Analytics, Strategy, and Agriculture

Data Doesn’t Have To Be Big To Be Useful

tl;dr Large amounts of data isn’t always useful, so make sure the data you’re collecting and analyzing has a measurable business impact along the way and grow to “big data” initiatives over time as you prove the benefit of your data initiatives along the way vs starting with a company-wide “collect first, analyze later” effort or jumping into a “Big Data” initiative and end up collecting too much and producing too little value to the business. Don’t focus on what’s interesting to analyze, focus on what’s going to drive profitability and positively impact stakeholders across the company. Less data sometimes can be better data, when it drives a more meaningful business impact with less cost and complexity along the way.


I often hear the quote “Data is the New Oil” given the growth of data initiatives across companies to mine, and refine data to be leveraged across companies around the world. However, like oil, data has to be refined and put to use in the right way in order to really be effective, just as oil left collecting in a tank isn’t useful and will go bad over time. If it’s hard to put all your data to use though, then “Big Data” initiatives can make it even harder. If you look up the definition of “Big Data” on Wikipedia, the first sentence reads “Big Data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software”.

As companies evolve to be more data-driven, the volumes of data that get collected across various types of data grow as well. From the early days of collecting transaction data in structured tables to now grabbing everything from log files to “unstructured data” in the form of images, videos, and the like, the state of the art around data analysis is pushing toward using advanced techniques like machine learning to analyze larger and larger data sets that are less structured and more unwieldy as the need to grab more data from a variety of different sources grows.

An article on statista.com regarding “Worldwide Data Created” states that “The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes.”

As someone that’s worked from setting up databases at startups, to managing the analytics team for Alexa worldwide at Amazon, I can say in my experience that more data doesn’t always translate to more insight and impact. There’s a lot of buzz out there about how data is the new oil, becoming an asset class that aids in growing the valuation of a company, but like oil not all of it is created equally and not all of it is certainly useful.

What does it take to make data useful then? In my experience, it needs to start with the problem you’re trying to solve and the questions you’re looking to answer. Data should be treated as the means to an end around business effectiveness, and not an end in itself. A lot of tech companies stand to benefit from more data being analyzed by more people inside of a company (the global big data market set to generate $103bn by 2027), but a lot of companies still find themselves lost regardless of how much data they have in front of them.

In a joint effort between Informatica and Capgemini, a white paper was produced in 2016 around “the Big Data Payoff”. In it, Capgemini surveyed 210 executives and found that only 27% of those execs say its company’s Big data initiatives are profitable. 50% of US executives and 39% of European executives said budget constraints were the primary hurdle in turning Big Data into a profitable business asset.

It’s no doubt expensive to collect, store, and analyze large sums of data when you consider hurdles around security, integration challenges, technical talent, data silos, legacy infrastructure, divided company sponsorship, etc.

If instead though, you looked at your data problem the same way an entrepreneur looks at starting a tech company and building a “Minimal Viable Product” or MVP, perhaps it wouldn’t be so difficult to demonstrate the value of data as it grew over time. Taking the concept of an MVP and using it as a “Minimal Viable Dashboard” or perhaps a “Minimal Viable KPI” and developed your data comprehension with the goal of making every data initiative you launch grow from the previous effort and ensuring that the end result is demonstrating impact back to the business. Even for companies with mature BI practices, centralized data teams, and data scientists developing complex forecasting tools, the real usefulness can often get lost in the complexity. Imagine how many dashboards get built with the intention of being used, but to be passed over by the end-users after the first use never to be opened again.

I think if companies started by making descriptive analytics as useful as possible, ensuring that the most basic use cases around data are being fully utilized and that everyone in the org is benefiting from the most basic use of data collection, modeling, and analysis then it wouldn’t be so tricky to justify the budget to go after more advanced initiatives involving increasingly more complex data sets.

The temptation to keep up with competitors, keep your quants interested and leverage advanced data capabilities to build a data-driven moat isn’t too unlike all the companies that rushed to build mobile apps in 2007 to try and gain the same type of competitive advantages being first to deliver something compelling on smartphones. Yet, how many apps really moved the needles for companies?

Big Data no doubt has its place, as tech companies demonstrate the power of analyzing increasingly large data sets and the insights those data sets can produce. However, if your company isn’t taking a “crawl, walk, run” approach towards data competency, and really making sure you’re exploiting all the benefits with basic data analysis before working your way up the analytics maturity chain (see https://computd.nl/demystification/4-levels-of-data-maturity/) you’ll not only leave valuable insights on the table but also find yourself spending increasingly more capital for less value while engaging fewer and fewer people across your organization.

At the end of the day, the amount of data you have doesn’t matter so much as the level of impact you have from the data you’re using regardless of the size. As small data sets become increasingly impactful, grow from that initial impact to increase the level of insight, and always measure against the business value you’re creating as you add more data sources to the mix. If the value isn’t there, drop the data sets and/or analysis in question, and find a path to measurable business impact. At Loftus Labs, we like to say that “companies have business problems, not data problems” – that data is the means to solving those problems instead. Perhaps it’s a matter of understanding the problems you need to solve, before diving into the dashboard development.

