Challenging Sacred Cows that Inhibit Successful Transformation

When newspapers saw their classified revenue erode when new competitors like EBay, Craigslist, and Monster.com challenged them.

When the music industry was first confronted by Napster music downloads and tried to hold on to their CD revenue streams.

 When Garmin realized that revenue from their auto GPS devices would never return as mapping apps on smartphones became mainstream.

These are huge disruptions that not only affected individual businesses that relied on these revenues, it transformed entire industries.

What are Your Organization's Sacred Cows?


Disruptions like the ones I just described happen fast, but they are not overnight transformations. Having been a part of several transformations, I can tell you the battle is not fought yearly or quarterly. It's a daily battle on the organization's values, product's fundamentals, and the "way we do things." Here are some examples

  • How much editorial control should magazine editors have of their home page versus allowing an algorithm to decide what content to prioritize?
  • Should you spend two months of redeveloping an advanced search capability that less than 5% of your customers utilize, or should you try  discontinuing or simplifying the capability?
  • Should you keep that legacy system running three more months because several VIPs have refused to migrate according to schedule?
  • Does every article require a custom design and layout, or can you target 30% of the articles to fit into one of 2-3 standard templates?
  • In your BI dashboard, should you try to make every dimension searchable and sortable to support different user needs even if the added complexity adds development time, testing complexity, or performance degradation?

I've seen these examples and many others. Often the time spent debating or arguing the importance of preserving something from the past outweighs any of potential benefits?

Why the Sacred Cows Inhibit Transformation


A sacred cow is "an individual, organization, institution, etc., considered to be exempt from criticism or questioning." By definition, when we are trying to transform we have to be able to question everything, challenge assumptions, bring in new data and facts, and develop new innovative thinking. If individuals hold on to the past or inhibit the discussion on what really is important for the future versus what worked in the past, then transformation may not be possible. You may transition and improve, but a transformation requires determining whether something implemented in the past is still required, in what form and with what priority.

This discussion, if and when it happens, is likely to get heated. Doug Moran states it well in his post Don't Mistake Cooperation for Collaboration

In reality, cooperation can be one of the greatest obstacles to collaboration. 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. 

Should You Burn the Ships?


In most scenarios, this may not be advisable and forcing changes on people may not yield the desired results. But sometimes it is a necessary tool. Change agents have to learn when to use it and how to apply to truly get team members motivated.


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Getting the C-Suite on Board with Agile Transformation

HBR is proving they understand Digital Innovation with two recent posts, one on Embracing Agile co-written by Jeff Sutherland one of the Agile Manifesto's signers and the other, You Don't Have to Be a Software Company to Think Like One.

Anyone who has led an agile transformation in their organization knows that getting people, teams, departments, and organizations on board takes time and energy. It often starts with the agile leader's own group, often the technology department if the transformation is led by the CTO or CIO, but sometimes by the product management team that has been chartered to innovate new products. Agile leaders start with practice issues to get their team executing but then often face larger cultural issues ones when they extend the practice beyond their own teams.

Bringing Agile Principles to the C-Suite


The C-Suite is often the last on board with agile. Some may be hands off with underlying business processes and elect to observe from the stadium seats while others compartmentalize agile leaving their own organization walled off from its changes or impacts. Confronted with these challenges, here's one reason agile leaders can use to explain why agile is key to transformation and growth

Recognize [there is] a fundamental shift in the sources of value creation and competitive advantage toward software. Companies face major risks if they fail to recognize this new platform-driven context and the different economic rules that govern it.

Your business may not drive revenue from creating or selling software, but all businesses can develop strategic advantages by how it leverages software to reach customers, data to drive decision making, and algorithms to deliver new value or efficiencies. Your leaders need to understand the digital transformation urgency and also what happens to businesses that lag behind digital competitors.
 
But here is one underlying problem

When we ask executives what they know about agile, the response is usually an uneasy smile and a quip such as “Just enough to be dangerous.”

How can this be? Is this good enough? Executives read P/Ls, sales plans and operational reports, but they can get away with only rudimentary knowledge of underlying processes that drive digital transformation, product innovation, process improvement? What's worse is when they do get involved, they fall back to command and control tactics

These executives launch countless initiatives with urgent deadlines rather than assign the highest priority to two or three. They talk more than listen. They promote marginal ideas that a team has previously considered and back-burnered. They routinely overturn team decisions and add review layers and controls to ensure that mistakes aren’t repeated. With the best of intentions, they erode the benefits that agile innovation can deliver.

