Imagine I tell you that I want to go to San Francisco from LA. You then begin plans to determine the best way to travel to SF by either car, plane, or boat. After careful analysis, you select flight as the most efficient way to get us to SF and begin working on travel plans and booking flights. About midway through your work to get us to SF, I come back and say actually, Toronto is a much better destination. How would you feel if the tickets had been purchased and lodging booked? This is what many clients do to their teams by believing that the project objectives are fluid and can be a “living document” to be changed anytime. This the “what” part of tech strategy (what are we doing, what is the destination etc.) that I believe is critical to defining before you get to the “how” (how are we building this, how are we getting to the destination, etc.) of any project.
Let’s be honest, defining the “what” part of tech strategy is hard work and is commonly avoided or giving very minimal attention from managers and executives before they embark on that high-profile digital initiative. It is what causes many tech teams, designers, and PM’s to pull out their hair as they attempt to lock down the objective of a project while being simultaneously pushed to “just build the damn thing and get it launched.”
So how do we avoid this headache and get stakeholders to agree on objectives that provide the team with a compass to find the project’s “True North?” My advice is to be honest with managers, executives, and clients. Let them know that by “evolving” goals, they are forcing the team to constantly find a moving target. This typically results in increased costs, delayed or canceled projects, and a huge hit to team morale. By defining the objectives upfront, the success metrics can be defined and measured upfront. If the metrics dictate a pivot, you have the data to back up a change in strategy. This is why it is critical to define the “what” in your tech or digital strategy.
Daniel is a digital consultant specializing in IT advisory on technology strategy, investment, and implementation. He helps companies solve complex and strategic problems across multiple industries and domains. His drive to find solutions for clients and attain personal growth for himself is what keeps him at the forefront of innovation and helps him guide teams and organizations to cultivate amazing products and services. He can be found on Twitter at @dewilliams.
In my role selling professional consulting services and IT solutions to Fortune 500 customers, I see a variety of support models ranging from simple staff augmentation to large-scale managed services contracts. Although best practices across multiple industries over the last 10 years has seen a gradual migration away from staff augmentation towards managed services, I still see push back for various logical (and illogical) reasons.
What if there was a intermediate step between staff-aug and managed services? Fortunately, there is such a step called “Managed Capacity.” This support model combines many of the perceived benefits of staff augmentation (flexibility, onboarding consultants quickly) with the benefits of managed services (vendor takes on responsibilities for deliverables, outcomes, and management of resources). We still recommend that clients start on the path to managed services, but we have seen the best outcomes when customers start with managed capacity to get accustomed to an outcome-based support model, then move to managed services with all of the enterprise advantages that it brings. Here are the 5 signs you are ready and should move from staff augmentation to managed capacity.
Reason #1: You are seeing budget constraints from unplanned staffing or project costs.
Every organization experiences unplanned costs due to staffing, projects, or changes in direction / strategy. Unfortunately, the easiest (and costliest) way to deal with this is to throw more bodies at the issue through staffing. In my experience, we have helped clients work through these issues via managed capacity where we take on the burden of managing deliverables, outcomes, and time/cost tracking.
Reason #2: You aren’t seeing the project outcomes / progress that you expect from your vendors.
No enterprise project portfolio is perfect and issues / failures do happen. However, if you are seeing a pattern of delays, quality issues, and/or project failures, then maybe the delivery model needs to be adjusted. One question I get from customers is “How would managed capacity help with project delays/failures?” One way in which managed capacity helps is the focus on delivery of required skills to get the project done and not just a “butt in a seat.” Secondly, customers get predictable and cost-effective outcomes. Third and most importantly, customers get active knowledge management that is retained and shared across the customer and project teams, reducing the risk of valuable IC leaving the if there is staff turnover.
Reason #3: You constantly need to ramp teams up and down quickly for new projects or initiatives.
Projects are one of the core elements of an enterprise and ideally, you would only work on planned projects on a carefully crafted roadmap. However, anyone that spent any time on a medium-to-large organization call attest that this is not always possible due to competing priorities, internal politics, and sometime just dumb luck. This is where managed capacity can help manage the shock of fluctuating project needs by outsourcing the overhead and maintenance of staff capacity to an external vendor with the experience and track record of outcome-based delivery.
