“Data is the new oil.”

Unless you have been living under a rock for the last 15 years, you have probably heard the phrase “data is the new oil.” It was 2006 when Clive Humby, a mathematician and entrepreneur originally coined the phrase – and it has been echoed by thousands ever since. 

Clive Humby probably never expected this phrase to make such a lasting impression but it really was the perfect comparison. Oil and data have a lot in common.

Look around you, in some way or another, oil has played a crucial role in the existence of everything around you. Many everyday items are a product of oil, they are manufactured in a factory powered by oil, then transported by vessels which are also powered by, you guessed it, oil. 

Today, data is making just as much of an impact. Data is used in a multitude of ways that influence decision making in government, public policy, healthcare, science, and of course business.

Society relies on data and oil to make things go. For that reason they are considered valuable commodities – that many governments, policy makers and businesses are desperately trying to get their hands on. Data and oil are power. Without them, making things happen can be challenging. 

Most importantly, when we drill for oil or mine for data, to do so successfully requires some knowledge and the right tools. Otherwise it could be a colossal waste of time and resources. To drill for oil requires knowledge of where the oil might be and how to extract it. It requires machinery, and man power. Once the oil has been extracted, it needs to be refined so it can be used in its full potential. 

The same goes for data. There must be knowledge of where to collect data and how to extract it. It requires specific technology and manpower. Then once the data has been collected, it needs to be refined so it can be used to its full potential. 

Unlike drilling for oil, you won’t break your back trying to collect data. But it does require some effort. Collecting data is the first step in answering those valuable questions before we can refine it and get its true value. 

Here are some best practices for collecting data:

Have a robust internal system:

Collecting data in-house is the best practice when solving internal business challenges or identifying opportunities. To collect data in-house means that information is being recorded and stored internally on a consistent basis. Typically data is collected and stored in a customer relationship manager (CRM). And if your team is using your CRM, you probably have a pretty decent set of data to get insights from. If they aren’t using the CRM to its full potential – you should really consider it. Having some data, even if it is incomplete, is better than having no data at all. 

If you are not sure what to collect, it is still a great practice to capture as much data as you can. It’s never possible to know exactly what’s going to be of value in future. So collecting data in a structured format that can be translated into information and insights down the road and can create new opportunities in the future. And the good news is that data storage is cheap these days, so holding onto that data isn’t going to make much of a dent in your budget. 

Buy data from third parties:  

There are times when a company is unable to collect their own data because they could be a start-up, a company looking to enter a new market, or there is a major gap realized in the current set of data. Instead of starting from scratch, buying data from third parties is a good option. There are plenty of  data sets out there that third party data brokers have carefully taken the time to put together. They have a wide variety of data sets that are usually made up of data that is challenging to collect, data that is challenging to access, or very large sets of data – that could take decades to collect.

If you aren’t sure where to start, Quandl and Explorium are reputable data brokers with plenty of data to go around. 

Use third parties to buy training data: .

If you are looking to partake in the AI and Machine Learning boom and want to take on a project, the algorithms are going to require some training data to learn from. If there is a shortage of training data available, third parties are also a good solution for this as well. Third parties can provide data sets a lot faster and efficiently rather than trying to put together a set of training data

There are many options for training data, check out Hive, Scale, NTT Data, or Weights and Biases.

Use third parties to collect training data:

There are times where third party data brokers don’t have the exact set of data needed to answer that million dollar question. When specific data is needed but maybe your team doesn’t have the manpower, tools, or time to collect data there are third parties that can collect that data for you. Once again, long behold the third party data collectors. One of the most popular examples of this is Amazon Mechanical Turk. A platform that individuals from around the world collect data based on the instructions they have been provided with. 

Scrape data off the internet:

As everyone is aware — there is a lot of data on the world wide web, readily available at the tips of our fingers. This practice does require some expertise to create specific tools. But once that has been handled, very specific data can easily be scraped off the internet, parsed and used accordingly. Please be respectful of the terms and conditions of where you scrape data from though! Only do it if you allowed to do.