Develop a C-Suite Agile Program


The two articles go on to give sound advice about why businesses need technology innovation to be more competitive and how leaders can be supporters of agile practices. My top three -

  • Codify proprietary know how and use digital platforms to scale and monetize your offerings.
  • Learn How Agile Really Works
  • Destroy the Barriers to Agile Behaviors

And then remind your executives -

Innovation is what agile is all about.  Companies that create an environment in which agile flourishes find that teams can churn out innovations faster

Further Reading on Agile and Digital Transformation


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Is Leadership Ready to Handle a Shark Attack?

CIO and IT leaders should be able to relate to this video. Watch the first few minutes to see Mick Fanning waiting to catch a wave when suddenly he is attacked by a shark. You can see him scramble a bit then fend him off before a boat comes to his rescue.


Now for those of us in IT, there are some great lessons to share with your business leaders on handling a real life crisis from this video.





  • The announcer shows no real sign of panic. Yes, he utters a cuss word when he realizes what's going on but he regains his composure and shows no panic or stress in his reporting despite the fact this has rarely (if ever) happened before.

  • There are clear operational procedures for this type of crisis and within minutes, you can see two boats zooming in to help with the rescue.

  • There are also safety and communication procedures outlined on what to do and the commissioner of the event is bound to follow them. 

  • The shark gets away which unfortunately is more often to happen in the corporate setting when a business leader goes on the attack.

Unfortunately, I think many CIO can reflect on this situation. Does your leadership team panic at the first sign of a major issue or crisis? Are you well prepared to handle the crisis with clearly defined operational procedures? Are you ready to handle communications during and after the crisis? If the issue is internal, do you have a culture that deals with individuals that are the source of the crisis?

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10 Practices of Strong Agile Product Owners

I've worked with some excellent agile product owners that have developed new products, delighted customers, grown revenue, and collaborated well with their agile teams. I've also worked with other product owners that have some very bad behaviors reflecting in poor under performing products and angry team members.

So I thought to share some of the behaviors and practices that make product owners successful. They fall in three categories; understanding customer value, practicing agile, and leading data driven practices.



Customer Value + Agile + Data Driven

  1. They develop a holistic view of market segments, customer needs, and value proposition - Sounds like a 101, but many product owners dive right into developing solutions without developing segments, personas, and values as a guide.

  2. They are great listeners and collaborators - The best product owners know they are sitting in the center of a virtual circle between sales, marketing, technology and other stakeholders that have different opinions and skills. The best product owners listen first and collaborate with the team on priorities and solutions.

  3. Leverage agile strategically to shape their product to market need -  They capture customer feedback and use it to reshape their vision, requirements and priorities. They pivot when required and experiment to see what's working. 

  4. Sell their vision, detail their stories - Great product owners have to be excellent communicators to get larger teams to understand and follow their vision. They also have to be strong practitioners, best demonstrated by making sure the active agile stories are precise on what is required.

  5. Leverage a network of key customers and prospects - Great product owners develop these networks to get insight into the industry, test ideas, pilot new capabilities, or capture critical performance feedback.

  6. Partner with technologists on platforms, standards, and prototypes - Every organization develops standards to be efficient and leverage skills and investments. The best product owners learn to leverage these to their advantage by reapplying existing capabilities to accelerate speed to market rather than developing new solutions from the ground up. And when new capabilities are needed, they partner with technologists to develop prototypes to validate and gain consensus on approach.

  7. Review financial performance and contracts - Great product owners understand the fundamental financial performance of their products, profitability of key customers, health of the sales pipeline and other performance metrics. They also review customer contracts in order to make sure their requirements can be met. 

  8. Develop KPIs and use them to drive priorities - Product Owners need to insure their teams are data driven in their decision making. Their role is to define key KPIs on the products performance in areas such as financial, customer satisfaction, risk and other criteria to help drive priorities.

  9. Develop brands, platforms, and ecosystems - Winning at digital business requires product owners to recognize that products do not survive in silos. They need to consider how their offering might evolve to become a platform or interface into appropriate ecosystems. They must also partner with marketing to build brand, attract prospects, and develop relationships. 

