Tweets for week of 2018-05-06

May 13th, 2018 No comments
  • RT @rayidghani: Machine Learning can help improve society but we need to make sure policymakers and developers can not only think about dis… 2018-05-10
  • RT @datascifellows: Aequitas, open source bias audit toolkit, now available for policymakers & machine learning developers to audit machine… 2018-05-10
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Tweets for week of 2018-04-15

April 22nd, 2018 Comments off
  • RT @NPR: Carl Kasell "was grateful for every day he was able to speak to you. And we were grateful for every day we got to spend with him,"… 2018-04-17
  • RT @acroll: Every time I get one of these "our terms have changed" mails from a tech company (and there has been a flurry of them in the pa… 2018-04-21
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Tweets for week of 2018-04-01

April 8th, 2018 Comments off
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Tweets for week of 2018-03-25

April 1st, 2018 Comments off
  • RT @CBSNews: "We are the survivors of unjust policies and practices upheld by our Senate. We are survivors of lack of resources within our… 2018-03-25
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Tweets for week of 2018-03-18

March 25th, 2018 Comments off
  • Rayid's three suggestions on how data should be handled are worth understanding. “Why what Cambridge Analytica did… https://t.co/s1hrhwCZyo 2018-03-20
  • RT @BarackObama: Michelle and I are so inspired by all the young people who made today’s marches happen. Keep at it. You’re leading us forw… 2018-03-24
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Tweets for week of 2018-03-11

March 18th, 2018 Comments off
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Tweets for week of 2018-02-25

March 4th, 2018 Comments off
  • RT @GWR: .@NASA astronaut @AstroPeggy Whitson is an incredible icon in the world of science as the world's oldest female astronaut.

    The s… 2018-03-03

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Tweets for week of 2018-02-18

February 25th, 2018 Comments off
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You Have 100 Days to Lead a Data Revolution

February 22nd, 2018 Comments off

Most data resides unused on corporate servers—information that, if unlocked, could create sustainable competitive advantage. That’s why smart companies are looking for digital change leaders to guide their organizations through the transformation around using data rather than just collecting it.

We’ll call these change agents Chief Data Officers, or CDOs, although they aren’t always called by that title—they may be the CIO, Chief Digital Officer, or lead a line of business. It might be you. Whoever it is, the organization is relying on this person to lead change that probably includes parts of all of these:

  • Extracting more value from existing data
  • Monetizing data as a new revenue source
  • Leading a cultural shift around data-driven decision making
  • Tearing down data silos
  • Ensuring data privacy and security
  • Taking on dry but crucial issues such as data governance

This all means big issues, lots of moving parts, and a high-risk challenge for your career. In all likelihood, the board is watching intently.

If you’ve been asked to take on this challenge, where do you start? We believe CDOs have 100 days to get this digital transformation rolling downhill and towards a successful conclusion. If the basic building blocks aren’t in place and moving towards real progress by then, there is trouble ahead.

But before we start on how to make this crucial transition happen, we need a brief look at why it is occurring now.

Technology Is Changing the DNA of Business

Chances are the company you work for has evolved dramatically over the last decade, spurred by transformational shifts in technology. These shifts started with open source software, allowing enterprises to access innovation at lower cost and without having to invest in long-term platform decisions. Then, the development of public and hybrid clouds enabled companies large and small to compete at scale and reduce time-to-market for technology solutions. And now, the primary change-driver has shifted once again: to the availability, analysis, and use of data.

Data is everywhere—it flows in from products reporting on their own performance, from digitally-linked business partnerships, from video of shoppers’ traffic patterns around a store, from IoT sensors around the world, and from field reps using mobile apps to retrieve up-to-the-minute customer information before making a call.

Companies are rushing to digitize big swaths of the business that haven’t been previously, instrumenting more and more business processes to capture the data they need to fuel improvements and make better decisions. Some have realized they know more about how a user moves through their website than they know about how a new product makes it to market.

Because of these technological earthquakes, the expectations and skills of today’s knowledge workers around data have evolved rapidly. Individuals who are data-conversant and understand how to connect data, systems, and decisions together are in high demand—just ask GE, which last year moved its headquarters from Connecticut to Boston primarily to be closer to technical talent.

The First 100 Days

A CDO’s first 100 days should lay a foundation for iterative—but fast—action. The change agent must educate leadership about what needs to be done, and drive home the idea that nothing less than a new way of thinking about achieving business goals is being introduced.

The CDO needs to be an evangelist for the art of the possible. What business actions and decisions can be improved by better data? What unused data can be put to work? By evangelizing what can be done and the ensuing benefits, the CDO helps people overcome their resistance to change.

The value of experimentation is another critical message of this evangelism. Enterprises that can formulate a new hypothesis, construct capabilities to test the hypothesis, gather data about outcomes, and then iterate and try again, have a strategic advantage over their competitors.