For example, something that our team has been successful at while scraping data off the internet for lead generation. Instead of sales reps spending hours prospecting qualified leads, our team created a tool that scrapes data off service providers websites such as lawyers and accountants to generate leads. The information was parsed and then put directly into the CRM to be accessed by the sales team. With the right expertise, a tool can be created to collect data on anything and be used to answer that million dollar question.

Ready, set, mine.

The top two reasons that businesses don’t collect data is because it appears to be either too intimidating or time consuming. What this really comes down to is not seeing value and the potential prosperity that could be uncovered. Data drives decision making and uncovers new opportunities. If data collection isn’t a priority, opportunities and insights are being left undiscovered, while challenges are being left unaddressed – both now and in the future. Which can equate to hundreds of thousands of dollars slipping through your fingers.

Collecting data is the easiest part of the process, especially with a little bit of guidance. Refining it is where things can start to become a bit trickier. Luckily, there are data experts out there who can help you uncover the true value of your data.

Plus ça Change, Plus C’est La Même Chose

Jean-Baptiste Alphonse Karr

In case you don’t speak french (or you forgot almost everything you learned in highschool – looking at you Canadians!). The timeless proverb; “the more things change, the more they stay the same” implies that no matter what turbulent changes we experience, the status quo is reinforced. 

Our first thought jumped to sales operations. Sales ops has come a long way over the last few decades. Before, when many of us thought about sales the first thing that popped into our head was a sleazy, used car salesman, trying to nickel and dime you at every corner. Or the guy in the fedora, going door to door, selling the next best vacuum or tupperware set. Heck, even over the last year we have seen a major shift in sales operations from in-person to almost exclusively meeting our prospects over Zoom. 

Our point is, the sales process has changed a lot over time. And regardless of all the trends and changes in the market, sales operations has always been about selling better, selling faster and selling more. This is a capitalist market-driven economy after all. 

Sales Ops in 2021

It is fair to say that sales has become more difficult over the past few decades. 

Information is more widely available. Competition is stiffer. And for that reason, the modern customer has higher expectations and there must be a sense of trust before a purchase is made. Creating strong honest relationships is crucial to driving sales in the modern day. 

We are also living through the 4th technological revolution. Technology has swept through sales operations and has streamlined many of our day to day processes. Seems like there is a service or a tool for anything you could ever imagine. Then you blink and somehow, somebody has created another tool to make your business even more efficient. 

Despite all the amazing software services and solutions out there, many sales teams are still living in the stone age. Otherwise known as… superstar culture. 

Superstar Culture Sales

Is highly individualistic. It relies on a couple people to carry the team to the sales quota. Energy and resources are poured into the few sales wizards that seem to hold the world on their shoulders. Meanwhile, the rest of the sales team is not being given the support needed to improve their processes.

This culture is bad for two reasons. Firstly, sales superstars are not common. They know their worth, and if they left the company for a better offer, you better pray for another one. 

Secondly, when focus is on the sales wizards, the rest of the sales team is left in the shadows. They are not getting the coaching or mentoring they need to develop into becoming the next up and coming sales wizard. They also know their worth and if work culture is lacking – they will be gone too. And nobody wants a churn and burn culture. 

Data Science Technology Prevents Superstar Culture.

One of the biggest tech changes in recent times (that we would argue is here to stay) is the integration of data science technology into the sales process. This changes decision making entirely. Instead of making decisions based on observation and opinion. We can much more confidently make decisions based on data and numbers. We are not forced to rely on a couple sales superstars to hold things together.

Data science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. 

By taking a data science approach we are choosing a science-driven culture.

