1 – Follow the CAR framework. Based on the Context – Action – Result principle, where:
Context – why was this needed / background
Action – what things did you specifically do
Result – what was the outcome (preferably quantified)
2 – Make it One Line / Skill. Each line of the CV should show a unique skill e.g. analytics, team work, product/platform expertise, problem solving.
3 – Align Resume bullet points to Job Description. Try to figure out what the job description is asking for, and then tailor your resume accordingly. Each line of the resume should align to each line of the job description of the desired role.
Hiring managers regularly spot such things as it shows structured thinking and makes it easy for them to match your skills to the requirements. So if job description says skills required:
Technical fluency in IBM Websphere BI
Strong communication skills
Strong team work
Strong problem solving skills
Then resume lines for your last role (at least) should follow the same format
Line showing IBM Websphere BI technical skill using CAR (Context, Action, Result)
Line showing strong communication skills using CAR
Line showing strong teamwork using CAR
Line showing strong problem solving skills using CAR
4 – Linkedin, Github and other relevant profiles updated
Include working links to updated LinkedIn, Github, and/or any other profiles a hiring manager can find on the web, especially in this day and age where most resumes are being submitted electronically through email or ATS’s (Lever, Greenhouse, etc.). A 1 to 2-paged resume isn’t enough cover everything you can offer so having these supplements can help hiring managers see if you are a good fit.
A one-page (yup, keep it to one page only) letter that is very important in making your application stand out from the candidate pile. In a cover letter, you can explain certain parts of your resume that you want the hiring managers to focus on. Here’s a sample cover letter I used in 2013.
It also allows you to address a specific individual in the company (the hiring manager or Head of HR, perhaps? and not the generic “To Whom it may Concern”) can show how much you are committed to getting the job because you clearly did your research. And while you’re at it, make sure to take the cover letter as an opportunity to tie yourself to the company — learn about their goals, mission and vision, etc. and connect it to your experiences. It should answer questions like: Why this field? ~ why consulting / marketing / engineering? Why this company?
What’s important in writing this letter is that you make it simpler to read. You have to assume the reader is an idiot, so don’t leave anything for readers to make connections / speculations on their own. In other words, don’t make them think at all, just do the thinking for them and spell it out. You don’t have to spill everything, just highlight a few relevant details to make them interested and want to learn more about you to give you an interview.
Alternatively, you can send an email to someone with a potential opportunity.
Sending out your resume
Before you send out your perfectly formatted CVs, don’t forget to:
While spell check and grammar check tools are helpful, don’t rely 100% on them, have your family and friends to look at your resume and cover letter for you for a fresh set of eyes.
And another quick tip: whenever you send in your resume to someone, try to write an email 2 to 4-sentence summary of your application, as it helps them understand your profile without opening any attachments. The summary can briefly include: what you did, where you went to school, what kind of roles you would be great at, what type of person you want to work for. It is similar to a cover letter, but much much shorter, aim for the highest number of insights with the least number of words.
Optionality killed many startups in good times, and will impact more in unprecedented times like now.
We will hear a lot of bad news about tech startups doing layoffs or shuttering down.
Remember that no-decisions is also a decision, and usually not a good one.
The world has come to a standstill. It has never happened before in our lives.
If you have 36 months of runway, consider yourself lucky and sit tight.
If you can get to profitability, do it now whatever it takes.
If you need to raise money, remember you are not in a position to negotiate terms.
If you need to downsize, cut once and cut deeper.
Don’t listen to the gambler in your mind, spending that extra month to get ahead of the competition when this is all over is not a good idea.
To be ahead of your competitors by 1-2 years, you just need to keep standing when this is all over.
Once your risk of ruin is reduced to near zero (as close to 36 months of cash as possible), take the opportunity for the smallest possible bet on a validated opportunity that will deliver outsized returns on your business, or you will learn about the fatal flaw as quickly as possible.
This is the time to work on it since the world is going through an unprecedented pause.
Again, secure survival before making any opportunistic bets.
