Lauding Reshma Saujani and Girls Who Code

There is much to laud about what Reshma Saujani has come up with and what she aspired to do by 2020.  For one, Girls Who Code a pragmatic humanitarian effort that supports a disenfranchised segment of our population, specifically on technology, and it instills confidence in these young women in ways that their families and societies probably do not.  For another, it harkens to an old adage: Give a person fish, and she has food for a day.  But teach her how to fish, and she has food for herself, her family and others for a lifetime.  In essence, Saujani speaks to the essence of what I've conceived as Theory of Algorithms and The Core Algorithm; that is, you learn literally the underpinning of how technology works (coding), and with this grasp you knock on the doorstep of an enormous house of options for jobs and possibilities for innovation.

MISSION: Girls Who Code programs work to inspire, educate, and equip girls with the computing skills to pursue 21st century opportunities.

VISION: Girls Who Code’s vision is to reach gender parity in computing fields. We believe this is paramount to ensure the economic prosperity of women, families, and communities across the globe, and to equip citizens with the 21st century tools for innovation and social change. We believe that more girls exposed to computer science at a young age will lead to more women working in the technology and engineering fields.

PATH TO SUCCESS: The U.S. Department of Labor projects that by 2020, there will be 1.4 million computer specialist job openings. To reach gender parity by 2020, women must fill half of these positions, or 700,000 computing jobs. Anecdotal data tells us that an average of 30% of those students with exposure to computer science will continue in the field. This means that 4.6M adolescent girls will require some form of exposure to computer science education to realize gender parity in 2020. Girls Who Code has set out to reach 25% of those young women needed to realize gender parity.

Bravo!

Value of Ideas Trumps Value of Products

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In a well-marked line from the movie The Social Network, Facebook founder Mark Zuckerberg turns to the Winklevoss twins, who are suing him for stealing their invention, and says: "If you guys were the inventors of Facebook, you'd have invented Facebook." The words speak volumes about the origins of one of the most successful companies on the planet, but are also a commentary on the origins of any invention.

"Anytime you invent something, you have really invented two things—the thing itself, and an idea," says Harvard Business School visiting professor Gautam Ahuja, a professor of strategy at the Ross School of Business at the University of Michigan. In the case of Zuckerberg vs. the Winklevosses, the twins may have had created a simple interface for college kids to connect with one another, but it took Zuckerberg to take the idea and turn it into that of a worldwide social network that would allow everyone to share their lives with one another across geographies.

"Compared to the value of the global network idea, the value of the actual product of a platform for college kids was much less," says Ahuja. "Often the concept value of the invention is more important than the physical aspect."

In a paper published last year in the Academy of Management Review called "The Second Face of Appropriability: Generative Appropriability and Its Determinants," Ahuja makes distinctions between two types of value: "primary appropriability," or a company's' ability to exploit the opportunity of an invention by turning it into a product, and "generative appropriability," a firm's ability to capture the later value inherent in the idea.

"Often companies don't fully exploit the latest ideas that their product has created," says Ahuja, who wrote the paper with Curba Lampert of Florida International University and Elena Novelli of City University London. "They go on and create new products and inventions without realizing the potential for building new products out of their existing inventions."

The idea, in the way that Ahuja emphasizes it, is akin to how I define and use algorithm à la Theory of Algorithms and The Core Algorithm. An algorithm is the conceptual underpinning of something, the essence of how it works, and the DNA that governs its evolution. For example, the essence of how an automobile runs is via the combustion of gasoline, which releases energy to power it. The auto industry has banked its numerous business worldwide, over the past century, on this very idea or algorithm. In fact, with the advent of hybrid and electric vehicles, we can distill that essence even further: They run by tapping potential energy in the natural or manufactured resources provided.

For companies looking to increase their generative appropriabilty, Ahuja offers advice as well. [1] "Push for stretch goals in innovation," he says. Under pressure to produce in a short period of time, research teams are necessarily forced to go back and find new value in what has already been done than reinventing the wheel.