Science-driven culture prioritizes:

1. Technology is used to make our day to day processes more efficient. It is also a crucial tool to collect the necessary data to make informed decisions.
2. Processes are what exactly your team is doing to move opportunities down the sales funnel. By prioritizing processes onus is being put on the functionality of the sales funnel steps rather than the individual. When a problem is identified, we look to how we can fix the process, not the person. 
3. Teamwork is crucial to science driven culture. All processes need to be documented and shared within the organization. When mistakes are made or insights for best practices are discovered they are not to be kept as a secret by the individual. Instead they should be shared to benefit everyone and the organization as a whole. 
4. Skills are acquired when we learn from our mistakes and successes. This is an ongoing process. When we continuously collect data and get feedback we are able to learn and improve in the places we are struggling most. 

Science-driven culture is about continuous feedback, learning and making decisions based on numbers and data. It is about fixing weak processes, not fixing people. 

Why Data Science Works

Data science relies on numbers. Not opinion. Not intuition. But cold, hard, calculable, numbers. 

Metrics must be measurable and objective. If your metrics are subjective, based on observation and personal experience – consider them an opinion. And opinions with all due respect are not important and can’t guarantee results. Without numbers and data your metrics are meaningless and left open to debate. 

Let’s apply the scientific method to the sales funnel using a subjective methodology:

1. Observation: We aren’t hitting our sales quota 🙁
2. Question: Why aren’t we hitting our sales quota?
3. Hypothesis: Hmm, well looks like the sales reps aren’t making enough calls. 
4. Prediction: If the sales reps make more calls we will make more sales. 
5. Tests: *sales reps make more calls*
6. Results: Still not hitting the sales quota.
7. Refine: Well, that didn’t work. Maybe it’s ______. (and repeat).

Using a subjective methodology (aka our intuition), we tend to make an observation, make a hypothesis, and test it.  And rinse and repeat steps 3-7 when your first hypothesis doesn’t turn out to be the problem. Repeating these steps is a massive waste of time and resources. 

However, when we use data science and recognize there is a performance gap somewhere in the funnel. The problem can be easily pinpointed by just looking at the numbers. No need to play the guessing game. From there feedback can be provided and a specific solution can be prescribed that is geared towards that exact problem in the sales funnel. This means a much faster turnaround time when solving issues. Rather than having to repeat steps 3-7 until you are just about ready to give up and try to solve the next problem.

It is beneficial at both an individual and organizational level. From an individualistic standpoint, we can analyze where a performer is successful and where they are struggling. This feedback is personalized and can help the individual to improve where they need it.

On a larger scale, this data can be used by the organization to make a more efficient sales process and provides valuable insights for the next-best action with future customers. 

Got the Data?

Most organizations, once they have reached a certain size, have a formal sales process in place and are collecting data. This ensures that they can continue to scale. A typical sales report breaks down the basic key performance indicators (KPI). Number of calls/emails, conversion rate, opportunities lost, profit margin, customer acquisition costs and so on. Not to say these are bad indicators. They are necessary for every business. The problem is they just don’t tell the whole story. 

KPI’s tend to be high level. They leave out the finer metrics that can give us a more accurate picture of what exactly is going on. It is important to know your industry and know what KPI’s are relevant to the problem you are trying to solve. Furthermore, if you are looking to take a science based approach in sales ops, it is crucial to have somebody who knows how to ask the right questions, can extract the necessary data and come up with meaningful solutions.

Tune in next week,  We’ll be following up with a post on “How to Collect Meaningful Data”

Over the last decade we have watched Jeff Bezos climb to the top of the ladder to become the richest man on the planet. As founder and CEO of Amazon – it is clear that his leadership made an impact on the success of the multi-billion dollar corporation that started from his garage in Bellevue, Washington in 1995. 

There is one aspect of Bezos’ leadership, more specifically team building that I would like to focus on, as I believe it is one of the cornerstones of his success. It is known as the two-pizza rule. Simply put, if you cannot feed your team with two pizzas then it is too big. This number sits at a maximum of 10 highly effective team members. 

So, the secret to being a successful team is a small team. Let’s break down the benefits of keeping your team small and pizza bill low:

1. Smaller teams have healthier communication.

In a group of 10 or less, with proper introductions, team members are going to know one another by name. Which ultimately leads tighter knit relationships. Instead of seeing your teammate as employee #2342, they are another human. With their own strengths, weaknesses, fears and aspirations. Having a personal connection with team members fosters a sort of mutual respect. And creates an environment where people are not afraid to share their ideas or make mistakes.