Reason #4: You are working on new, complex projects requiring specialized skills.
Many projects that enterprises undertake are pretty routine and straightforward (maintenance, enhancements, etc.). However, in order to stay competitive, organizations must innovate with complex projects requiring specialized skills such as new application development, data migration, cloud migration, or new strategy development. Managed Capacity allows us to support a range of skills with minimal risk to the customer. We take on the staffing, deliverable, and outcome risks of complex projects where there many “unknown unknowns.”
Reason #5: You are facing new threats (internal and external) and need your project teams to be more efficient and effective.
There are always new threats to your organization (both internal and external) that you need to address and overcome on a daily basis. How you take on these threats can affect your success or failure in the short-, medium-, and long-term. If you go with a pure staffing model, you will get the ramp up in bodies, but what is the guarantee that you will have the staff you need in the right place at the right time? With new pressures from large enterprises, SMBs and startups, the ability to deliver better outcomes at a lower overall cost could be the key to your organization keeping into advantages and the key to your individual success as a manager or executive.
If you are interested in learning more about managed capacity or managed services, feel free to contact me in the comments or on LinkedIn.
I spent almost 2 weeks reading a 4 -part series on Elon Musk, Telsa, SpaceX and the goal of Getting to Mars, and First Principles Thinking on Wait But Why. The posts are from 2015, but are still relevant today given what we know about the success of Telsa and SpaceX. As I read through each post, I felt like I was going deeper and deeper into a rabbit hole of the importance of engineering, physics, and the need to solve the world’s biggest challenges. By the end, I felt myself asking why more companies aren’t trying to solve big problems and why governments aren’t taking the issues of climate change, population growth, and the probability of humanity being wiped out by a man-made or natural disaster seriously. I highly recommend reading the entire series for anyone interested in science, tech, engineering, and entreprenuership.
In a press release from Gartner, Inc. in October 2016, analysts discuss the growth in overall worldwide IT spending in the coming years, “forecast to reach $3.5 trillion in 2017, up 2.9 percent from 2016 estimated spending of $3.4 trillion.”
The bright spots mentioned in the report are the software and IT services segments. Gartner projected software spending to grow 6 percent in 2016, and another 7.2 percent in 2017 to total $357 billion (see table below). IT services spending is on pace to grow 3.9 percent in 2016 to reach $900 billion, and increase 4.8 percent in 2017 to reach $943 billion.
Of particular interest to me is the software segment as this is the world that I live in every day helping clients develop and deliver innovative software products and applications. I have seen the trend each year skew more and more to “cloud-first” or “SaaS-model.” These numbers firmly backup what I’ve seen first-hand, that SaaS will overtake traditional software development sooner than most business leaders and executives think.
For example, according to IDC, the cloud software market reached $48.8 billion in revenue all the way back in 2014. This represented a 24.4% YoY growth rate from 2013. IDC predicts cloud software to surpass $112.8 billion by 2019 at a compound annual growth rate (CAGR) of 18.3%. If you take the Gardner forecast for software with it’s projected growth rate of 1.2% from 2016 (6%) to 2017 (7.2%), and project that out to 2019, software will represent roughly 10.4% of total IT spending or $377 billion. Of this projected $377 billion in software spending, SaaS will have captured 29.9%.
IDC goes on to state that “SaaS delivery will significantly outpace traditional software product delivery, growing nearly five times faster than the traditional software market and becoming a significant growth driver to all functional software markets.” By 2019, IDC predicts the cloud software model will account for nearly $1 of every $4.59 spent on software.
Why is this important? It depends on your perspective and role in an organization.
If you are a developer, and have not jumped fully into cloud-based development, WHAT ARE YOU WAITING FOR? This is a fundamental shift in the technology landscape. As big an opportunity as those that learned HTML in the early days of the Internet. This is just as big.
If you are a product or project manager, you will (and should) seek out the most cost-effective and efficient way to deliver new applications and features for your customers. SaaS and cloud represent this path for you.
If you are a business manager, you will need to be familiar with modern approaches to application development to adjust your expectations and broaden your imagination to what is now possible with a “cloud-first” model.