[2] Likewise, firms are better off providing moderate levels of resources—not too much and not too little—to research teams. "If the budget is too small, there is little possibility of creating new products," says Ahuja. "But if it's too high, there is no incentive to go back and look at your own ideas." With a level in the middle, R&D teams will both forced to look at current ideas and able to take them in new directions.

[3] Finally, says Ahuja, it's essential to put a knowledge management system in place that provides incentives for new designers to talk to old-guard engineers rather than just reading about their inventions on paper. He cites the words of former Hewlett-Packard CEO Lew Platt: "If only HP knew what HP knows, we would be three times more productive." Just the interchange between old and new designers can help generate new ideas from old ones.

Reference: Inventing Products is Less Valuable Than Inventing Ideas.   

Over the last five years, I have learned really tough lessons in entrepreneurship, and these lessons fall perfectly in line with what Ahuja surmises and advises.  Early on I was able to secure good financing, which, unfortunately, I used too quickly and unwisely.  There were a lot of concepts, models and ideas I still had to work through, and clarify, and the inevitable mistakes I made ended up being expensive ones.  For example, I engaged website developers who were not competent or scrupulous, and I had to dismiss them in an effort to cut my losses.  It was when I was virtually depleted financially that I figured out how to create the platforms for my many projects - that is, websites, blogs and social media - in ways that served my purpose and at costs that were nominal.  It was a combination of [1] and [2] above: I was under pressure to get my businesses and projects launched, and I managed to work effectively with minimal resources.   

The Inventive 13-Year Old Richard Turere

In the Masai community where 13-year-old Richard Turere lives, cattle are all-important. But lion attacks were growing more frequent. In this short, inspiring talk, the young inventor shares the solar-powered solution he designed to safely scare the lions away.

Transcript

This is where I live. I live in Kenya, at the south parts of the Nairobi National Park. Those are my dad's cows at the back, and behind the cows, that's the Nairobi National Park. Nairobi National Park is not fenced in the south widely, which means wild animals like zebras migrate out of the park freely. So predators like lions follow them, and this is what they do. They kill our livestock. This is one of the cows which was killed at night, and I just woke up in the morning and I found it dead, and I felt so bad, because it was the only bull we had.

My community, the Maasai, we believe that we came from heaven with all our animals and all the land for herding them, and that's why we value them so much. So I grew up hating lions so much. The morans are the warriors who protect our community and the livestock, and they're also upset about this problem. So they kill the lions. It's one of the six lions which were killed in Nairobi. And I think this is why the Nairobi National Park lions are few.

So a boy, from six to nine years old, in my community is responsible for his dad's cows, and that's the same thing which happened to me.

So I had to find a way of solving this problem.  [1] And the first idea I got was to use fire, because I thought lions were scared of fire. But I came to realize that that didn't really help, because it was even helping the lions to see through the cowshed. So I didn't give up. I continued.  [2] And a second idea I got was to use a scarecrow. I was trying to trick the lions [into thinking] that I was standing near the cowshed. But lions are very clever. (Laughter) They will come the first day and they see the scarecrow, and they go back, but the second day, they'll come and they say, this thing is not moving here, it's always here. (Laughter) So he jumps in and kills the animals. So one night, I was walking around the cowshed with a torch, and that day, the lions didn't come. And I discovered that lions are afraid of a moving light. So I had an idea. Since I was a small boy, I used to work in my room for the whole day, and I even took apart my mom's new radio, and that day she almost killed me, but I learned a lot about electronics. (Laughter) [3] So I got an old car battery, an indicator box. It's a small device found in a motorcycle, and it helps motorists when they want to turn right or left. It blinks. And I got a switch where I can switch on the lights, on and off. And that's a small torch from a broken flashlight.

So I set up everything. As you can see, the solar panel charges the battery, and the battery supplies the power to the small indicator box. I call it a transformer. And the indicator box makes the lights flash. As you can see, the bulbs face outside, because that's where the lions come from. And that's how it looks to lions when they come at night. The lights flash and trick the lions into thinking I was walking around the cowshed, but I was sleeping in my bed.

(Laughter) (Applause) Thanks.

