Technical debt

There were times in college when I would not do laundry for weeks (gross, I know). Then finally I would have no choice but to spend an entire Sunday in the laundry room. I could not do any of the fun things I would normally associate with a Sunday, and I would kick myself for not doing it more regularly to make it less of a hassle.

That is exactly what it is like when software, systems, and tools are not upgraded, patched, or replaced on a regular basis. The time and money associated with these legacy systems piles up and contributes to your company's "technical debt." The longer it lingers, the harder technical debt is to clean up.

The perils of too much technical debt...

Preventing and eliminating technical debt

The best way to avoid technical debt is to have a continuous pulse on your systems and their health. This is everyone's job. Everyone must be aware of the risks and understand the impact of their technology's lifecycle. This requires the people most familiar (usually the most technical) to be able to articulate the need and value of an upgrade, for example, in ways senior management or product owners can understand. That is not always easy to do, so I recommend ways in which addressing technical debt becomes part of the process:

1. Continuous improvement culture - A culture in which people are rewarded for making things better and always striving to improve is the single best way to minimize technical debt. When team members inherently feel motivated and empowered, technical debt goes down exponentially.
2. Mandate open standards - Utilizing proprietary technologies can lock companies into vendors or tools for many years. This can be quite costly. Mandating the use of technologies which are built upon or use open standards is required to be nimble and relevant long term. Open standards prevent team members from re-inventing the wheel, help avoid vendor lock-in, improve agility and choice, and dramatically increase application portability and integration. These all in turn help minimize technical debt.
3. Add a hardening sprint - If you are following Agile practices (and even if you are not), you can consider adding a hardening sprint to your schedule every quarter. So if you do 2-week sprints, the last 2 weeks of each quarter can be dedicated to working on stories related to reducing technical debt (or "hardening" the system). The beauty of this is it becomes part of the whole team's routine, and increases visibility to the importance and benefits of eliminating technical debt. It does still require those closest to the technologies to be proactive in identifying improvements and articulating the value to the product owner.
4. Semi-dedicated Systems Team - Having a separate team of people dedicated to "owning" the architecture and maintaining system health sounds good on paper, but actually it leads to accountability problems. Instead, having everyone support what they build makes people think twice about cutting corners and throwing things "over the fence." As a result, I suggest having a group of senior technical folks dedicate 20-30% (not 100%) of their time to continuously improve and monitor system health. This does not mean that they do 100% of the work, but rather set the standards, goals, and requirements for the them and the rest of the team to do and follow. Some (or all) of the work done in a hardening sprint, for example, likely will be suggested by the Systems Team.
5. Call for back-up - In many cases, legacy systems come with significant risks to the business, usually security-related. Leverage your friends in the security department to help you build a business case for making the necessary changes to your systems (or what might happen if the changes are not made). Be sure to also paint a positive picture of the business benefits to come in the future as a result.
6. Governance - While my least favorite on the list, sometimes it does take some hard and fast governance rules to help prevent technical debt. Projects should not be approved which build upon legacy systems or have no clear plan for future upgrades.

Customer reliability engineering

I was blown away when I spoke with Dave Rensin during my visit to the Google campus. He is Google's Director of Customer Reliability Engineering, and his views on customer support are world-class.

Dave's goal is to drive customer anxiety to zero; to remove the things that would cause customers to want to leave.

To do this, Dave's team has several principles they follow:

1. Make sure your customer understands they are not alone. They need to feel a sense that "we are in this together" and you will stay with them until the problem is solved.
   
2. Ensure customers never feel like they are talking in a vacuum. Never let the customer feel like you know more than you are telling them. Tell your customer all the details (without sensitive info, of course).
   
3. Create a shared fate with your customers. Arbitraging issues with money (or credit) is not good enough. Dave's team reviews their customers' production systems and provide guidance on how to make it up to Google's standards. If the customer meets those standards, they will then identify when issues are caused by their customer's system, and proactively reach out with possible solutions (utilizing a shared dashboard both his team and the customer can see) -- and they do that for $0. This drives mutual accountability; truly being "in it together."

Dave goes into some details of the above in the video below. More details can be found in Dave's Google Blog post.

Bet on machine learning

Companies like Google and Microsoft offer impressive machine learning capabilities in their public cloud products. This means artificial intelligence is significantly more accessible to any business than before.

