3 Signs You Have Overloaded Your Digital Transformation Program


Pressure to take on too many initiatives may bottleneck the overall program and burnout participants




I am an advocate for speed and doing more. I’ve seen products, businesses, and an entire business models fall off the cliff because executives were too slow to respond, experiment, invest, challenge sacred cows, and scale toward new digital business models. So, when presented with the opportunity to lead transformation programs, I would rather say yes to new initiatives especially when they deliver improved customer experiences, grow revenue, improve analytics, or are likely to replace an existing legacy revenue stream.

I often get asked, “How much is too much” and its corollary question, “Can the team take on one more important initiative?” The core of these questions is whether you are pushing leaders and the team too hard and overloading them with too many initiatives.

I have a method that helps address these questions outlined in my book, Driving Digital: The Leader’s Guide to Business Transformation Through Technology. It involves understanding how to effectively plan projects, what type of team structure enables you to scale and add people when required, and how to manage the impact to end users (both customers and employees) as the initiative delivers changes. It would require too much detail to share in a blog post.

But what I can provide are some of the symptoms and indicators of when an organization, a team, or a person is overloaded. My focus is at an organizational level, but since people have different productivity levels and abilities to handle stress you also should consider the impact on individuals. For this, review other articles to help individuals such as signs that employees are suffering from stress and helping a coworker that is stressed out.

Signs of Overload


So here are signs that you can observe and even measure at the organizational level indicating that the team may be overloaded  

1. Lots of great ideas, few are being implemented 

This can happen for a few different reasons especially if you don’t have a defined process to plan initiatives or if your innovators are on the hook to plan too many of them. This is a problem that’s easy to measure and harder to solve. Start by looking at who is leading or has a significant role in each of your initiatives and you are likely to find several innovators assigned to too many programs. Everyone has their limits on how much multitasking they can handle and how much they can get pulled in different directions. Even with some simple guidelines, your initiative to resource mapping should give you clues where you have taxed key people.

You will also find initiatives that are under resourced and making progress at a glacial pace. You can use this resource and initiative mapping and if an initiative is under resourced at the leadership level then it's less likely to make consistent progress.

2. You are spending more time communicating than getting things done 

The more independent things you are trying to plan and execute on, the more communications are required. If you feel like key members of the team are spending a lot of time and energy getting on the same page, aligning on priorities, achieving consensus on solutions, or reporting on status then it’s likely that they are overloaded. Collaboration tools can improve the productivity around communications, but if innovators are context switching too much then it's difficult for them to get the things they committed to done.

3. Users are slow to adopt, and many are not happy 

Changing workflows, behaviors, mindset, and culture often happens over longer timescales than the time it takes to introduce new capabilities. If you're introducing new capabilities to multiple organizations such as sales, marketing, and operations then you must schedule them around their peak periods of activity and gauge how much change they can manage. Transformation programs often require leaders to push organizations to learn and adopt new practices quickly, but pushing too hard can easily overwhelm a group.

How you handle this depends on many factors. Most important is too identify key people in these organizations from leaders down to entry level employees that are "early adopters" of new capabilities, are in tune with their organization's pulse, and are prepared to give you honest feedback. Since they are early adopters, they are likely to give you practical feedback on the capability you are introducing. If they are equally in tune with their organization, then they will balance this out with insights on the success and roadblocks getting mainstream adoption across the organization.

Leaders Must Drive the Urgency

If you're trying to move your organization smarter and faster, then employees must hear this message repetitively. More specifically, they must understand why the urgency and how going too slow impacts customers, employees, and them.



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5 Steps to Prepare Your Organization for Artificial Intelligence

Last week I delivered a keynote talk at Synechron's InSync Charlotte event around developing the data driven organization. The talk showed the audience the alignment between digital transformation and data driven practices and how evolving data practices should address small data (through citizen data science programs) and big data governance that can enable organizations to be organizationally ready to experiment with machine learning and artificial intelligence. Later that evening, I shared the stage with Imam Hoque, COO of Quantexa and Sandeep Kumar, Head of Capital Market Solutions at Synechron where we answered questions about AI in the organization.

McKinsey Global Institute -
Artificial Intelligence
The Next Digital Frontier
Let me share a punchline from a recent WSJ article, Artificial Intelligence Is Ready for Business, Are Businesses Ready for AI?
The report is based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors. Only 20% of respondents had adopted AI at scale in a core part of their business. 40% were partial adopters or experimenters, while another 40 percent were essentially contemplators.
So unless you work for a tech company, it's very likely that your AI journey is just beginning if it has begun at all. The main barriers fall into three categories (i) lack of talent, (ii) data isn't ready for AI, and (iii) unclear to business leaders what problems and opportunities where AI can drive value.