As you make your bet(s), remember that 20% of your efforts will yield 80% of the results so really push your thinking to find your blindspots and increase chances of success.
The return to normal will not be rapid, it will be slow. The pandemic doubled every few days, the return will take months and years.
It will be a new normal anyway, so time to re-think all your strongly held assumptions.
We’re entering a new world of practical digitization focused on needs over wants, where teachers become influencers, and learning goes online in a rush.
If you want to work on something new, make sure it’s a knife worth falling on.
The job of the founder is to make better decisions than others, its a tough job and I wish you the best.
Health, family and friends are most important, so take the time to be there for them. But first take care of yourself.
Hours before a proposal deadline, a partner at a technology services company was about to make a mistake that would cost him his job and his firm lots of money.
As he was going through the resource allocation plan, instead of padding the estimates by a factor of two, the partner ended up dividing the estimates in half — a mistake of omission that managed to go unnoticed. It cost millions of dollars and ultimately, for the partner, their job.
When it comes down to people making decisions on capital allocation, it inevitably becomes “how long will it take?” and “how much will it cost?”
A little abstraction in between is story points → it is an abstraction that is not time, but a fibonacci series that can show s, m, l and xl in terms of effort.
The key rule for story points is to compare teams against themselves, but comparing ourselves to others is a problem most people have but fail to acknowledge. Another reality as an early stage entrepreneur is that many teams are changing quite often. 🤷♂️
When someone asks me how long something will take, here is what I think about:
Most people will answer the “how long will it take” question with 🤷♂️.
To avoid looking stupid, one way is to pad our estimates. Then (maybe) also schedule a lot of meetings, touch-points etc. in case anyone calls us out on it.
See below for an illustration of what just three levels of management in estimation adds, regardless of the methodology being applied.
Software estimators regularly pad their efforts by more than 40% and the number only grows with the size of the organization.
In this post, I’ve tried to understand how story points work and if they are a better alternative to time based estimates with a confidence level.
I struggle to understand why people are die-hards of a software development methodology. Each has principles for high performing teams that can be used for the team & project situation
We’ve seen the rise of agile and scrum, and the gradual farewell to waterfall for good reasons to make way for sprint/agile. Along with the sprint/agile/scrum/kanban/xp processes has also arrived the idea of story points. Story points are not well understood by most people who are planning or doing the work.
Estimates and Sizing in the Real World
I asked about 50 people I trust working in senior software development positions, who shared the following choice quotes:
It works for people at mature organizations:
So naturally, I asked why they moved back and got the following answer:
Abstracting away the complexity with story points
There is value in abstracting away the complexity of a software development process. In a very large company, it also makes it harder to get called out for not doing anything useful.
So here is how the story points abstraction may have been formed, according to the guy who likely invented story points →
days → ideal days (when people leave you alone to work) → ideal days (which became confusing) → story points
Story points with distributed teams
Story points do not work well for:
Teams whose members are geographically dispersed or part-time: In Scrum, developers should have close and ongoing interaction, ideally working together in the same space most of the time. While recent improvements in technology have reduced the impact of these barriers (e.g., being able to collaborate on a digital whiteboard), the Agile manifesto asserts that the best communication is face to face.
Teams whose members have very specialized skills: In Scrum, developers should have T-shaped skills, allowing them to work on tasks outside of their specialization. This can be encouraged by good Scrum leadership. While team members with very specific skills can and do contribute well, they should be encouraged to learn more about and collaborate with other disciplines.
Products with many external dependencies: In Scrum, dividing product development into short sprints requires careful planning; external dependencies, such as user acceptance testing or coordination with other teams, can lead to delays and the failure of individual sprints.
Products that are mature or legacy or with regulated quality control: In Scrum, product increments should be fully developed and tested in a single sprint; products that need large amounts of regression testing or safety testing (e.g., medical devices or vehicle control) for each release are less suited to short sprints than to longer waterfall releases.
For large companies managing an army of engineers, kanban & story points work well. Also abstracts away the need for multiple layers of management. For small teams, we just want to make bets on what to make with a confidence level and expected ROI.