How does it work?
At a high level, machine learning is a data analysis method which uses historical data, examples and experience to devise a model to automatically predict future outcomes (instead of hard-coded rules). The key is the "learning" part: the algorithm continues to evolve to make the predictions more accurate over time.

The traditional ways of machine learning involved more manual methods of developing models and algorithms. IBM's DeepBlue, for example, was programmed to learn to play chess in the 1990's (and beat the world champion). However, chess has a relatively small and finite set of moves per position (about 20) -- fairly easy to program a computer to learn through brute force.

Fast forward to 2016 and Google's DeepMind project AlphaGo. It utilizes sophisticated neural network algorithms, and was used to defeat the Go world champion. Go has about 200 moves per position, with more possible board configurations than there are atoms in the universe! This demonstrates the power of the neural network algorithm. Most importantly, it shows that general-purpose artificial intelligence can exist.

Neural networks mimic the learning process of the human brain. The AI from DeepMind uses a technique called Deep Reinforcement Learning. It learns from experience, using raw pixels as data input. AlphaGo was shown hundreds of thousands of Go games so it could learn from human players. Then Google had the AI play against itself 30 million times. Over time, it got better; to the point where one of the algorithms had an almost 90% win-rate against the other. That was the one selected.

Naturally, a human could never play 30 million Go games in their lifetime. The machine does not get tired, nor make emotional mistakes. The AI's experience becomes super-human, despite the fact it originally learned from humans.

Watch what happens when Google used the same algorithm to train the machine on the famous Atari game Breakout. The goal given to the machine was to maximize the score it could achieve in the shortest amount of time. At first, the AI is pretty terrible at the game. However, after about 2 hours of playing, it is very good. After 6 hours it does something amazing: it becomes super human.

Swiftkey, the makers of a keyboard app for mobile devices, nicely demonstrate how a neural network helps improve their word predictions.

Using ML in your organization

The ability to plug directly into some of Google's (and others' like Microsoft and Amazon) algorithms in the cloud make ML much more accessible. I am more familiar with Google's offerings, so will highlight a few:

Google's Cloud Vision API is image recognition in the cloud. It can detect what is occurring in images (including sentiment analysis of humans). A city in Canada trained Google's AI using thousands of school bus stop sign videos. The goal was to have the machine watch the videos and identify if a vehicle went passed the bus' stop sign illegally. The algorithm was trained to identify when the sign was out and active, and when vehicles had passed through it. It turned out to be 99% effective, while humans were only 83%. This resulted in increased revenue through traffic violation tickets.

Disney used Cloud Vision for their marketing campaign for the movie Pete's Dragon. The site set children on a hunt in their homes for common objects (like chair, door, tree, clouds, etc.). Once detected by the algorithm, Elliot the dragon would magically appear on the screen.

Google's natural language processing API is something which could be leveraged in the example given in my earlier blog post on data. By analyzing millions of public social media posts for certain sentiments and cues, the sales team can potentially land deeper leads.

Google also has a translation, audio-to-text, and even a new job search API.

Lastly, Google's open-source TensorFlow is a machine learning library for numerical computation using data flow graphs. Developers can use this to build models with very little code and eventually translate them into products in Google's cloud.

The future: humans + machines

I believe the businesses which adopt and master machine learning the best will be the most successful in the future, regardless of industry. (Of course, it helps to have a lot of data to train your model.)

While ML may eliminate some jobs, I feel it will be the successful partnership of humans and machines which will bring the most fruitful benefits. Take a radiologist, for example: she may leverage ML to assess her readings faster, but also provide additional oversight for deeper analysis. 

Ultimately, where the AI takes us is hard to predict, but the positive impact and advancements made will most certainly be exponential.

Data, data, data

"Data, data, data" is the new "location, location, location."

Uber owns no taxis, yet is the largest taxi company in the world. AirBNB owns no real estate, yet has the most accommodations in the world. These companies run their businesses on data, and lots of it.

Data is king, and it is only becoming more important. Proper analysis and utilization of data helps to uncover the what, the why, and even predict the future. As a result, data must be a core component of your digital strategy.

Hindsight
At the most basic level, data gives us hindsight. A simple example is how grocery stores utilize loyalty cards. Customers sign up for them with some basic personal information, and in return the store gives the customers discounts when they use their card. Data collected from these cards helps the grocer identify individual purchasing habits -- it gives them hindsight.