Here’s some tips to maximizing the usefulness of your data, regardless of the size

1) Analyze what’s being analyzed today. Consider analyzing how often your stakeholders are using what’s developed and develop surveys or personal engagements to make sure people are using what’s being provided effectively (most BI servers will make it easy to track who’s using what and how often). All because it exists doesn’t mean it’s being fully utilized by end-users, so treat your centralized data team as its own internal startup and the “revenue” or measure of success for that team comes in the number of users taking advantage of what you’ve built to date on a regular basis.

2) Make sure someone is asking for what you’re analyzing. If someone isn’t asking for it, then it’s not going to be utilized. I’ve made the mistake of building dashboards I thought would be useful to a department, only to find out that they were busy solving other problems and never actually spent time with what I built. If no one is worried about customer trends at the moment, don’t spend time collecting and analyzing customer trend data. It’s not to say customer trends aren’t important, but the data needs an end-user that cares to spend time with what you’re building in order to generate the business impact. Wait for the need to be raised, before spending the time developing analysis related to that data related to that need if at all possible. Better yet, consider if you even need to start collecting data related to that area if it makes sense. Yes, often times for things like trends you need to have already collected data over time to circle back and analyze it, but that’s also the excuse companies will use to collect and store EVERYTHING on the off chance someone wants to analyze the full corpus over time.

3) Get rid of what’s not tied to a measurable business impact. Don’t be afraid to drop what’s not being used or stop collecting data that isn’t useful. A lean and effective data warehouse driving quantifiable business impact is far more useful at the end of the day than massive data lakes with very little tangible business value. Too much data infrastructure built up around data that’s not entirely useful is going to grow in cost and complexity to maintain. It’s better to ramp down parts of your data organization that isn’t beneficial than try and keep it all going on the off chance less useful parts of your data ecosystem are utilized at some point down the line.

4) Use what’s easy to collect and analyze first, then work your way to bigger and more complex data ecosystems. Doesn’t have to be state of the art to be useful, and it doesn’t have to be big data to drive a business impact. Consider the right tool for the job, and only grow your ecosystem into more data sets, handling more complexity, as what you’ve built today ties to a measurable ROI with the full buy-in of your stakeholders. If everyone can point to the use what you’ve build and managed today can provide, it’s much easier to justify going to the next level in both time and cost.

Top 5 Considerations for IoT Sensors in Farming

Sensor-based data collection in farming using data collection sensors (Referred to as IoT or “Internet of Things” devices) is growing in all types of crops, as more and more AgTech companies release products to track everything from irrigation levels to crop vigor. In previous posts, I’ve talked about the importance of tracking data to make better decisions on the farm.[LINK] Like with any new technology though, it’s sometimes hard to evaluate its effectiveness. 

  • Did that thing I buy really help the bottom line, or was it something else I did? 
  • How much better was it than the one that cost half as much? 

Along with that, determining what to consider up front before fully committing capital to implement and scale the technology can also be tricky.

Based on working with several sensor vendors over the past several years, I’ve come to view every new AgTech solution through a set of five criteria, or considerations.  

1. Concentration & Cost

When thinking about rolling out sensor-based systems across an orchard or field, it’s important to consider what the ideal level of concentration would be and the cost that goes with that. If a weather station is being sold as the means to track data for micro-climates, how many weather stations do I need per acre? If the real benefit comes from one station per acre or block for example, what will the benefit be if I roll the solutions out over time? Will I get value from just one sensor to start? Or is there a minimal number of devices I need to get any kind of meaningful output out of the devices? It’s important to start slow with any new approach towards farming, but knowing the minimal required concentration to be successful, along with tracking the return on investment over time, is important in justifying the upfront time and money. If it takes ten devices to start, how long till I see results that could justify buying the next ten? And what does that benefit really look like at the end of the day? 

2. Connectivity 

Most sensors require either wifi or a cellular signal to transmit data, so it’s important you have the necessary connectivity across the entire area you plan to deploy sensors, both your initial and eventual planned area. It’s important to know upfront if you need additional fixed wireless towers, a mesh wifi network, or sign up for a cellular plan on top of purchasing and deploying the sensors. 

3. Utilization of Data

So you have installed the sensors, and now they’re connected and tracking information. What do you plan on using the data for? Is it mildly interesting information, or something that will really drive impact to your yield? Some people will track steps or calories with a FitBit, but interest will drop off after 4 weeks. It’s important to make sure your implementation won’t suffer the same fate. Thinking through the use cases for what to use the data for is important to maximize the value of what you’re deploying. It’s also important to discuss with the vendor how that data can be made available. 