  10. They balance priorities to short and long term needs - Product owners have the tough job of digesting all the issues, wants, and needs demanded by top customers and sales people, the technical debt and other system priorities escalated by their technologists, branding and messaging ideas escalated by their marketing professionals into a manageable prioritized list. Then comes the harder part of determining how to best balance these needs against the strategic vision and long term success drivers. 

And here's an extra one - one that many product owners under the stress of market conditions, difficult customers, challenging stakeholders, engineering realities often forget -

  1. They celebrate small wins and thank the team - Things go wrong all the time and the team is never truly 'done' with everything that customers need and product owners want to deliver. Great product owners know how and when to thank the team and individuals on accomplishments both big and small.

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Where are you Implementing your Big Data Algorithms?

It sounds like a simple question. You have to load several data sets, implement some data cleansing, perform some matching to third party data, compute several aggregates, develop some rankings, group several dimensions, benchmark against another data set, analyze for trends and then normalize the data for multiple data visualizations.

In all likelihood, the algorithms that perform these functions are going to be implemented by different people in different technologies and perhaps at different stages in the analysis. End to end, they represent a complex data flow from data sources, computations, analysis, and delivery.

Key Data Architecture Considerations


So my question is, where are you implementing these data processing functions? Where are your algorithms stored? How are they documented? How do you answer questions around, "Where should I do this data processing?" What is your big data culture - Are you more likely to let data scientists determine what tool to use for different needs, or are you centralizing these data architecture decisions?

Once implemented, how do you review to determine what parts of your data processing needs to be refactored? Maybe a step isn't performing well? Maybe a data visualization required some last mile data cleansing that should be moved upstream to benefit other analysis? Perhaps some algorithm fails to meet the "KT" (Knowledge Transfer) test and is so complex it will be impossible to be maintained?

Or maybe, you've implemented something in a Big Data tool that has just released a major upgrade requiring substantial changes to the implementation? Or even worse, perhaps the tool you selected is on the downside, having never achieved critical mass and now you have to explore alternatives and consider switching costs.

The reverse question is equally important. Perhaps you're bundling some activity in the wrong tool and should consider expanding your technical architecture? Perhaps you are spending too many cycles getting SQL to perform and should consider a NoSQL store? Maybe the Python scripts you developed for data integration are becoming unmanageable and an ETL tool is needed?

Managing the Evolving Big Data Landscape and Growing Business Need


So the business need is growing, the technology landscape is changing, quickly, access to talent is volatile, and both standards and best practices are evolving. What does this mean for Big Data specialists and Digital Transformation leaders who need to prove results today but manage to an evolving practice?

My simple answer is to rely on the basic practices that have made application development practices evolve through significant changes in demand, technologies, and development practices. Some specifics -

  • Invest in basic version control so that you can track changed implementations  across platforms and practices.

  • Evolve a data governance practice that starts with basic data dictionaries and documentation on algorithms.

  • Build an agile data practice to make sure participants focus on the problems of highest business value and demo their results

  • Develop operational KPIs covering development cost, implementation complexity and system performance to sense when an implementation shows signs of becoming a pain point.

  • Capture technical debt data quality barriers and other things that need improvement.

And most important:

  • Invest time/resources to perform R&D and experiment.


Thanks to Matt Turck: Is Big Data Still a Thing
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How to Kick Off a Citizen Data Science Program

Big Data Scientists
Citizen data scientists is catching on. Perhaps your organization can't hire or retain fully skilled and trained data scientists because of the skill shortage and the increasing salaries top talent command. Or perhaps you believe in the democratizing of data and that business analysts that were once influenced to become spreadsheet jockeys just need to be retooled with new data visualization skills and data governance principles.

Either are strong rationale to rethink how to transform the people, practices and technologies around your internal enterprise data and to take steps to drive a data driven culture.

Getting Started with Citizen Data Science Programs


So here are some tips on how to getting a citizen data science program started:

  1. Find a handful of underserved business units or operational groups that desire to be more data driven but are lacking the tools or practices, This can easily be the marketing department that needs to be more data driven to define segments and process leads. It could be the sales department that needs better reporting to drive sales management practices or the finance department that are under pressure to slice/dice P/Ls in new ways at greater frequency. 

