How to handle detractors to digital transformation

Handling detractors
It takes a lot of effort to align an organization to a digital strategy and digital transformation program. They are disruptive programs that aim to drive growth through new products and customer experiences and not everyone in the front office is going to want a new direction. They also require changes to the underlying business processes by advancing customer service capabilities, automation, and data intelligence. If you're a manager overseeing a large operation that's about to shrink because of automation, you're unlikely going to be a happy participant.

There's a lot of good advice out there to handle detractors in change management programs. 

Start with the champions - "My advice is to forget about the Antagonists at first and to put the initial focus on your Champions. Because if you start where you already have a strong base of support, your Champions will spread that message throughout their vast networks, building the strong platform you need." - Mark Murphy, In Change Management, Start With Champions, Not Antagonists

Respond to conflict to drive transformation - "For collaboration to occur, there needs to be conflict. Great collaboration can get heated. To an outsider, it sometimes resembles hostility or anger, but when we look more closely it is neither. Without collaboration, our ability to create transformative change is limited." - Doug Moran, Don't Mistake Cooperation for Collaboration

Leverage agile practices to drive collaboration, supporters and incremental wins - "If you want big impact from data science and big data, then think of demonstrating wins incrementally. - Isaac Sacolick, Why Agile Data Science Practices Drive Big Data Impact

Communicate and celebrate small wins - "Meaningful organizational change often takes years, yet most people lose interest in an initiative after a few weeks or months. Be prepared for this, and bolster excitement and commitment by continuously rewarding the accomplishment of shorter-term goals." - Alexandra Levit, 10 Ways NOT to Do Change Management

Change management takes preparation and time - "We’ve heard the adage that for successful end user adoption and engagement of a solution, we can’t “communicate on Monday, train on Tuesday, and go live on Wednesday.” For an implementation to be successful, particularly one that requires stakeholder behavioral change, a key component is time, both collectively and individually, for the pending changes. Each person has to make the decision to change, and ultimately go along with (adopt) a new way of working independently, and this is not an overnight process." - David Chapman, The Grass is Greener

Slowly add participants to your transformation program - "The leading analytics-driven organizations not only have more employees involved in data and analytics, but they are also more informal about involving them, and worry less about segmenting teams. This more informal approach may be the key to success." - EY, Change management for analytics success

Stay focused and win change without them - "Be secure in the knowledge that you are doing something good. Sometimes there’s nothing you can do. You can’t win them over, you can’t avoid them, you can’t laugh with them. So you have to just ignore them, and keep telling yourself that when you do achieve your goal, that will be your reward for enduring this detractor." - Leo Babauta Best 8 Ways to Deal with Detractors
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What discipline should CIO master to drive transformation?

Several years ago, I was in a forum where a CIO I deeply respect admitted to me that the hardest thing for him to do on a daily, weekly, and monthly basis was manage his time. Time was his biggest issue? Not a demanding CEO? Not firefighting the latest operational issue? Not trying trying to get a team to align on priorities or technical strategy? We all have struggles managing our time and we've all found a mix of tools and practices to help us manage it. Why was this such a struggle?

So now years later, I think about this issue because it's not really a question about time management and more about leadership. It's a question about where are you going to invest the most leadership clout, emphasis when addressing an audience, and attention to detail because (a) it's important to drive transformation and (b) you have the skill and opportunity to advance the agenda.

How are you investing your time and leadership clout?

To help understand this, I developed this simple six question survey. If you're a CIO, CTO, CDO or report to one, I hope you'll consider investing five minutes and I guarantee you'll rethink your priorities as you answer these questions. Are you spending sufficient time with non-technical staff on change management activities? When you're investing time on technologies that enable digital transformation, are you spending more effort on emerging technologies like blockchain or artificial intelligence, or are you equally investing time on internal practices and technologies?

Here's a relative, non-scientific breakdown of my time. I tend to work in businesses that have significant customer facing application so no surprise that product development and data science program require significant attention. It may be surprising that I spend more time with the leadership team than the technology staff, but when you consider the effort required to organize, gain agreement, and set a course on digital transformation it may make more sense. Why is operations and security so small? It's because I tend to find lieutenants who are strong in these areas and can drive the agenda

It's easy for CIO to become a slave to the schedule of standard meetings, key people you need to meet, vendors that require attention and other activities that get scheduled. What I am suggesting is to work your calendar backwards especially on thirty to sixty day horizons. What key activities should you schedule to drive your transformational agenda?

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Can AI learn to code?