So I set it up in my home two years ago, and since then, we have never experienced any problem with lions. And my neighboring homes heard about this idea. One of them was this grandmother. She had a lot of her animals being killed by lions, and she asked me if I could put the lights for her. And I said, "Yes." So I put the lights. You can see at the back, those are the lion lights. Since now, I've set up seven homes around my community, and they're really working. And my idea is also being used now all over Kenya for scaring other predators like hyenas, leopards, and it's also being used to scare elephants away from people's farms.

Because of this invention, I was lucky to get a scholarship in one of the best schools in Kenya, Brookhouse International School, and I'm really excited about this. My new school now is coming in and helping by fundraising and creating an awareness. I even took my friends back to my community, and we're installing the lights to the homes which don't have [any], and I'm teaching them how to put them.
So one year ago, I was just a boy in the savanna grassland herding my father's cows, and I used to see planes flying over, and I told myself that one day, I'll be there inside. And here I am today. I got a chance to come by plane for my first time for TED. So my big dream is to become an aircraft engineer and pilot when I grow up.

I used to hate lions, but now because my invention is saving my father's cows and the lions, we are able to stay with the lions without any conflict. Ashê olên. It means in my language, thank you very much.

What a delightful, instructive TED Talk by one Richard Turere.  I see his efforts as good thinking and sound problem solving.  In the transcript, I italicized key actions he took and numbered the three ideas he tried out, as he worked at solving a serious problem.  Indeed this young man is curious and inventive, and these traits help in problem solving, but the process he went through is something that we as leaders and staff alike, anyone really, also go through.  Finally, he has a generous, benevolent spirit: He installed his invention at other homes, and he taught friends how to install it as well, all without killing the lions.

McKinsey: Redefining Healthcare and Banking

Medicine today is essentially an art, which relies on heuristic judgment by highly skilled professionals which are distributed around the world.

When my mother took seriously ill early in 2012, she needed care from a range of physicians, such as neurology, pulmonary and infection.  For the most part, the nursing staff kept them informed.  But on occasion, a physician will ask us questions about what another physician has said or done, and it was clear there were some gaps in the coordination of care.  So what Nicolaus Henke said about Big Data and Analysis as pulling disparate aspects of care together is crucial: There should be no reason why physicians are not clued into what each of them is doing with a particular patient. 

Big data improve efficiency and effectiveness of clinical trials, and thus improve topline and bottomline.

I want to emphasize what Sam Marwaha said about case for action: Before embarking on Big Data and Analytics, be clear on how and where the outcomes will be used.  There must be a purpose or an endgame to what you do.  In fact what you're trying to accomplish must determine what you do.  Moreover, as with his colleagues, Marwaha talks about case for action, based on value: I interpret this to mean ROI essentially.  That is, value is getting back more than what you put in. Outcomes from Big Data and Analytics are therefore meaningful, effective and efficient.  Given tighter finances, resources and regulation in healthcare, the potential benefits of Big Data and Analytics are huge.

You really have to make sure that third-party data you tap is good data and the right data for you.

The banking case study that Toos Daruvala talks about essentially had outdated algorithms (underwriting models) for distinguishing good risk from bad risk and their success rate was correspondingly low.  So management took a 360° view of their customers across their entire organization to gain holistic, that is, complete and precise, insight on risk.  Any data you collect, such as third-party or social media, must be good data to begin with, and it must the right data to realize that insight.  In brief, then, you must clear on (a) what you're trying to accomplish, (b) what you need in this regard, and (c) how you'll make it all happen (rf. Part 4 - Achieving Organizational Aims).

McKinsey: Putting Big Data to Work

Leaders, pay attention: Data analytics help you predict and optimize better, and thus make more money. 

If your competitors have slid ahead of your company, it is important to pause as soon as you can and reflect on why and how and what.  In the preceding article, I emphasized (a) clarifying your purpose at the outset (i.e. begin with the end in mind) and (b) determining all that you need to do to serve that purpose.  Your competitors may have drawn on Big Data and Analytics, in which case you need to move purposefully to catch yourself up, as David Court rang an alarm on.  But even if they haven't done this in any concerted or formal way, Big Data and Analytics ought to be a serious consideration vis-a-vis your purpose.