  2. Cultivate a relationship with these business leaders to insure they are ready to take on a transformational challenge. If they are not ready to participate and sponsor this initiative it will fall short when you need to engage their resources to become citizen data scientists or you need their sponsorship to promote organizational change on leveraging any new data tools. 

  3. Find the data super users that are currently doing data work manually. These may be users that are skilled at asking good data questions, are very hands on with spreadsheets, or elect to use database tools that drive data silos. They may also be versatile running analytics in specialty tools like CRM, web analytics, or even ERP. These users have skills, but need technical direction on which tools the organization wants to support long term. They also need defined data governance practices to help avoid a new generation of data landfills.

  4. Define standards that can be developed incrementally but insure a scalable set of organizational practices. What data visualization tools will be used? What tools will be used for modeling? How do you separate what's in "development" vs. testing vs. ready to be used in decision making? Where are data dictionaries maintained? How is data access established? These are some of the new data management practices IT needs to help support and that citizen data science teams need to practice.  

  5. Define an agile data practice that provides citizen data scientists a prioritized list of problems to solve, a delivery practice based on agile principles, and assigns roles/responsibilities in developing solutions. By way of example, here is a practice defined to help find nuggets in an organization's dark data using an agile data mining process. The idea is to make sure there is a disciplined cadence that defines priorities, drives citizen data sciences to commit to completing an analysis in a fixed duration, and insures results are presented before moving onto new challenges.

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Stop! Don't Let Your Business Fall off the Digital Disruption Cliff

Have you ever seen the industry you're working in walk off a digital cliff? I have, and let me tell you it isn't pretty and it happens fast.

Once an industry begins transforming to a digital business with digital only competitors, incumbents have to move early, quickly and intelligently through a digital transformation. These businesses have to bring in new talent, invest in digital capabilities, and leave sacred cows behind in order to compete with digital disruptors.

Newspaper Ad Revenue 1950-2012

The figure shown here is what happened and continues to happen to the newspaper industry where the digital transformation started as soon as the internet became mainstream in the 1990s. The startup I joined and later became CTO of partnered with newspapers to help them go from "print to web" as the transformation was called back then in media. We started with a classified ad platform with natural language processing, a search engine, and a pre-CSS publishing engine that enabled us to aggregate classified ads from ~1600 newspapers and deliver a SaaS (back then it was called an ASP) product that enabled searching classified ads on the newspaper website. Over the next decade we built, merged and acquired capabilities to help run all the digital capabilities newspapers required including publishing content, hosting job boards, integrating with auto dealer web sites, and running a digital newspaper ad network. By 2002 we had eleven newspaper companies investing and using our platforms.

The Impact of Digital Disruption


But most of you know the other side of the story. Craigslist, ebay, cars.com, monster.com, hot jobs, realtor.com, yahoo, aol and countless others that saw the paper driven, inefficient, local newspaper as a slow fat target. Newspaper revenue grew in 1996-2001 during the bubble but fell off a sharp cliff when it burst? Why? Simply because consumers and advertisers had lower cost and higher value options and the branding, localization, and trust newspapers heralded wasn't sufficient to retain loyal customers.

Once digital alternatives appear in market, things go downhill fast for incumbents. While digital players learned how to compete digitally and improve user experiences, newspapers had to adjust their operating models and philosophies. Hours invested by newspapers to preserve editorial excellence, transform manual processes, or consider print/digital subscription models came in lieu of the hours invested by digital companies to improve customer experiences, steal market share, develop analytics, or automate processes aimed at growing revenue profitably. That transformational gap, along with drastically different digital pricing creates the steep fall off in revenue.

But that was Media, It Can't Happen In...

If you're naive to think this is a media phenomena then think again. I shared a number of other examples in my post, What is Digital Business and Digital Transformation. HBR recently reviewed results of a Russell Reynolds Associates survey of industries where executives anticipate moderate or massive digital disruption and while Media was number one on the list, it is quickly followed by telecom, consumer financial services, retail, technology, insurance, and consumer products. Basically, any product or service that can have differentiating capabilities via digital transactions, digitally enriched products (content, data, and IoT), multimode or omnichannel customer experiences, algorithmic or AI driven automation are likely to experience some form of disruption.

I'll let the experts predict which industries will be disrupted, whether they will be moderate or massive, when will they kick in and how fast they will transform. I will continue to share what to do about it.

Further reading on Digital Transformation



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