If you are an IT manager, you will need to be well-versed in cloud-based approaches to software development and delivery in anticipation of questions and concerns from your counterparts in the business and product teams. This includes knowledge in areas such as containers, orchestration, 12 Factors, cloud platforms (AWS, GKE, Azure, etc.) and the pros and cons of each platform. How will you organize your existing applications and make the move to the cloud? How will you maintain legacy apps in the datacenter and newer apps now deployed to the cloud?
If you are an executive (business, IT product, ops, etc.), you should treat cloud-first as a priority and incorporate it into your strategic plans if you have not done so already. This represents one of the biggest shifts in business and tech since the Internet and the companies that make the move early will dominate. The companies that take a “wait and see” approach will get left behind wondering what happened.
Applications have gotten infinitely more complex over the last 20 years, but the complexity is going to get even bigger with everything related to SaaS and cloud apps being software and governed by software.
If you are interested in how to get a handle your software applications and projects (traditional or cloud-based), check out Mindsight.io, a startup that I am currently advising. They are building tools to increase transparency of software projects, track productivity of the team and ensure the accuracy of their estimates. Mindsight will also enable predictive analytics around software risks, user stories, and code releases.
The last few months have been quite a ride. I took off a few months to help my wife take care of our son back in October and what I assumed would be time spent mostly changing diapers turned out to be much more (I may write about this in a later post).
In addition to having lots of family and friends visit and a bit of traveling back to the east coast, I managed to cram in an Intro to Machine Learning class at Udacity. The only way I can describe it is INCREDIBLE! After spending the last few years leading large teams in digital strategy, project management, and consulting, it was refreshing to get back to coding and learning something new.
I can hear someone out there asking: “Why would you spend your free time in python, learning how to analyze data with machine learning algorithms?”
The short answer is that this is where the world is headed and we all should learn at least one new skill every year.
So what exactly did I learn? Here are the top 5 things that stuck with me from the Udacity class and my own self learning:
Machine Learning (ML) and Artificial Intelligence (AI) are here now, so if the machines will be our overlords, why not jump teams and join them…All kidding aside, AI and ML are already in many products that we use each day and will continue to use in greater numbers and frequency (Amazon Alexa / Echo, Google Now, Google Home, Microsoft Cortana, Self-driving Cars, Parking Assistance, etc.). Many of our interactions online are with Chat Bots that learning from human interactions to get increasingly more conversational each day. I wanted to have a deep understanding of the underlying technology to not only have relevant discussions on the topic with clients and business partners, but also look for new opportunities in this space.
Machines will not make (all) humans irrelevant. This was something that I found interesting as I delved into the accuracy rates of the various algorithms and methods that make ML and AI possible. With most generalized ML algorithms, the best we can currently hope for is between 75% — 90% accuracy. Not bad, but not that great either. There aren’t many situations where 75% accuracy is celebrated. This is where a concept of Hybrid Intelligence fills in the missing pieces to help machines with particularly difficult decisions that humans have evolved to answer very effectively.
The next wave of AI will have us texting with our AI companions in much the same way that we text each other now. The first wave of mass-market AI will be products like Alexa and Google Now that require you to voice your commands out loud. This is a great start and novel at first, but eventually speaking out loud at home when you are alone will seem weird and unnatural (I am there already with Alexa and Google Now). I believe Mark Zuckerberg in the right path with potential integration with FB Messenger and his own custom smart home set up. The coming AI interfaces will leverage the gains made in natural language processing to give us the the ability either voice commands such as lights off or text a picture of a friend to our AI assistant to unlock the front door when they arrive (this involves natural language processing, home security, facial recognition, chatbot, etc…fun stuff).
Many jobs and tasks that are thought to be safe will be disrupted.However, this only means that we need to acquire new skills to move up the ladder into positions or jobs that require human judgment and intuition. Jobs that are repetitive, sequential, etc. are ripe for AI and machine learning right now. However, tasks that require lots of input but have a predictable outcome are being taken on by machines now. Legal documents that are just templates to be filled in by paralegals, legal contracts that don’t/won’t change, technical documentation such as data schemas, network diagrams, devops processes; these are all jobs that machines are doing every day in increasing frequency.