Preparing for AI 


So if you are an experimenter or a contemplator, what are some steps your organization should consider to be ready to leverage AI? Here are my five - 
  1. Define data governance - This may seem like an odd place to start, but without a data governance policy stating who has access to data and on permissible uses it can lead to organization dysfunction. Some will interpret the lack of a data policy to imply that they can do anything they want with the available data, others will be paralyzed and may prevent basic data from being used in analysis. Data governance teams should also be developing data catalogs, dictionaries, and reviews of data quality that are all important for using data in AI experiments. 

  2. Establish your data lake - AI and ML algorithms work best when lots of raw data is applied to them. Centralizing primary data sources (including external ones) makes it easier for AI developers and data scientists to tap in and leverage them. In Chapter 3 of my recently published book, Driving Digital: The Leader's Guide to Digital Transformation Through Technology I cover the data technologies many organizations use to develop nosql data stores and other data lakes.

  3. Find industry partners - AI talent is still scarce, and leveraging AI to develop insights and applications may be a long journey for many organizations that cannot attract sufficient talent. In addition, AI applications can be developed to leverage industry knowledge and plug in common data sources. For these reasons, many organizations will benefit by partnering with AI experts in their industry rather than building on their own.

  4. Identify examples out of your industry - Many business leaders are still learning about what types of problems can be solved with AI. While you might find some examples in your own industry, you'll get a better sense of the opportunities by looking at successes in other industries that are strong in the AI areas of interest. Looking for AI in customer experience then maybe review retail experiences. Have a lot of unstructured content, then look at news media for examples of natural language processing and natural language generation. Want to use AI to identify fraud then look for Banking and Fintech examples. 

  5. Backlog POCs that drive value - Once you have your data policies in order, a starting data lake, an understanding of where industry partners can help you accelerate, and some basic learning in your organization about where AI is successful then you're ready to explore options in your organizations. Start by asking big, bold questions, consider the value in the result rather than the path to get there and then plan to experiment

These steps shouldn't be done in sequence. Data governance is likely to be an ongoing program while building the data lake, identifying partners, and seeking examples should be parallel activities. Once milestones are achieved in these areas, then you can consider brainstorming POCs.

What are your AI plans for 2018?
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How to Implement Agile on Lowcode and aPaaS Development Platforms

A colleague and friend recently asked me whether agile practices were appropriate when managing development on lowcode or aPaaS (application Platform as a Service) technologies. These higher level languages enable developers to write a class of applications faster and maintain them easier by providing development environments, programming constructs, and other tools. Many of these platforms are designed to rapidly develop mobile applications or applications that connect to disparate data sources. They differ from citizens development platforms and citizen data science platforms which target business end users and enable these user to develop applications, dashboards, and other tools without having to use coding constructs.

How LowCode and aPass Development Differs from Software Development


I reference lowcode development platforms in my book, Driving Digital: The Leader's Guide to Business Transformation Through Technology as a means for enterprises to develop the tools to empower their workforce and to provide capabilities to tailored customer segments. I've used them to develop applications to track the IT budgets, manage a large portfolio of projects, and to establish workflows on hiring and recruiting.

So here are some of the differences between traditional software development and lowcode/aPass development that impact agile practices


PracticeSoftware DevelopmentaPass / LowcodeAgile Impact
RequirementsRequires upfront design and UX with detailed accepted criteria on functionalityDesign, UX, and functionality often bounded by the capabilities of the platformProduct owners must know the capabilities of the lowcode platform to stay in its scope of capabilities, but can often draft more simplified requirements and acceptance criteria
DevelopmentCan scale from small to very large teams. Leverage standards for coding, commenting, and documentationOften designed for individuals or smaller teams. Coding and documentation capabilities differ by platformEnables rapid development around functionality, but documentation may require additional effort outside of the lowcode development platform
TestingDevelopment practice should factor automating unit, functionality, performance and security testing. UAT should focus on user experience UAT focus on functionality and workflow but specialized testing is often required to validate algorithmsMore opportunity to bring business users into the development process to validate implementation while developing
DevOpsDev and Ops teams need to select and implement tools/standards to for continuous integration and deliveryDeployment steps are often factored into the lowcode platform, but platforms may not have the capability to version or rollback a changeLowcode platforms without roll back and versioning capabilities have higher risks when introducing large functionality or workflow changes

Recommendations on Implementing Agile in Lowcode


Based on the factors identified above, here are my recommendations for leveraging agile in lowcode and aPaaS development environments

  • Make sure the application need is appropriate to the platform because trying to use lowcode platforms as a Swiss army knife to every development need can result in applications with poor user experiences and coding complexities.