This is why online retailers encourage customers to create accounts. The data collected (which products are being viewed, which terms are being searched for, etc.) all help track what is happening in their store.

Insight
Understanding the "what" is just the basics when it comes to data analytics. Having the view into the "why" provides insight.

Why do certain customers buy one product over another? Why do certain products sell better at certain times of the year? These are the types of questions the data can help provide insight into.

Foresight
Being able to predict behavior is the next step; this is where the most positive transformation can occur for an organization.

Again using grocers as an example, stores can use big data to predict and suggest the price points of certain products at certain times to ensure the right amount is in stock and fresh. If the price of strawberries, for example, is too high grocers risk having too many in stock and the strawberries going bad. If they accurately predict the right price point, they can keep the right amount moving off the shelves at a pace that ensures each package is still fresh.

Lastly, there are some scenarios where proper data analysis can actually help to prescribe some actions. In other words, using data can help make things happen.

Let us use the car company Fiat as a fictitious example for this. If Fiat mined the publicly available social media posts specifically looking for terms which suggest a propensity to buy their car, they may be able to help drive more sales. The scenario could go something like this: John Smith posts to Twitter, "Thinking about buying the new Fiat. Can't decide between that or the Toyota Prius." That post will get picked up in Fiat's social media scanning algorithm, and alert the salesperson in John's region to contact him directly. That contact may help to influence John in purchasing a Fiat.

Making it happen in your organization
To leverage data effectively, naturally you need data. Determine the sources, and if none exist start setting up your data collection processes.

Once you have the data, it needs to be usable. Having it in 25 disparate systems will make life tough. Rather centralizing it and "cleansing" it for use (i.e. ensuring accuracy, removing duplicates, etc.) is key.

Additionally, data can help create a source of revenue. Identify any data which may be unique to your organization which others externally may pay to access. Ensure proper usage controls and governance are in place.

Also keep in mind potential external integrations or partnerships.

Ultimately, there are endless possibilities to how you can utilize data. Start small, take an MVP approach, and build from there as you learn what works for your organization.

High-performing teams, Part II - Being proactive

There is not one thing which creates a high-performing team (HPT). Trying to define the numerous aspects of an HPT culture took me an entire blog post. However, being proactive is one key attribute required for all individuals of a high-performing team.

Doing what is expected
My prior post discussed team growth expectations. In order to achieve continuous growth, each individual simply doing what is expected of them is not enough for achieving HPT status.

Take a software developer, for example. They are expected to create X features working on Y product while collaborating with their teammates. They are expected to complete those features on time, follow proper standards, and ensure their code is efficient and secure. That is the baseline. That is expected of them each and every day. While that may sound great, my view is if everyone on the team did that year after year team growth would be stagnant. (And it may get boring for the developer!)

Being proactive
Being proactive is the key to unlock exponential growth and creativity in both individuals and teams.

The definition of proactive:

Creating or controlling a situation by causing something to happen rather than responding to it after it has happened.

Take a software developer again as an example: They can be proactive in numerous ways, including identifying a new solution to a problem the team is facing (without being told, of course), implementing it, and organizing a lunch-and-learn session to ensure everyone is aware and understands the new way forward.

The key is for individuals to take the initiative in looking for ways they can help improve themselves, the team, and the company. This is often where new and creative ideas emerge, which naturally leads to learning and mastery.

The proactive expectation (and contradiction?)
I argue being proactive is therefore expected of all team members.

Does this mean, however, any proactive work is then simply viewed as par for the course? Does this mean no individual can ever be seen as going above-and-beyond?

No. The beauty of being proactive is while it is expected of everyone, there are so many ways in which it can be done. Therefore, it is impossible to define exactly how to do it, so it can never be explicitly expected.

Be open: Integrate and let integrate

A key principle I drive at my organization is technical openness. This means all the tech we leverage should be based on open standards and frameworks. There are many reasons for this, including:

• Superior interoperability and integration with other systems.
• Prevents "re-inventing the wheel."
• Avoids being locked into proprietary and costly technologies or vendors.
• Improves agility and choice; can select best-of-breed solutions for each job.
• Broadens support pool and timelines.
• Increases innovation, as open standards invite everyone to participate in providing feedback.