The cost of prediction continues to fall as machine learning algorithms get more accurate and we accumulate more data. Many companies hire consulting firms to analyze multiple scenarios and lots of data to give their best options for a critical decision. These very highly paid consultants are not cheap, but do serve their clients well. However, I believe that AI and machine learning will not necessarily replace the need for consultants, but it will decrease the cost of consulting, data analysis and prediction across every single industry. As we see better use of the data that is accumulated each day and we all get better as asking the right questions of our AI companions, we will see its use in common use from preschool to nursing homes.
Just as millennials were the first generation to grow up with the Internet, we are about to see the first generation (my son included) that will grow up with AI as the norm. The next few years will be really exciting and I look forward to being a part of it.
Daniel is a digital consultant specializing in IT advisory on technology strategy, investment, and implementation. He helps companies solve complex and strategic problems across multiple industries and domains. His drive to find solutions for clients and attain personal growth for himself are what keeps him at the forefront of innovation and helps him guide teams and organizations to cultivate amazing products and services. He can be found on Twitter at @dewilliams.
In many Chief Information Officer (CIO) organizations, there is a perception by customers that CIO capabilities can be very limited. In these types of environments, information technology (IT) is viewed more as a cost than a strategic investment. In these cases, customers may only work with the CIO organization for network issues of email problems. To the customer, the CIO may meet their expectations in dealer with an issue, but falls short in providing continuous strategic value. However, the modern CIO can take a lead role in changing that limited perception, moving the organization toward fully leveraging IT to provide real strategic value to the enterprise.
For the last 15 years, I have focused on identifying and building new solutions that solve unique problems for public and private sector organizations. In that time, I have come across many obstacles causes gaps and finger-pointing between business and IT teams. Many of these gaps have delayed or impeded progress, thus causing quite a bit of pain for team members, stakeholders, executives and even customers as progress isn’t being made on the tasks at hand. Below I outline the six reasons I believe are the top contributors to the ever growing gap between business operations and IT that are possibly holding back you and your company’s digital transformation goals.
1. Conflicting Objectives and Strategies
In most large organizations, IT and other company business groups evolve in separate but necessary directions in order to accomplish what that division is tasked to. The IT group focuses more on the basic IT hierarchy as it relates to the needs on the overall business (network, security, email, hardware, software, back and front office, etc.). Business groups evolve to solve the needs the customers (marketing, products, services ¬– e.g. consumer value). Based on this simple description, it would seem that both groups work together in a sort of layered or tiered model, with IT supporting the business and business supporting the customers. In reality, this is usually not the case. What you often find are silos where everyone is working on their own projects with little collaboration with other groups.
Business teams normally look for an opportunity in the market to increase revenue, decrease costs, or improve performance. This can take the form of a new or improved product or service. It can take the form of automating previously manual processes, or it could simply be to phase out redundant or legacy solutions.
IT teams normally look to balance IT stability with incremental improvements to their tech stack. This is where the divergence rears its ugly head and we begin to hear rumblings about IT holding the business back from its digital dreams. This stability is necessary in a functioning organization and without it there would be chaos and constant executive escalations.
2. Different Priorities
Business and IT inherently have different priorities given their perspective with the organization. IT’s basic function is tech stability whereas the business is focused on bringing new revenue and even disrupting current tech to make way for innovative ways to serve customers. This is where we typically see conflicts with the organization with finger-pointing on failed or delayed initiatives (“IT is holding us back” or “the business needs to understand this IT program is a critical initiative”). So how do we bridge the divide with not only different, but also conflicting priorities?
This is where strong digital project managers (PMs) can provide the most value as they have the deep technical experience and strong understanding of the needs of the business come in handy. Digital PMs are able to accomplish this through hands-on working sessions using the “post-it note” process to digital portfolio reviews that include stakeholders from both the business and IT to ensure priorities are aligned. There are many are tools that are available to digital transformation teams to bridge the gap which I plan to discuss in future posts.
3. Different Definitions of Measurement
Having spent a number of years in IT before moving on to management consulting and digital marketing of enterprise retail, I understand that different groups across an organization will define and measure success based on their own objectives within the organization. The IT team normally measure success in terms of the technology (uptime, page speed, bps) or project implementation (time, budget, scope). The business teams attempt to measure success in terms of business drivers cost reduction, revenue, leads, and profit. However, when IT and business groups come together to achieve a common goal such as digital transformation, their needs to be common criteria to define and measure success. From cloud migration to API implementation, success lies in the initial rounds of communication can ensure that end goals and KPIs are met.