One hundred days is not a long time. Let’s get down to specifics. Starting on the road to implementation requires understanding of three things:

Understand the Business Goals

First stop on this journey is understanding how data advances business goals. In short, what does the business need to accomplish? All explorations of data and capabilities should be grounded in what the business needs to ensure you aren’t building capability for capability’s sake. How will you demonstrate to the business the impact you’ve made without an eye on what matters to them?

Once you know the business goals, you’ll also have a good idea of important stakeholders to invite along. Establish strong partnerships with them. These will likely include internal business owners, IT experts, and outside business partners.

Understand Your Data

Get started conducting data audits and catalogs—but delegate this and don’t let it distract from your true purpose. (In our experience, some people think an inventory of assets is the entire job. Believe me, that is just the start.)

More than just finding data, you need to comprehend what it can do. Does it have the predictive power needed to meet the business goals ascertained above? You’ve also got to turn that question on its head. It’s tempting to start from the data, but the big question is: Does the enterprise have the data needed to accomplish its business objectives? If the business wants to improve the customer experience, are you collecting the data that tells you where customers are frustrated today?

Knowing where data gaps exist, by business objective, is a great starting point for your action plan. There’s a good chance your board wants to know, too.

Understand Your Organizational Capabilities

Even if you already know what data is available, is your organization capable of using it effectively, given your current level of data maturity? This is more than grading technology—it’s understanding your complete capabilities: people, process, and systems.

One of your tasks is to evaluate how data, information, and insight are put to use (or not). This is more difficult than it sounds, because the opinions you hear will be influenced by their holder’s place and experiences in the organization. We’ve all heard the story of the CEO who, when asked to describe how a process works on the manufacturing floor, gives an answer that rolls the eyes of the shop foreman. It’s vital that both voices are heard.

It is also vital to identify where talent is misdeployed. You might have the best and brightest PhDs on your machine learning team, but if they aren’t working on problems that impact the bottom line, then why are they there?

The capability to turn data into decisions is not something accomplished by a single department. It’s not the business side of the house that does it, and it’s not IT that does it. It’s both, along with a cast of supporting players that can include engineering, your data science team, business analysts, floor managers, and top executives. Having the right data available at the right time empowers potentially everyone in the company to make decisions quickly and confidently.

Lay the Groundwork for Investment

The final step in the first 100 days is commonly called putting your money where your mouth is. Saying, “We are becoming a data-driven company” but not asking for data and analysis to support key decisions is pointless and also demoralizing to those who have signed on to follow your lead. The CDO has to evangelize for a new culture that values actionable data as much as previous business models prioritized cash flow.

Assessing the Assessment

As you come to the end of the initial assessment phase, it’s time to apply what you’ve learned.

Start by translating your technical and business assessment into a formal action agenda. Without a battle plan, you’re wasting effort. Use business results as a way to focus on what’s important. Finally, think of your action plan as more of a roadmap, where occasional course corrections are inevitable to stay up to date on changes in the competitive environment, internal changes in strategy or tactics, and financial realities.

Here is what you should be able to show stakeholders at the end of the period:

  1. You have demonstrated how integral data is to achieving their business goals, and specifically where the opportunities lie.
  2. Your organization now has a survey of its critical information, where it resides, how it can be accessed, and by whom.
  3. You have delivered a clear illustration of how data moves through the organization, and have identified problems such as dead-ends, misdirections, and black holes.

The Next 100 Days

With specificity developed around these three areas and links drawn between them, you should have a compelling story for stakeholders and the executive suite to win support for the next steps, whatever they may be.

As your initial three-month run comes to an end, set goals for yourself and continue generating momentum. When you hit 100 days, pause, take a deep breath, reflect on your accomplishments, and visualize where the road leads next.

 

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Three Ways the C-Suite Can Embrace (Gulp) Failure

February 22nd, 2018 Comments off

Originally published on LinkedIn on June 2017

In today’s hero culture, failure can lead to demoralization, loss of status, or loss of job. Teams that fail too much lose resources and ultimately fall apart. Fear of failure may be especially acute among company leaders, those women and men who are paid handsomely to produce success stories. So, yes, failing hurts and has real consequences. No wonder so many of us fear failure and manage our projects with safety nets and guard rails that rein in risk.

But failure has a bad rap. Failure is not only inevitable, it’s something to be embraced. And in almost all cases, success springs from a series of failed experiments, design iterations, or chances taken that led to true innovation—the ultimate market conqueror. Thirty-nine versions preceded the spray lubricant megahit WD-40.

As leaders work toward building their own experimental enterprise, it’s important to have a perspective on success and failure. You can’t avoid failure, but you can learn to cope and make the most of it. In this post, we’ll look at three ways you can embrace failure:

  • Understand that failing is learning.
  • Design processes and organizational structures that learn by failing.
  • Gain control of failure anxiety.