I believe the ability to integrate fast and effectively is a skill which all companies will need to survive over the next few years. This is why the first bullet above is most critical.

Examples of key business integrations 

Here are a few "integrations" which help drive business growth:

1. Pizza Hut can be ordered directly through Amazon Echo (Alexa). Amazon provides vendors a standard way of connecting to their Echo service, and companies like Pizza Hut are able to connect their ordering systems to allow for another potential revenue stream. Pizza Hut was one of the first onto the platform because their systems allow for integrations with external sources to place orders.
   
   
2. Uber is a great example of being able to integrate with various channels. Users can request rides directly within both Google Maps and Facebook Messenger. They try to capitalize on being available to request a ride at the exact moment when someone is likely to need one.

The examples above demonstrate the need for enabling the in-the-moment, simple, and fluid purchasing capabilities. None of which would be possible if the systems were closed and unable to move quickly to meet the changing dynamics of their users. 

There are other examples which do not include purchases, but rather provide information or other service more easily through atypical channels (see KLM's Messenger integration, for example). Those help to drive customer engagement, satisfaction, and loyalty. All wins for good business, and only possible with open technologies.

Integrate and let integrate!

High performing teams -- Part I: Culture

We all want our teams to be high performing. Here are just a few general traits I associate with high performing teams:

• Team members collaborate extremely well, with deep trust and openness;
• Consistently output high quality;
• Deliver at a rapid pace;
• Continuously learn and able to shift to new areas;
• Demonstrate innovation and creativity;
• Strong customer focus and knowledge;
• All team members contribute to their work and also proactively seek to improve the team;
• Has the same goals, moves in the same direction.

How do you create high performing teams? It takes a bit of effort across several different areas. I will try to use my experience to provide a framework, starting with culture.

CULTURE

Having the right culture in place is the first step toward achieving high performance. Here are some key areas I focus on:

• Relentless optimism. Doubt, fear, and negative outcomes from past experiences can hinder individuals, and bring down the entire team. With a positive outlook comes more possibilities. More possibilities bring infinite upside.
  
  Relentless optimism must start from the top. Leadership has to believe in positive change, positive results, and envision a future that is bright.
  
  This also means the senior members of the team (those whom the team look up to) need to pay attention to seemingly minor things like body language in meetings, and wise remarks that may spark doubt into others. There cannot be rolling of the eyes or anyone blurting out "Yeah, right!" Statements like, "This will never work," should be replaced with, "This could work if..."
  
• Ownership. When individuals are held accountable for their deliverables, they are more likely to ensure its quality.
  
  Leadership must identify areas where they want the team to take full ownership of their work. The team can also help to identify ways they want to be measured or demonstrate accountability. It is important for leadership to truly step away here; allow the team to be autonomous in their solutions while never micromanaging.
  
  Taking ownership should not be dreaded, rather done with pride. Leadership must help paint this light, recognizing that demonstrating a lack of ownership (perhaps as first examples for the team) could be challenging. So I remind leadership to ensure praise is given to those doing well.
  
• Failure + rapid learning is OK. A high performing team knows that it is fine to fail fast and cheap, as long as they learn from it. A team creating a new product, for example, does not want to find out it will not sell in the market after 18 months of development. They want to test their hypotheses and obtain feedback early to ensure the company does not waste time and money.
  
• Continuous improvement. The team must have a constant and proactive urge to improve processes, products, tools, and each other. I wrote about the bad words in a previous post -- those must all be removed in some fashion. I also wrote about finding the time to innovate through automation and elimination -- the essence of continuous improvement.
  
  Leadership must also make time for learning and development. Sending employees to courses can be beneficial, but how many of them are truly worth the time and money? Pick and choose wisely, and ensure results from training can be measured and demonstrated. Look for other avenues to embed training in ways which engage the team more. This takes a deep understanding of the individuals on the team, and how they operate.
  
• Need for speed. Each team member must have an eye on the speedometer. What is slowing down the process? How can we get something out the door faster? This is similar to a continuous improvement mindset, but focused strictly on speed to deliver.
  
• Safe to speak up. In order to achieve a lot of the above, the team must feel safe to speak up. Challenging status quo can be uncomfortable if leadership is not open and does not truly listen. New ideas will not emerge from team members if they are continuously stifled or ignored. Create an environment where everyone feels like their opinion is valued and they can make a difference.
  