Having a smaller team also leaves more room for open and thoughtful discussion. Team meetings can be run more efficiently with a smaller group of people. Rather than one hundred people chiming in at once. A small team meeting allows team members enough time to formulate and present their ideas in-depth. All while leaving room for feedback.

2. Smaller teams hold themselves accountable.

There is a well known theory called the “Ringelmann effect.” It was realized by French agricultural engineer Maximilien Ringelmann in 1913, it still holds true today. Ringelmann created a study where he had volunteers pull on a rope. He observed that the more people that pulled on the rope resulted in the individual putting forth less effort. 

He concluded that in a larger group, there is more anonymity among the team. It is much harder to tell who is pulling the rope and who isn’t. But in a smaller group, it becomes quite clear who isn’t pulling their fair share. 

This means, you don’t need a huge team of people to pull the rope per say. Instead you need a strong team that is going to pull their fair share of the rope, hold each other accountable and get you across that finish line. 

3. Smaller teams are nimble.

The only thing in this world that is constant is of course, change. In this fast paced world it is crucial that teams are agile and can respond to changes both quickly and efficiently. In a large team, decision making is often a long, drawn out and bureaucratic process. A slow response to external changes can result in a variety of losses for an organization. And in some cases organizations will end up folding – unable to keep up. 

On the other hand, a small, tight knit team can quickly come up with strategic responses to changes in their market and environment. With fewer decision makers, the decision making process is dramatically reduced and strategy can be implemented at a much faster rate. 

4. Smaller teams cost less money.

This almost goes without saying. Of course, having ten team members is going to cost less than fifty team members. Less people equals less resources spent. Yes, this even applies when you have some real unicorns on your team that might cost a little (or a lot) more than your average employee. 

On that note, it is worth investing more in a small team that is multifaceted and offers deep expertise in a variety of areas. 

5. Smaller teams are more innovative.

When seats on the team are limited, you have to be picker with who makes the cut. Having a small, focused team, full of multifaceted experts (in a variety of fields) can make it much easier to come up with creative solutions to some of the most pressing challenges businesses face today. Take a look back in history, some of the most notable inventors did not just specialize in one narrow discipline, rather they had a wide range of skills. 

Lets use Benjamin Franklin as an example. He wore many hats. He was one of the founding fathers of the United States, a writer, a scientist (that most notable made many major contributions towards electricity research), a businessman, an author and printer, and made many civic contributions to his community, by founding a fire hall, academy, hospital, and an insurance company. This paragraph hardly does any justice for the guy. But my point is, finding a few people with deep skills and curiosity will drive innovation at a much faster rate than one hundred average joes would.

One example that comes to mind when considering team size is a 2015 interview between Ried Hoffman, host of Masters of Scale, and Eric Schmidt, former CEO of Google. Schmidt recalls his introduction to the Google founders team and their strategy to oversee growth within the company. 

In one year, Google had quadrupled their number of employees. Eric observes that exponential growth is not always a positive attribute. Rather, the team size should be scalable to the product or service being offered. Growth should be controlled and bringing on new team members should be strategic. 

To control growth, Eric Schmidt did a full review on hiring and the hiring process. Each employee would have something to offer and was brought on to contribute to projects in a specific way. This would reduce the number of what he referred to as “glue people.” Employees that were loyal and assisted in activities but were not necessary to the teams success.

The benefits to having a small team are clear, but what does it actually take to make your small team run smoothly, efficiently and meet deadlines? Here are three tips on building a small team for success.

• Hire wisely. Just because your team is smaller than ten, does not automatically make it an all-star team. It is important to choose your teammates carefully when there are limited seats available. You want to choose people that offer an array of skills, with a deep understanding of both the business and technical side of things. 