In the early 2000s, the first three rocket launches made by Elon Musk’s SpaceX were spectacular fiascos, and left the company one more failure away from bankruptcy. Test four, of course, succeeded, thanks to the culture he built in his company of learning from mistakes and iterating forward to build a better product. SpaceX eventually got its first break: a $1.5 billion contract with NASA. Innovative leaders like Musk, Richard Branson, and Jeff Bezos probably have had many more failures than successes—but their wins changed the world, and they didn’t get there on the first try

Understand that failing is learning

It’s easy to comprehend why we avoid talking about failure. For example, we may fear seeming negative in a culture of “positive thinking.” In truth, though, planning for negative outcomes is just good planning. Even CEOs don’t control the world as individuals, and we can’t make everything go our way.

And sometimes, if you look closely enough, failure really isn’t. A technology client of ours, a global travel integrator, asked if we could predict costly resource-allocation failures from their business and systems data. Weeks spent on an analysis concluded that we couldn’t predict any failures. It turned out that the data we’d been given didn’t have enough breadth to cover all the required variables. Finding nothing is an uncomfortable situation for any consultant, but the insight was that the data wasn’t correlative with the predictions that the client wanted. We shouldn’t be afraid to say that to our client. And our client shouldn’t be afraid to say that to the boss. The true failure would be not deciding what to do next.

Design your process to learn by failing

It’s not enough to accept failure if it happens; you must build in the possibility at the start of a project. When preparing, pretend there are binary outcomes—a positive and a negative. You must be prepared for both. What must go perfectly for the whole project to come together? What are those things so critical that, if they go wrong, the project craters?

But it’s not just risk planning around individual projects that we need to be concerned about. Pivotal personnel and organizational components must be addressed to create a culture around learning from mistakes.

Who should be spearheading this methodology? In traditional project management, the project manager better be thinking about both sides of what you are trying to execute. I think the true issue is the leader—the sponsor, or the person that is responsible and accountable ultimately for the investment being made.

Roche CEO Severin Schwan told an interviewer that he holds celebrations for failed projects to underscore that risk taking is endorsed from the very top of the organization. He said, “I would argue, from a cultural point of view, it’s more important to praise the people for the nine times they fail, than for the one time they succeed.”

Another way to overcome that fear is to make failing the objective. In the true agile sense, you do want failure–you want to test fast and find out what doesn’t work so you can go down the positive path. (As my former colleague, Mike Bechtel, puts it, “Failure isn’t what you’re after. You’re after big, honking, 100x success.”) We’ve spoken extensively about working with agile teams.

If you are truly agile and everything goes without a hitch to, say, roll out a new user experience, then you probably haven’t really tested the boundaries of where else success might be found. Failure can propel you to grow faster by accomplishing what innovation consultant Mike Maddock terms “failing forward.”

There is a right way to fail forward; I think of what scientist Max Delbrück called the Principle of Limited Sloppiness. He meant that your research and development activities should be open to and encourage unexpected serendipitous possibilities that appear out of nowhere. But you shouldn’t be chasing rainbows to the point where the results can’t be reproduced.

This isn’t about dressing up failure—just saying, “Well, I learned a lot!,” and moving on. You must dig deep into exactly what you learned. Maybe you discover from a project that you are not especially good at building product or increasing sales, and you need to either strengthen those skills or team up with the right person next time. Rather than throwing up your hands and saying it was out of your control, you must use your perspective to empower yourself to do better in the future.

Great leaders shouldn’t penalize well-conceived risks; they should penalize not taking risks or making the same mistakes twice.

Gain control of failure anxiety

You’ve acknowledged that failure must be planned for, and you’ve changed your perspective so that you see failure as an opportunity to learn. That’s great, but moments of anxiety will still pop up. How can those doubts be mitigated?

As Dr. Guy Winch says, focus on what you can control. Specifically, “Identify aspects of the task or preparation that are in your control and focus on those. Brainstorm ways to reframe aspects of the task that seem out of your control such that you regain control of them.” Have a plan and iterate quickly—that’s what gives you more control. Planned iteration helps you knock out unknown parameters and move to success (or failure) with speed and certainty.

Also, try visualizing your obstacles, just as an Olympics luge driver visualizes shooting down the icy, twisty track at 90 mph. In that early planning phase, when you’re acknowledging what could go wrong, really sit with it. Think about what it will mean for the project, the conversations you’ll need to have, and the solutions you’ll need to develop in order to get down your own critical path.

What’s next?

Becoming comfortable with failure is not easy, not for you or for the organization you lead. It won’t happen overnight. Use the skills we’ve detailed here to fight the knee-jerk reaction of fear, so you can commit yourself and your team to leverage failure into ultimate success.

(For more on the experimental enterprise, and how to build your own, watch our video.)

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