It's like "Moneyball": Your manager and coaches have to buy into it, or your analytics go nowhere.

What the film Moneyball dramatized is how the General Manager of the Oakland Athletics navigated the need to rebuild the team and bring talent in, while working the constraints of a very tight budget.  He determined to tap statistics and analytics, but it was a major struggle to convince the constituents in the organization to follow suit.  For one, the scouts didn't quite understand Moneyball.  For another, the manager didn't quite buy into it.  Consequently the GM had to work diligently to build capability (understanding) and appetite (motivation) among them.

It always comes down to three things: data, models, transformation.

Court reiterates what I've emphasized: Transforming your organization means delving in your people, and making sure they have the requisite knowledge, skills, motivation and follow through to reap the benefits embedded in Big Data and Analytics.  Moreover, he calls for the bimodal athlete, that is, managers and staff who have business acumen + technical facility.

Do not over-reach with your Big Data initiative: Keep things simple and manageable, and focus-focus-focus!

Any business has several moving parts, all connected in a dynamic, complex web.  The kind of focus that Court talks about doesn't mean you as the CEO and your people put on the sort of blinders we put on race horses.  Instead, focus is about prioritizing two or three aspects, while keeping the rest of your business moving forward as usual.

McKinsey: Making Data Analytics Work

To hear it from management consultants, data scientists, and CEOs of firms in this space, is to get the impression that Big Data and Analytics is the saving grace or the holy grail of competitive advantage. Moreover, it is to believe that this is the endgame for any smart company out there, especially if they seek to lift revenue and profit markedly.  It is to think that this is the new wave in how to run a business, serve customers, and transform the organization.

I say, Let's put Big Data and Analytics in perspective, and begin with what you, as the CEO, and your leadership team are trying to accomplish with your business.  What are the broad and particular targets you're aiming to hit, and how well have you done so?  What is the essential reason or purpose you're in this business, and what values do you and your people hold near and dear?  What challenges, obstacles or issues do you face, have been facing, and anticipate facing?  What are the needs, gaps or opportunities in your customer base and in the broad market, which you seek to tap into?       

You see, the major irony of Big Data and Analytics is it does not begin with Big Data and Analytics, but with the fundamentals of your business: again, purpose and reason, targets or aims, challenges and opportunities.  The next step is to take as hard and unvarnished of a look as possible about what you and your people need to do to serve this purpose.  There may be numerous things to look at: from infrastructure and equipment, to operations and process, to sales and marketing, to leadership and workforce.  If you believe at this stage that you need better insight into all of these, more effective means for making decisions, greater effort at tapping potential, then bring Big Data and Analytics dead center in your radar.  You explore its possibilities not as an end in itself, but as a means of serving your purpose.      

You need to be very clear on what kind of business value you want to create with your data transformation.

What Matthias Roggendorf articulates is sound enough vis-a-vis my opening remarks: Be clear on what you expect Big Data and Analytics to do for your business.  I might add, however, that the benefit you need to demonstrate with this effort is largely determined by how well Big Data and Analytics serves your purpose and how necessary it is in relation to your targets.

Working data analytics is about getting hardware + software + PEOPLEware right. 

I attended a Big Data and Analytics shindig with IBM two years ago, and one speaker emphasized, as Matthew Ariker did, the need to build capability.  The speaker also included the need to build appetite in the organization.  Again, tech firms and analytic consultancies may focus on technology, statistics, and process, but without due attention to people matters, the best of what they have to offer will fall like a lead balloon.  What are these people matters?  Skill sets, previous experience, requisite knowledge, proper motivation, clear commitment, and right buy in.

Manage data + run mathematics + just do it = winners vs losers.

Tim McGuire does a fine job of beginning with the end in mind, in relation to his clientele.  You may call it starting with a hypothesis as you do in science or identifying what problem you're trying to solve, but it speaks exactly to what I advise.  Without clear purpose at the outset, you and your people can soak up enormous time, effort and resources going nowhere.