• Create a thriving work culture and environment. This is important to establish early on when creating your team. Team culture is created whether intended or not. And who you bring onto the team will help define the culture. Building strong relationships among team members in the early stages creates a space that team members can be open and vulnerable with one another. A space that it is okay to brainstorm and make mistakes will lead to innovative solutions faster. 

• Outsource projects. No matter how fantastic your team of ten really is, you won’t be able to take on the entire world. Recognize when something that is out of your team’s scope or that a group of experts might be able to do better. Then hire the right consultants to take care of it. Firstly, this ensures that the job is going to get done right the first time. Secondly, this prevents your team from becoming overwhelmed by work load. Instead they can focus their attention towards challenges that they are skilled to fix. 

This is not to say that there is no place for large teams, but a business must carefully evaluate the scale and resources available when choosing the right size team for the project. Larger teams tend to work best when looking to maintain or advance and develop in their given industry. Whereas smaller teams are best in situations where innovators are trying to keep up with the latest trends or looking to disrupt the status quo – which is what most of us start-ups would like to do, isn’t it?

“We are drowning in information and starving for knowledge.” — John Naisbitt.

The Era of Big Data

We have entered the era of big data. For example, there are about 60 trillion web pages; 300 hours of video are uploaded to YouTube every minute, amounting to 10 years of content every day; the genomes of 10000s of people, each of which has a length of 3.8 × 109 base pairs, have been sequenced by various labs; Walmart handles more than 1M transactions per hour and has databases containing more than 2.5 petabytes (2.5 × 1015) of information [1]; We are currently creating around five exabytes a day roughly equivalent to 500 million songs — the amount of data available today is giving machines the possibility to become super-intelligent.

What is ML?

This deluge of data calls for automated methods of data analysis, which is what machine learning provides. In particular, machine learning (ML) is defined as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data) [2]. A more technical definition of ML is “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” [3].

AI vs ML?

It’s worth noticing that often ML is used with artificial intelligence interchangeably. Although artificial intelligence is technically broader than ML and they are different, for the purpose of this note, we treat them the same.

Why Now?

Now, you might ask, how old is this stuff and why is it getting a lot of attention now? Artificial intelligence was founded as an academic discipline in 1956. It because most of ML methods work well when you have a large amount of data (to be precise it really depends on the problem you are trying to solve) and as noted we have entered into an era that there is no shortage of data, yay! Another interesting thing recently happed; our computers got way stronger and faster than before. Analyzing and extracting information from an enormous amount of data is not trivial. It needs a lot of computational power and the introduction of graphics processing units (GPUs), which were developed to speed up the graphics in our everyday computers and especially in the gaming world, to ML world changed the game.

What are Some of the Applications of ML?

What are some of the applications of AI/ML? Good question. Although you might not notice it, ML already is a big part of our lives. Amazon makes its recommendations based on a set of ML algorithms called “Recommender systems” specifically a hybrid of “Collaborative filtering” and “Content-based filtering”. Google uses ML to detect spam emails. Facebook uses ML to show you the content that you would find interesting. Tesla is using AI/ML to make self-driving cars. Intelligence services are using ML to detect criminals in crowds and …

ML and AI are already here. The question is how to align your business to benefit from it rather than getting killed by it.

1. “Cukier K., The Economist, Data, data everywhere: A special report on managing information, 2010, February 25, Retrieved from http://www.economist. com/node/15557443”
2. “Kevin P. Murphy , Machine Learning: A Probabilistic Perspective Hardcover — Aug 24 2012”
3. “Kaplan, A. and M. Haenlein (2019) Siri, Siri in my Hand, who’s the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence, Business Horizons, 62(1)”.

The current coronavirus pandemic situation has created a sudden change in our everyday lives that was unforeseen just a few months ago. As individuals adjust to the “new normal,” businesses should also take a moment to reassess their situation.