For small teams that want flexibility while getting things done on time, they are better off using time estimates with a confidence level in a scrum way. Here is what I’ve been doing more often to get this done:
Structure – set clear time constraint e.g. six week flights
Shaping – estimate time & confidence level per story e.g. 2 days at 70% confidence
Shaping – identify the value to be created
Betting table – allocate resources based on risk appetite
Story points are not well understood when applied in real-life situations. Time is the scarce commodity, and in my experience, probabilistic estimates and actuals of time are better.
If you’d like some help with it I’m happy to share my experience & templates, etc. for how to communicate the above items to your teams.
As we move towards distributed teams, and contract workers, the cost of mistakes & padding will increase further. For high performing teams, it does not matter. Its large organizations and understaffed teams where padding hurts the most.
Do you have any experience with the above, drop a line with what you would do if you were the CEO of your small or large company.
When I was pitching my startup to investors, a common question was, “what if the FAANG (Facebook, Amazon, Apple, Netflix, Google) enter your industry?”. Such questions were often asked by rookie venture capitalists, but we diligently pleaded why ours was an unlikely target market for the tech giant.
I had not heard this what-if question in my time consulting large enterprises, and it appeared they operated on a belief that a big tech company would never succeed in their industry.
As “software eats the world” and more industries are disrupted, this question is becoming a priority area for leaders of incumbents and challengers in many industries. “Software is eating the world” is no longer a headline from Netscape founder Marc Andreessen for what is to come, but an accurate depiction of an on-going global transition.
Leading this change are dominant technology companies with two distinct qualities.
Marketplaces (multi-sided markets) which enable these companies to leapfrog the incumbents in any industry.
Advancements of dominant tech players using network effects and market places, are leading to cross-industry competition. This is leading to a race on personalization, privacy, and emergence of data as the new currency.
We also share some ideas on how incumbents can innovate to disrupt their industry before they are disrupted.
The game has changed. Fast food restaurants don’t fear McDonalds. Instead, they are afraid of Uber Eats. Netflix closely monitors HBO & Disney, but it’s Fortnite that keeps them awake at night. Big traditional banks are also facing the FAANG financial revolution.
The dominant tech platforms have effectively started serving existing industry players with products like AWS, advertising, and developer tools that drive a large portion of recurring costs for new companies & products.
A shift is also underway to pick up more substantial parts of Enterprise operating expenses, e.g., Microsoft and Amazon were the big contenders for the US Jedi $10B Cloud Modernization contract, which was ultimately won by Microsoft.
Here are some of the areas of interest for dominant technology companies:
WHAT HAPPENS TO THE CONSUMERS?
A common question at Board Meetings from our clients often is “What happens to our consumers?”, but the reality is most companies have limited interaction with their consumers on a regular basis. This has enabled tech players to win and often monopolize their target industry with a highly personalized, technology-enabled experience.
Consider this example: from a consumer perspective, I would be quite pleased with the experience if Google provided my car insurance since they already know my routines, travel, transport modes, plans, and driving habits thanks to Google Maps, Gmail & Calendar.
Therefore, they could offer me a tailored insurance plan with simple terms and conditions, plus a usage-based pricing plan, and coverage all over the world instead of a specific state/country. It would use machine learning to help me become a better driver & avoid unsafe driving conditions, e.g., too tired to drive or hazardous road conditions (suggest to work from home).
Compare this with the broken experience of the current insurance provider, where I only contact them when I need to renew/have a problem. It would obviously be better to have a self serve and personalized option.
From a big tech company point of view, FAANG find fragmented industries with low customer satisfaction (Net Promoter Score) and consolidate (roll them up) with a well-designed software/abstraction layer that takes complexity away, and passes on some value back to the consumer.
An interesting example here is the Apple Credit Card was launched at a time when traditional credit card companies were busy ‘innovating’ a commodity product with points & interest rates.
Instead, what Apple offered as a product was privacy, customer service, together with better rewards and rates. They also added one more thing, a way towards responsible spending, which fundamentally alters the traditional credit card model.
