the fullness from a good meal. It was mountain relaxed

The three types of Data/Business Analysts you meet in the conference room hall

Late last year, I attended a future of marketing-styled conference. There was a specific track on data and analytics, so I got the ok to attend. The venue was pretty typical. It was held in a huge conference hall, with break out rooms located in a subterranean galley area lit by very dim, yet very ornate chandeliers.  The types of room, where a natural disaster could occur wiping out the entire city and you would have no idea.

Towards the end of one session called the Future of Account-Based-Marketing, we were asked to descend to the bowels of the hotel room and join one of the break out sessions. There were three or four separate sessions focusing on: content, targeting, and of course, data. I joined the data one.

We piled into the room. I became instantly saddened to see that the chairs were arranged in a semi-circle to invite greater participation.  There was not one chair by the exit to allow for stealthy pop-outs. The moderator, a youngish guy with nerdy, black-rimmed glasses reviewed the various themes this working group was supposed to solve for.  Hands remained firmly in everyone’s lap for most of the themes. But hands shot up to discuss the data foundation for successful ABM. It was a good, in-depth conversation with lots of relevant insights, which I won’t be bringing up here (yet). Namely, because I’d rather talk about the insights about the business analysts that I observed.

Our working group was pretty decently sized. There were about 45 of us, more men than women but not by a significant margin. As the discussion progress from technical aspect to business question, the group seemed to self-select into three camps: a) the data idealist b) the  reporters c) the predictors.

The data idealist

I was torn between calling these people quibblers instead of idealist because this group won’t be satisfied until every pulse, vibration from any digital or human interaction is identified, culled, aggregated, threaded and rolled into an attractive bar chart including multiple dimensions. These are not my people. I appreciated their vision but also found it grotesque and although, it may be the way the world is leaning that does not mean I should actively encourage it.

The archetype for this peculiar group was one very earnest, very passionate woman. She worked for the one of the big consulting houses and spoke with quite a bit of authority and experience—the kind of person I typically tend to avoid because they always seem to want to tell me how to do things.

As she was talking, she leaned back in her chair and described how every morning, she reviewed each of her dashboards for any inconsistencies or unexpected changes. That most of her time and energy was spent working with their IT team to understand why certain data was not rendering, the timing of data maintenance, data gaps, etc. She held forth to several people, who nodded their head in agreement, about lack of data, or wrong data, or bad goaling.

Not once did she mention how she was using data to help the business.  For her, at least, the goal of data was the data itself.

The reporters

This type of person has a purpose and it’s to report out on the performance of the organization. They want to provide their teams with the most accurate, most timely insights to help the business understand where they are, how they got here and the progress they are making. They have a solid understanding of their team’s KPIs, and the metrics they need to tell that story.  Maybe not the most creative bunch but solid and extremely smart.

One guy talked about how he’s invited to any meeting that is related to big marketing efforts to gather requirements and then ultimately to report out on progress. He spoke confidently about working with his team and as a true partner, helping to influence what was tracked.  I smiled to see that he was already wearing the requisite conference hoodie and wondered if he relied on conferences for his traveling wardrobe.

He did echo some of the sentiments of the idealist, but it was in the context of how and what he was reporting out on.  Less about data flaws and more about strategic urgency to data issues. It was a slightly yet critically different perspective.

The predictors

These are the visionaries. The take the characteristics of both the idealist and reporter add a touch of fortune-telling to conceive of a world, where we begin to anticipate the needs of our clients/patients/customers/etc.  They don’t appear to get mired in the minutia of the data—this is not to say that they don’t have a deep understanding of the data foundation but I got the sense that they choseto trust the expertise of their data architects.

As this one guy said, “once we have the foundation, we know what metrics to watch for to monitor the health of the company, we’re now free to explore and innovate with data. It’s good to be able to know what’s going on now but it’s better to be able to forecast and potentially influence tomorrow."

He was one of these people who is simply fun to listen to. Someone who has a gift for translating pretty complex concepts and restating them in a way that everyone can grasp. These are not my people either, but I aspire to be.

He worked for a start-up and as a data person actually was actually involved with the executive team so that he could plan his data strategy. Because data architects and data architecture costs money and visualizing KPIs are not free either so data was just part of the way they operated.  When I asked him later how large his start-up was, he did concede there were only 10 people. But I still was impressed that any company, especially a start-up, would include their business analyst in planning discussions. 

I’m not that type

I suspect many of us in this field rangewithin this spectrum of types and I believe must.  Sometimes the situation dictates the need: integrity of the data vs. standardization on KPIs vs. the next waterfall model.  The one overriding goal that I think is critical is keeping the business in mind when you’re working with data.  Ultimately, make sure that what you areanalyzing helps the business meets its goals.