In order to achieve communication success, the best approach is to properly onboard all necessary teams to the project so that they understand the business drivers of the project and can define IT metrics that can be tracked and support the overall initiative. For example, if the business driver is to increase customer account registrations by 50 percent over the next six months, IT would be expected to deliver a solution that removes a friction to the sign-up process (showing an increase in sign-up completions) and has been deployed to production (well before six months). Too often we see IT teams come to the table with a solution that takes six to nine months for implementation and is not focused on the business value (increased sign-ups in this example). By using an onboarding approach, both business and IT can have alignment on what is important (KPIs related to what should be tracked and measured).
4. Lack of Customer Understanding
Have you ever filled out an online survey or given feedback on the user experience on a website? If so, there is a very high probability that your feedback was presented to a few stakeholders in marketing, but never made it past that single presentation. When the planning for the next redesign or product iteration begins, all of that valuable customer feedback is either forgotten or deprioritized in favor of other stakeholder requests (executives, senior managers, and more). What is needed to solve this problem is a simple process to ensure that customer feedback flows into the release planning for the future enhancements. If these are paying customers, then their feedback should get pushed to the top of the list at least for a proper review.
5. Too Many Silos and “Fiefdoms”
Maybe it is my background of working with the military and law enforcement throughout my career, but I found myself accustomed to always have a senior officer, commander, or even civilian manager that could resolve disputes between two different but equal groups. In the private sector, this is a luxury that I sometimes miss dearly. The many siloed organizations; the fiefdoms that spring up to protect a manager or executive’s personal priorities are the very obstacles that block true digital transformation as these fiefdoms fight progress for the whole in favor of the status quo for the few. Until these silos are knocked down, true progress cannot be made to bring an organization into a digital 21st century. In many cases, the only option is to spin out a “tiger team” that is completely separate from the larger organization to is then capable of delivering the true promise of digital transformation.
6. Access to Data and Services via Simple/Easy-to-Use APIs
In looking at the success digital and tech projects that I have led and been at part of throughout my career, I can see a common thread across all of them: access to data via easy, simple APIs. This allowed our teams to quickly prototype ideas throwing out the bad and keeping what works for continual improvement. The APIs were available with simple web-based documentation, there was no need to set up a project to access the API, and there were no “gatekeepers” exercising an API tax (this is more common in larger organizations and one of the main causes of failed integration projects).
There are quite a few API solutions that are available that make this digital transition APIs to easier. But first there needs to be a top-down directive that all data within the organization will be accessible via API and there will be no “walled gardens” or project “taxes” to access these APIs. Amazon took this approach in the early 2000’s and we see the results. I am ended this post on this topic and the outline of the Jeff Bezos’ directive because I believe this is most critical point to digital success:
All teams will henceforth expose their data and functionality through service interfaces.
Teams must communicate with each other through these interfaces.
There will be no other form of inter-process communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.
It doesn’t matter what technology they use.
All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.
Anyone who doesn’t do this will be fired.
I will borrow one final bullet to this list from Steve Yegge’s Google Platform Rant that I believe is critical for anyone that reads this post, works in an organization that has not begun it’s digital transformation (or in the midst of transformation), and does not feel a sense of urgency:
I’m not saying it’s too late for us, but the longer we wait, the
closer we get to being Too Late.
Most people think of Enterprise 2.0 of Enterprise Collaboration as a particular set of technologies, such as blogs, wikis, and profiles. Others describe it as simply the ability to share information or knowledge within the enterprise. However, these definitions are inadequate–Enterprise 2.0 is the ability to leverage business and IT strategy together to increase the effectiveness and efficiency of technology initiatives. Therefore, to establish Enterprise 2.0 means organizations must choose and measure IT projects on the basis of three criteria: 1) does it increase revenue, 2) does it cut costs, and/or 3) does it increase performance. Enterprise 2.0 is not simply a series of technology tools, but a transformation of the enterprise mindset to build strategy to address business requirements that realize cost savings, performance improvement and if possible, new innovative sources of revenue.