WHAT CAN TRADITIONAL COMPANIES DO?
In response to such threats, many traditional companies have created Innovation as an agenda item and organize themselves to work with startups.
This innovation effort is typically led by a ‘sneaker & slides’ executive (someone who can make fancy slides about internet 4.0, AI, blockchain disruption, etc. and increase their budget without anything tangible to show for it). A good signal for them is a disproportionately high number of buzzwords/minute. These people are most often found at conferences, wasting precious time of early-stage companies.
These labs scour the market for interesting ideas and startups who can help them compete with the giants.
In most cases, innovation teams create less value than they capture for a variety of reasons, including board focus, ability to recruit builders, scaling prematurely, and focusing on things other than customers.
In my experience, incumbents are better off funding spin-outs as sole investors, since that better simulates the real-world founder pressures on the “executive-founders”.
While large companies focus on optimal organizational design for innovation, software is still eating the world, and executives in charge at large companies should be worried. There are good reasons for being worried. Consumers are generating more and more data, and demand for more personalized products is on the rise. There is also an increasing amount of personal data on the internet, allowing advertising engines & companies that already have consumers data to predict what they are doing to be doing next.
DATA IS THE NEW CURRENCY
There has been good work on privacy, starting with Europe and GDPR. Large tech companies have quickly become the guardians of privacy. Because it benefits them since they already collect data, and these rules make it harder for someone to challenge their market dominance.
For an internet that was designed to be open, we are all part of one walled garden or another, which we have to pay increasing amounts for as we generate more data.
As this market plays out, there are clear winners in the form of FAANG and their associated entities. They have a great business strategy and have made investments in many areas directly (Google – Waymo) and indirectly (Uber) to ensure upside as we go through this next phase of technological revolution.
New entrants in markets are growing rapidly, and companies that can drive a little bit of value can grow at 3-4x the industry average. They deliver a much better solution to a long-standing problem, e.g., how to make driving safer, to reduce risk, and claims paid out. Often incumbents who build innovation teams do so with mostly limited success or have defaulted to an inorganic growth strategy buying what works and then attempting to bolt it on to their existing products.
INNOVATE WITH SENTIANCE
Sentiance is a data science and behavior change company. We help traditional industry players & challengers level the playing field by giving them access to leading machine learning technology, ethical AI-generated context, and behavior change techniques.
These companies can make the most out of their data securely and effectively, deliver services with personalized experiences while protecting users’ privacy, which consumers have now come to expect for “free” from the dominant technology platforms.
We help our clients with a methodology that combines strategy, product discovery, innovation, and monetization in an ethical and sustainable manner, delivering value to all players involved.
Sentiance partners with big companies who are thinking and acting ahead, and also those who are struggling to keep pace with rapid innovation from startups.
The ultimate innovation goal is for incumbents to disrupt themselves before someone else. By giving their customers personalized services, companies become relevant to their customers. Respecting data protection and user privacy builds trust between these same two parties.
For any solution to work, it has to ensure that incentives are aligned across the stakeholders. For example, it must solve a real problem, and show quickly that we can deliver a much better solution to that problem using the combined talents of new tech and existing distribution.
It also must demonstrate that new technology and existing machinery can drive value together and get the right data/learnings in quickly to ensure that the project wins in the competition for resources.
For a large company, they need to do the following to win.
DELIVER VALUE FOR OUR CLIENTS
Sentiance clients are always in control of their data and get hyper-personalized services.
We’ve worked with several large companies in the past years, and we have the skills of a top strategy consulting firm to help drive the right insights and focus areas – without having to sell time.
In Sentiance, we also have a skilled product discovery team – without having to shoe-horn a specific product into the solution. Our teams help arrange the necessary ingredients for the best solutions for solving clients’ challenges.
We have the skills of a roll-out team – to help integrate and scale with existing systems – while keeping skin in the game as we get paid when our clients make money.
Below is the flow of how we deliver value for our clients. At the core of our work is our desire to find the truth about customers’ needs, and magically deliver that as fast as possible.
Let’s innovate, and build something amazing together!