• Have a purpose. With everyone moving toward the same goal, vision and standards of work, the team will move in lock-step even when leadership is not looking. Leadership must set out clear goals, measurements, and objectives. They must be continuously re-enforced and re-visited in various ways to demonstrate progress and positive impact.
  
• Have fun. A culture of friendliness, fun, and collaboration ensures everyone trusts each other, is willing to lend a helping hand, and enjoys coming to work. Smile, give high fives, and lighten up a little. :)

Creating the time to innovate -- Part II

I wanted to expand on my earlier post on creating more time. As I inherit or join new teams, the first deep-dive I do is an analysis on what people are spending their time on. The goal is to identify what can be eliminated, automated, or delegated to provide us time to do more impactful work.

Eliminate
I often tell folks, "Some parts of your role will be eliminated." I can sense fear in their eyes when I say that, so I quickly follow up with, "The goal is to eliminate the items which are not value-added work, so you have time to do more exciting and creative work."

The reaction I tend to get is no longer fear, but excitement, followed by a, "How...?"

The how is easier than you think. I propose taking an inventory of the work the team is doing, and assessing each item for its value and impact on the organization as compared to how much time is spent doing the task. It may become apparent some things which take a lot of time are not worth the value it is providing.

As an example, one team member told me each month for the past 2 years he worked on producing a set of reports for a particular group. It took him 2 days to compile the reports manually. I challenged him to identify if the reports are even being used or are still needed by that group. As it turns out, the reports were not being looked at any longer. He stopped creating the reports, and got 24 business days back each year.

Automate
Some things cannot be eliminated. Automate them.

Here are just a few items you should consider automating:

• Repetitive or common tasks
• Mindless items -- not requiring much human skill
• Anything considered a standard change (and if it is not standard, make it standard so it can be automated!)

The point is to let the machines do the work for you, so the team can focus on things which require more critical thinking and creativity.

Delegate

Having a single person be the sole holder of knowledge in a particular area can create bottlenecks. It means they may get pulled into urgent issues or other areas to help because no one else can. This is a time killer. They must spread their knowledge and delegate to others. 

Delegating can also occur in the reporting example I gave above. If those reports were still being used, perhaps the report creator could have devised a way for his business partners to view the details on their own. In other words, they can view or pull the reports whenever they needed without asking my team for help. An effective delegation.

The bad words

Certain phrases are considered curse words on my teams. They represent the opposite culture which I try to instill. We strive for continuous improvement and innovation. We cannot settle or become overly comfortable, because technology moves at the speed of light. We must always be learning and thinking ahead.

Here are a few of those:

"That's the way we've always done it..."
Or also, "We've done it this way for years." If you hear this often, it generally means your team is probably far behind high-performing teams. Getting complacent or not having a constant pulse on improvement will eventually make your team irrelevant.

"Legacy system"
Why does this legacy system still exist? It is likely that managing it is painful, it contains critical security holes, and only a few employees understand it. Removing or upgrading it will reap many positive benefits. Quantify those, demonstrate the value, and kill the legacy stuff!

"Temporary code"
There is nothing more permanent than temporary code. We spend a little bit more time up front to get things right and not have to pay the price three-fold in the future (when things may break or require additional efforts due to earlier "shortcuts").

"Manual work"
We believe in automating everything. We want to be doing deep work tasks, letting the machines handle the trivial stuff.

Changing culture and creating the time to innovate does not come overnight, but demonstrating small wins along the way helps to reinforce the desired behavior.

The digital urgency

Software is disrupting business.

The days of stating things like, "We are not a technology company, we do X," are over. (Fill in your choice of traditionally non-tech domains for X: finance, farming, etc.)

Every company should have a digital urgency: a healthy dose of paranoia, knowing your business can be severely disrupted by a more nimble and tech-focused firm.

PayPal was not invented by a bank. Uber was not created by Yellow Cab. Blockbuster did not invent Netflix. Disrupters can come from anywhere and anyone.

If your leadership does not believe your company is a technology company, you must act fast. Otherwise your business may not exist in 5 years.

Here are some thoughts on how your company can stay relevant and use technology as a business driver.