Can AI Code
AI is coming along with artistic impression. There is Paul and e-David that can draw, an AI that can write a Beatles inspired song, followed by an AI that wrote a Christmas song. There's been a lot of work on natural language processing, but natural language understanding remains elusive. It's hard to keep up with all of AI's creative works and how fast AI will go from pattern-based expressions to truly creative ones.

Coding, a significant AI challenge

Getting an AI to code is going to get a lot of attention. Microsoft Research and Cambridge University are experimenting with DeepCoder an AI that codes by reusing existing lines of code from other programs. It can solve challenges that require around five lines of code. It's a significant achievement, though DeepCoder's researchers admit that even basic programming challenges will take a lot more research and "Generating a really big piece of code in one shot is hard, and potentially unrealistic."

Still, for those of us who grew up coding or began our careers as software developers, this achievement makes us wonder how hard it will be to succeed at the next challenge and how fast will we see breakthroughs in this line of research. Is it a matter of breaking down coding challenges to the right set of expressions for AI to process, or is there a fundamental creativity in coding that will be difficult for AI to replicate? Having AI assemble code rather than program fresh lines of code seems like a good starting point knowing that it is often easier and faster for humans to reuse code rather than build new.

But the hardest challenge may be in deciding what coding problems to apply AI and the optimal way to present coding tools or skills to an AI engine. Trying to get AI to code even simple programming assignments is a challenge because coding is an expression. It's closer to natural language challenges, whereas successful AI has been in areas of pattern recognition (computer vision, basic forms of art and music) and decision making (playing games, self-driving cars).  

We'll have to wait and see!

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Five ways to align digital transformation with data-driven practices

Align Digital Transformation and Data Driven Organization
Let's examine a couple of ways you can align two transformation programs to achieve complementary and reinforcing results. Your digital transformation program has a number of initiatives that enable new markets, develop new products, and should be targeting an overall improvement in customer experiences. You may have a separate program aimed at enabling the data driven organization that enabled citizen data scientists and leverages big data technologies to drive leadership and managers to leverage data in their decision making.

To align these programs, you have to consider how to enable data driven decision making around the digital transformation program. Here are a few ways to do this: 

  1. Ensure digital transformation programs are grounded with key performance indicators (KPIs) that are early indicators of whether programs are achieving desired results. In addition to any financial metrics, consider KPIs in customer experience, employee engagement, and operational efficiencies that can be early indicators of financial success.

  2. Develop a data gathering and sharing strategy especially for new products and applications. I want to see metrics on marketing activities, product usage, and system performance to help guide decisions on priorities and roadmaps.

  3. Leverage data to update customer segments and user personas so that the digital strategy can evolve as new capabilities are deployed. Strategic activities should not be considered one-time events and should be updated with new data and insights.

  4. Engage a growing number of employees with data and insights when embarking on new initiatives. Transformation programs often begin with a short list of initiatives and participants, that should evolve over time to include a larger scope and participation. When bringing on new participants, align them early to both digital and data-driven thinking by leveraging insights captured from successful initiatives.

  5. Review enterprise systems, especially the CRM and marketing automation tools on new data sources and workflow changes that enable a digital business.    

Don't leave these as afterthoughts. Be explicit when working with initiative and team leaders to ensure these data-driven practices are incorporated in their programs.

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500+ Conferences for Technology, Digital, and Data Leaders

I've been slowly growing the number of events in my dashboard. I started with about 200 and now the list has grown to over 500 conferences for technologists.

Insights on Conferences for CIO, CTO, and CDO

  • Top technology topics are Big Data (42 conferences) IoT (19) and Artificial Intelligence (14)

  • 50%+ of the conferences are directly target CIO and CISO.

  • 30%+ of the conferences are in the top three locations - New York (65), San Francisco (59), and Chicago (41).

  • 50%+ of the conferences occur between March and June. Top months are June (98), May(73), March (69) and April (54).

  • About 10% of the conferences are industry specific with Finance and Healthcare the top two industries

Click here to use the dashboard to find conferences that interest you.

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5 Reasons to Estimate Agile Development with Story Points

I have a few posts on agile estimation. Here's one on why estimating is important, another one on how to do one-week agile planning sprint, and a more detailed one on how to conduct an agile sizing meeting. Does agile poker help? Yes. If that's how the team wants to self organize and come to consensus on an estimate.

I am often confronted with the question on whether to estimate in hours or in story points. The debate over whether to estimate at all and what measure to use is extensive, so I'm going to share some points why I always endorse software and application development teams to use story points to estimate.