  • Leverage shorter sprints to take advantage of the rapid development capabilities and fewer testing needs of the platform. For many platforms one-week sprints are possible.

  • Focus story writing on workflow and user experience since the technical implementation is bounded by the platform's capabilities.

  • Challenge requirements that drive customization and software development outside the capabilities of the platform since this drives complexity in supporting the application.

  • Add UAT members to the team so that business user testing can occur while development is in progress and ensure "shippable code" at the end of the sprint.

  • Optimize for shorter release cycles of 1-2 sprints especially if the platform doesn't have versioning or rollback capabilities.

  • Require technical documentation with every release to avoid accumulating technical debt and making it difficult to introduce new developers to support applications.

  • Factor in time to perform and test upgrades so that you don't fall behind in the supported versions of the platform.

One last word. There's significant diversity of lowcode platforms and their technical capabilities, so my recommendations are general guidelines.  


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Driving Digital Smarter and Faster - Because Everybody is a Technology Company

Driving Digital
I hope you will listen to my recently recorded podcast at AMA Edgewise on my book, Driving Digital: The Leader's Guide to Business Transformation Through Technology.

Link is below and here are some of my favorite quotes -
  • New entrants (startups) + consumer choice + availability of technology + falling price point => is driving digital transformation

  • CIOs "get it" - but many organizations (IT, Marketing, Sales, etc) are still learning digital practices.

  • Drop rank at the door. What will this company be 5-10 years from now without factoring in legacy and how you do things today?

  • You need to experiment. I don't call it fail fast. I call it learn quickly and adapt.

  • Understand your culture and capability. Are you an early mover, or are you better by learning from examples and finding niche opportunities to grow from?

  • Is there really something different between digital transformation and previous decades of transformation? What's really different is the aggregate of capabilities and that customers will switch brands and products easily.


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Driving Digital in the News!

Isaac Sacolick Driving Digital
Long time readers of Social, Agile, and Transformation know that I've recommended going on vacations to help clear the mind and having fun as a key tenet of your organization's culture. So before I sign off for some end-of-summer time off, I'd like to update you on the latest news around my book, Driving Digital: The Leader's Guide to Business Transformation Through Technology.

First, Driving Digital is now available at select bookstores and on Amazon in print, ebook, and audio formats. I'm pictured here with Driving Digital on the shelves at the Barnes and Noble at NYC's Union Square.

Second, there's been a number of articles written that reference Driving Digital -

"Driving Digital: The Leader’s Guide to Business Transformation through Technology by Isaac Sacolick is a timely, engaging, and practical roadmap to developing and implementing digital strategies. It shows how to make the culture of the organization more digital-friendly, generate growth through new digital channels, create digitally immersive and rewarding experiences for customers, develop new competitive advantages, and drive new operational efficiencies."- Gil Press
He goes on to say
"Sacolick’s book is a must-read for the business and technology executives managing the digital transformation of their organizations, whether they are CEOs, Chief Digital Officers, Chief Information Officers, Chief Technology Officers, Chief Marketing Officers or any manager, at any level, seeking guidance as to which digital tools and practices to deploy and how."- Gil Press

"Digital transformation impacts the entire organization. Technology and marketing teams need to operate as a collaborative digital organization driving leads to new products. Sales teams have to develop broader relationships and be well versed in how technologies embedded in products and services operate. Financial teams have to be more data driven to provide more frequent insights on how the business is functioning. 
We’re moving to a smarter and faster world. Those that are too slow transforming will be left way behind."- Isaac Sacolick
"When there is a shared understanding and a better alignment on priorities and process between the development and operations team, a more customer-centric devops culture emerges."- Isaac Sacolick
"The book is compellingly written, giving fresh insight into the role of technology officers."- Peter Krasilovsky 
  • Why Digital Transformation is my article on Medium speaks to why we need businesses with long histories of integrity to drive digital transformation
"Startups can challenge these businesses and some will disrupt them, but many long standing businesses can’t easily be replaced. These businesses have to modernize their customer experiences, leverage automation to improve quality and lower costs, and experiment with new business models. To accomplish this, these companies have to learn and borrow techniques developed at software companies"- Isaac Sacolick

CIO Insight Driving Digital


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Three Tips on Developing Successful Citizen Data Science Programs that Drive Transformation

Citizen data science or self-service BI programs are catching on with more businesses and enterprises of all sizes. More executives understand the importance of becoming a data-driven organization and are willing to break away from the mostly manual analytics performed in spreadsheets.