IT is no longer a back-office function
Most great IT departments help deliver key business solutions which drive down costs, improve productivity and efficiency, enable scalability, and increase security. The continuous improvement mindset of great IT employees is something which is hard to teach, but exponentially valuable. Combine that with the "anything is possible with technology" attitude, and you have found a powerful digital engine that is capable of being at the front-lines of business decisions.

General Electric (GE), the traditional industrial juggernaut, is now a digital-first company. Their Predix platform helps power what they call the Industrial Internet. It enables asset and operations optimization by providing a standard way to run industrial-scale analytics and connect machines, data, and people.

Jeff Immelt, GE CEO, talks about how the GE Chief Information Officer (CIO) role has changed. Immelt recognized the amount of technical expertise GE had in-house, and combined the CIO's org (IT) with the engineering org (OT) to make a new Digital org driving revenue for the company through technology. Listen to his keynote address at GE's Minds and Machines conference where he describes their transformation.

External strategic radar
Every company should have a constant pulse on the external technology landscape: trends, start-up activity, etc. However, it needs to be structured. Thresholds should be created to know when the company should take action in a particular direction. When is a new concept really worth the investment? How early do we want to be on this trend?

The list of trends can be specific to your company's domain, but can also contain a few outliers that may not be initially obvious to how it may eventually fit into future plans. The list should be regularly monitored and reviewed at senior levels, and be visible to management at all levels.

Internal innovation: Hack Days
Autonomy is quite possibly the single most powerful "tool" for bringing about innovation and creativity from individuals. A Hack Day (or days) can further enable this.

Mentioned in my previous post about motivation, some companies, like Atlassian, give their employees the complete freedom to work on whatever they want for a short amount of time (24 hours each quarter, in Atlassian's case). They have seen new product ideas, enhancements, and other improvements come as a result of these.

I propose variants of this to see what works best for your organization. One variant might be to have an idea capturing tool used prior to a Hack Day, and participants can choose which of the top ideas they want to be a part of creating during the Hack Day itself.

The key is for management to be on board with giving their employees the time to do this. In addition, management must make this visible and regularly commit to helping fund or enable some of the best ideas to fruition. This is not a one-time event. This must become part of the culture.

In summary, the digital threat is real. The need for digital urgency is required from all levels of all organizations. Make the time to get the best out of your technologists, constantly monitor the external landscape, and enable creativity and innovation from within.

Creating the time to innovate -- Part I

I am frequently approached by leadership from other divisions asking how my teams find the time to be so innovative. I propose that it is not finding the time, but rather creating the time. We all need more time in the day, but if you create a culture which inspires quality, you will naturally have the time you've been looking for.

Culture
A culture of continuous improvement runs through my team's veins. When inefficiencies arise, the team identifies solutions to improve productivity.

Team members are encouraged to give back to the team (I call this "team community service") by proposing and implementing better ways of doing things. Generally it's about 20% of their time (equating to about 1 day per week).

The key is not assigning tasks nor me saying what to do, but rather giving each individual a blank slate to identify and contribute to the areas they are most passionate about. (See previous post about motivation.)

Quality
Where do we get the time to implement these solutions? We have a constant pulse on things which prevent us from working on value-add tasks. From here we identify where we need to simplify or improve quality. These improvements in quality add up to very large time savings.

At first the team uses this extra time to catch up on value-add work and achieve a consistent flow. However, once we achieve optimal flow, we use the extra time gained to continue to innovate, gradually reaching the magic 20% time for each individual.

Suggestions to get started
Analyze your team's errors, production bugs, defects, and other distractions which require someone to stop what they're doing and spend time fixing issues. Use the 80/20 rule to determine the 20% of items causing 80% of the issues, and start to eliminate them.

It may be difficult at first, but leverage key team member strengths and passions. Some folks will not mind putting in extra effort, especially if it means helping the team in the long run and working on something they enjoy.

Be sure to track your team's progress. Take a baseline of key metrics today (number of production defects, average time spent fixing issues, etc.), and track improvements along the way.

The key is to have a tipping point in mind: when do you stop giving the time saved back to "business as usual" work and start giving it to "team community service?" Some individuals may only be able to reach 10%, while others may reach 20% or more.

You will see that 10-20% of time spent on innovation and continuous improvement will produce 2-10x gains for your team in the long run. Create the time to do it.


Update: Read Part II of this topic here.