Why using Story Points Drives More Reliable Estimates and Consistent Velocity

  1. Estimating with points is easier for developers and aligns with how they interpret requirements and develop solutions. When reading the story and acceptance criteria, developers typically ask themselves a few questions. Do I understand the requirement? Do I believe it's important and understand why it was prioritized? Most importantly, do I have an idea of how to implement it and is the solution similar to other things that I have already implemented? Developers will assign higher story points for vague requirements or for unknown implementations accounting for both the effort required to solution and the potential complexities in the implementation.

  2. Estimating high story points drives more questions and dialogue. Since story points expresses both effort and complexity, a higher estimate will often draw questions and dialogue on how to simplify the requirement or the implementation. Ask the developer why the estimate is high. If it was estimated in hours, you're more likely to get a list of implementation and refactoring steps that is hard for product owners and technical leaders without a deep understanding of the application architecture and code to interpret. If the estimate is in story points, more questions can be asked as to whether the developer is interpreting the requirement correctly, what makes the implementation complex, and whether there are alternative solutions that are easier to implement.

  3. It's easier to normalize story points across developers of different skills.  An advanced developer may estimate a story is only a couple of hours to implement while an inexperienced one is more likely to estimate greater effort to account for the learning curve and making mistakes. Now let's say teams are using the Fibonacci series for standardizing sizes and craft some definition of what three vs. five vs thirteen story points mean. Maybe a story size of three means that the implementation requires a single change to the user interface without any changes to the business logic or data model. When you define it that way, you're more likely to get both the advanced and inexperienced developer to estimate the same or similar number of points for the story.

    So what accounts for the inexperienced developer's added effort to complete this story? You'll see it in the team's commitment and the stories assigned to the novice developer versus the advanced one. The novice developer is more likely to commit to fewer stories (and fewer total points) than the advanced developer. As the novice developer gains more experience and knowledge of the application architecture, you're likely to see a higher commitment.

    Here is a great post by Mike Cohn that elaborates on why estimating with story points helps teams with different skill and experience levels. 

  4. For those looking to capture development costs, measuring actual hours provides an easier to implement and more accurate solution. Most agile tools allow developers to log their work in hours so if required, at the end of a sprint you can get a full cost accounting. See time tracking in JiraRally and VersionOne. What's more interesting is that you'll have better data correlating estimated story points to actual hours and asking questions on the variance. For example, a high point story with low effort implies a complex story or an overestimated one. 

  5. Estimating and committing to story points more often leads to a consistent velocity. Development teams will not only consider the total points of prioritized stories but the mix of them. So for example, they may commit to three 5-point stories and one 13-point story for a total of 28 points but may not commit to six 3-point stories and two 5-point ones even though they add up to the same 28 points. When committing, the developers take many other variables into context beyond size, complexity, and effort hours and are more likely to commit to a blending of story sizes that fit the skills and expertise of the team. The added context in the decision making often leads to a more consistent velocity.
Want to read more? Here are some more points on using story points. Also, remember that estimation is hard and what leaders should focus on is on getting the culture, practice, and requirements right that enables teams to deliver well designed and performing applications. 

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2017 Events for CIO, CTO, Chief Digital Officers, and Chief Data Officers

I was doing some research last week on 2017 events for CIO, CTO, Chief Digital Officers, Chief Data Officers, and IT leaders. I found articles on and TechTarget with a number of events listed and then went looking at other institutions (Gartner, Forrester, CDM Media, Evanta, Argyle, HMG Strategy, O'Reilly and others) for their lists.

I focused on events in the United States (MVP) and aggregated almost 250 events targeting CIO and other data, digital, and technology leaders.

It took some time to put together what I think is a reasonable, but not comprehensive list. I then used Trifacta to merge and cleanse the data, added a dimension on "topics" and developed a Tableau dashboard to review.

Here is the CIO 2017 Events Dashboard. To use the dashboard, click on the map for your location and use the bar charts to drill down by topic, timing, and sponsor. In the grid, click on the Event Name to get to the event's website.

2017 CIO Events
Click the image to see 250+ 2017 events for CIO, CTO, Chief Digital Officers and Chief Data Officers

Insights into Event Topics

  • Chief Digital Officers should look at events under Digital Transformation, Digital Marketing, Customer Experience and Innovation.
  • Chief Data Officers should look at events under Big Data which also includes events on analytics, data science, and data management.
  • Emerging topics include blockchain, AI, IoT, Wearables, and innovation.
  • DevOps includes cloud conferences. All other IT operations such as data centers and service desk are covered under Operations. Security is a separate topic.
  • Events from leading technology vendors are under the topic Technology.
  • There are separate topics for enterprise architecture, software development, and mobile.

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