For those of you that have embarked on this journey, here are some key strategies to consider to ensure your efforts drive transformation:


  1. Prioritize dashboards and analytics that have strategic impact - It's very easy for citizen data science programs to fizzle out if they are perceived as just the next enterprise effort to leverage the newest dashboard or reporting technology. To avoid this fate, prioritize efforts around some of the more strategic areas of growth or operational risk in the enterprise especially if they are high on a data-driven executive's radar. Citizen data science programs need to be highly visible initiatives to garner sufficient support if they are going to succeed in transforming more departments to be data-driven.

  2. The job isn't done when you've delivered the dashboard - Dashboards, like any other tools or applications only deliver business value when they are used in business process and stakeholders agree to sunset legacy methods. In many cases, that means using your dashboards and foregoing the use of spreadsheets or performing other manual analysis. So before moving onto the next dashboards, citizen data scientists have to consider approaches to gain user adoption of their dashboards. This is far less trivial than it sounds especially when users demand that dashboards implement improvements a, b, and c before they start using them or inform you that they will continue to leverage legacy practices even when these dashboards are done. Citizen data scientists should plan to spend significant time with end users to illustrate how to use dashboards and analytics when performing specific business processes. 

  3. Create visual standards and establish a Center of Excellence (COE) - Self service BI tools are designed to help citizen data scientists to rapidly prototype and deliver new analytical dashboards. So, it's very easy for even a small group to push out many dashboards very quickly without considering the impact on users when dashboards are published without functional or visual standards. BI programs need to start with some basic visual standards, get agreement on their importance, and grow them over time.

Driving Digital
My newly published book, Driving Digital: The Leader's Guide to Business Transformation Through Technology has a full chapter dedicated to best practices on citizen data science programs.


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How to Rapidly Plan Digital Products and bring Amazing MVPs to Market Smarter and Faster

When it comes to planning new digital products, I find many organizations fall into one of two camps.

"Analysis Paralysis" Camp - These organizations are slow to endorse new product ideas. They send sponsors and product managers back for more and more market research, industry analysis, financial forecasting, and other activities to build up organizational confidence that they are embarking on a low risk, high reward journey. 

"Serial Initiator" Camp - This camp is just the opposite. They float many ideas around and rarely say no to them. Their stakeholders scramble and often compete for marketing, technology and other resources to bring their products to market resulting in many products that don't follow standards or best practices. They then compete with sales leaders to be top of mind when presenting new products to clients. 

These are not ideal ways to manage product pipelines or to drive a digital, innovative culture. 

What is a Product Pipeline?


StarCIO Product Pipeline

Product pipelines are similar to marketing funnels to manage leads or sales pipelines to manage deals. In a product pipeline, organizations start off with a lot of half baked ideas. As more discussion, research, and experimentation is completed around the idea, the product's target customers, value proposition, competitive factors, strategic requirements, and feasibility become better understood. This planning process - whether ad hoc or highly structured - ends up helping stakeholders define key artifacts such as product visions, journey maps, go-to-market strategies, technology architecture, and financial projections.

Unfortunately, many organizations don't define what's expected in their product development process. More specifically, they don't define what decision making criteria is used in making product investments. The culture of the organization often dictates what happens next. Conservative organizations often fall into the Analysis Paralysis camp and stakeholders are left guessing what to focus on and at what level of detail to get backing, approvals, or investment. More aggressive Serial Initiator organizations are likely to start working on lots of ideas skipping many of the disciplines that lead to amazing MVPs.

Rapidly Planning Digital Products


I share a big secret on how to rapidly plan digital products in my book, Driving Digital: The Leaders's Guide to Business Transformation Through Technology

Driving Digital
Organizations that have invested in product management may have standards on artifacts needed for product approvals. What should the vision statement look like? How much market validation is required? What level of detail is required in financial projections? They may also define formal stage gates in their product development pipeline such as what artifacts and approvals are required to tap into budgets available for research or prototyping.

All important stuff and really good when you have experienced product managers that need formal guidance on what is expected of them. 

However

It's not necessarily sufficient especially if you have potential innovators or intrapreneurs that don't have all the training and skills of a product manager. Formal definitions of artifacts and pipelines define a target end state and don't always blaze an easy path for idea generators and influencers to develop a product.

My approach focuses on answering questions. Similar to how data scientists should be asking questions to find insights from data, innovators need to ask and answer key questions around the market, segments, personas, competitive factors, regulation, and other topics to drive smarter and faster from ideas to minimally sufficient plans that can fold into an agile development process.

A lot more in Chapter 6 of Driving Digital
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