Suddenly, the market is upside down, your team is working remotely, and a good portion of your team probably doesn’t have as much on their plate. Although the market is showing signs of coming back, it might take many months to a few years. This uncertainty presents an opportunity to concentrate on the “fundamental” work that’s always in the “to do” bracket. Tasks that were a lower priority with everyone focused on the daily business operations is now perfect for getting done. It’s time to position your company to leverage the current situation so that your company is super competitive in the “new normal.” Well, let me try to explain:

Utilization = morale

We know that excellent staff morale is key to productivity and utilization. I would argue that those who are most engaged are those that understand the reasons for what they are doing. If they are aligned with the company’s vision and understand their roles and possible impact, they will not need your constant motivation. Nothing creates a downward spiral more than underutilization of staff already suffering from the “Mushroom Syndrome.” Now is the perfect time to create alignment and focus. Typically, teams are too busy when things are going well

I’m guilty of this; you’re guilty of this. Typically, when things are healthy, and the company is doing well, everyone is laser-focused on their commitments and targets. We are guilty of working ‘in the business’ rather than ‘on the business.’

The market has changed, and the world is a different place–get ready for it

Some of the effects of COVID-19 are with us for a long time, and some are likely permanent. Now is the time to get ready for dynamic changes that are new to everyone. Markets after the 2009 crash were different. Similarly, the markets after September 11 also changed. The changes after the two events were different too! What do you need to be ready? Data can tell you a lot about this. It would help if you readjusted some of those fundamental assumptions which worked previously. Do you know which ones they are?

New technology initiatives take time

You have got your teams focused on the next 12 months of work; now you need to start thinking of the future. Once you have set the company’s direction, you need to gather intelligence to determine the criteria for success and failure. To quickly identify where the market is headed and stay on the leading edge. The best way of creating a picture is by analyzing many data. That’s where technology can help identify new trends and emerging patterns, but the intelligence lies in understanding data. Implementing technologies like AI and ML takes time. More than that, gathering data takes even longer, and it requires a lot of trials and error testing. Experiment with it while your business is not at full capacity. Now is the best time for when the market comes back, you won’t have the time.

So what?

OK, you’re convinced that it is a good time to do some fundamental work. Where to start? Start from where the impact is. Try to avoid shiny objects and only do what matters. I’m going to write about this topic next week so please stay tuned! Before that let’s look at some other bullet points.

Measure twice and cut once

You’ve heard the saying “Measure twice and cut once,” well, this also applies to businesses. By that, I mean companies should spend time planning their mid to long-term course before committing the workforce to a particular path. Moreover, that same workforce should be part of the planning process, so they have a stake in the outcome.

During this downtime, it’s your job, as the boss, to create the company vision for the 10–5–1 year horizons. You are responsible for the big picture. Where do you see the company (in terms of revenue, size, product development, market capture, and any other target you have been dreaming about) in 12 months from now, then 5 years, and finally 10 years from now. Identify the targets as accurately as you can and see if your staff think it’s possible. If they like what they see, they’ll provide most of the solutions to achieving the ‘want.’

Try filling in the details of your targets in the following table style:

Does everyone working for you know what the company does? Why it exists? You may think those questions are trivial, but to get alignment across the board, everyone must understand what service(s) the company provides, and why it exists. Now is your chance to consider how the current Covid-19 climate can be an opportunity to beat the competition.

In the following diagram, I created a mind map to identify the business of a company. It needs to be simple enough for anyone to understand.

What YOUR landscape looks like?

Start by identifying your current place in the market and then define how new opportunities are becoming apparent. Let your staff tell you what they think.

Along those lines, I created the following drawing for a company to highlight its position in the market. It shows their competitors, suppliers, partners, customers, and other channels. Again, a mind map is the easiest way for everyone to follow:

More efficient and last longer

Use your best and brightest employees to help set the path forward. Once they fully understand what the company does, why it exists, and where it needs to go, they’re motivated to succeed.

Task your leaders with creating milestones to achieve the goals for the next 12 months, let them demonstrate their preparedness to meet those targets. When that occurs, your job is to shepherd them, provide the resources, and trust them.

Finally, it’s very important to find us on LinkedIn and let us know what you think! Always happy to connect.