Oftentimes, there will be a web-based interface with fixed reporting provided, depending on the sensors. However, discussing means to collect the data (ie connecting to the data in an application, and extracting it) is important if you want to perform your own analysis on the data outside of the fixed set of metrics provided. 

Furthermore, combining that data with other types of data you’re collecting is useful to determine the holistic state of a given acre vs having to bounce between multiple systems to get a full picture of what’s going on. 

4. Scalability 

I touched on this earlier in #1, but you want to get a clear idea of what it would take to scale the sensor system across your entire orchard in terms of cost, connectivity, etc. If you find your initial sensors are useful, make sure you have a clear idea on the roadblocks in front of you, if any, to continue buying and installing more sensors across your farm. 

5. Diversification 

Your final consideration involves identifying what different types of sensors you’ll need to capture a full picture of what’s happening. If you are just investing in soil moisture for the time being, will you eventually need a weather station nearby to track evapotranspiration? If you are using sensors to collect / track bug counts, will you also need to track plant vigor & growth to determine the impact of pest infestations? 

It’s certainly not required to track everything at once, but having a good idea on the types of things you want to track will help you assess what sensor combinations would make the most sense. It will also help you understand what vendors either provide multiple sensors or work together seamlessly to help give you more of a complete picture. 

If you’re only tracking one thing today, are you going with an option that’ll easily let you expand into other things later on? Or will you be stuck using / supporting several different point solutions and trying to make sense of the data yourself across all these platforms? APIs become an important ingredient to extract and combine data for a given acre. 

Though there may be additional things to consider when rolling out sensor-based solutions in your farm, these five points will help you get a clear idea of what you’re getting into and how to maximize the value of your investment. 

If you have any questions about sensors, or IoT, you can leave a message for me under the CONTACT ME above and I’d be happy to discuss your specific scenario in greater detail.

The 4 Parts to a Data-Driven Farm

Data collection, and the tools and services that allow it, provide farmers with possibly the greatest technology benefit across a farming operation. Like calorie counting can help folks lose weight by simply gaining better visibility over what they are eating, data collection can reveal where resources such as labor and capital are being spent to better optimize. This allows for cost savings and greater yields as growers move closer to precision agriculture.

Regardless of the type of crop, farming inherently has always been a data-driven operation. The methods used to collect and analyze data, however, have been changing at a rapid pace. As a result, growers have started to go from clipboards and Excel spreadsheets to ‘Internet of Things’ data collection and business intelligence. Row crops have certainly led the way on automated data collection, given the large number of acres per farm compared to other types of crops. Specialty crops, however, have been catching up in the past couple of years with new AgTech companies forming that focus on crops such as wine grapes and tree fruit.

Where To Start

Before jumping into the mess of AgTech vendors though, it’s important to identify the four pieces you’re going to need to modernize data collection and analysis across your agriculture operations. This will help ensure you have a strong foundation from which to build from.

My advice is to start with data analysis as the first step (even though it’s listed at the end of this post) in order to understand what you want to measure. It’s easy to incur a lot of costs quickly if you bite off too much at once when it comes to building up a modern day data ecosystem. Having a clear set of metrics will allow you to tie what you’re measuring to what you’re saving / gaining and get to a return on investment faster. From there, you can add to what you’ve built, and measure more as your use of data grows across the operation.

Part 1) Data Collection

Recently there has been a surge in sensors for farms that can track everything from soil moisture to pest counts. There’s two things to focus on when considering what tools to use for data collection. 

  1. How many sensors and what type are needed for collection.
  2. How “friendly” the sensors are to feeding data to the system you’ve chosen to consolidate and analyze it in. 

Most vendors offer web-based solutions for showing you their data, but it’s only really useful if you’re able to extract it into a central place (such as a data warehouse) because the real value of this data isn’t what’s happening to an acre of crops by itself, but what collectively is happening within that acre. This is important because you will need to pull together individual readings over a given acre to then compare to an output metric such as yield. As for concentration, without sensors the data is likely already being collected today using manual sampling methods. It’s important, then, to consider the ROI from automated data collection at a sufficient concentration. And of course, always start with just a couple sensors to validate the technology will work for you before scaling. If you only have 1 type of sensor though, to track something specific, then the vendor provided solution may end up working fine.

Part 2) Data Consolidation (or ELT)

Once you have your data collection sorted out, and you’ve begun to measure data in an automated way, you’re going to need to get the data into a central spot, such as a data warehouse, in order to put that data to best use. In that case, you’re probably going to need to pick a vendor to help with this. Though there are ways to do it yourself, it requires hiring one or more experienced data engineers / programmers that can build and maintain the system.

For most farming operations, picking an ELT (extract-load-transform) vendor is going to be a better bet, such as FiveTran or Dell Boomi. These tools will allow you to connect to a data source, such as your accounting system or field data collection, and extract / load / transform the data into a central place which then allows you access to all of your raw data across as many systems as you’re able to connect to. This is why, when considering data collection, you need to understand what vendors have capabilities such as an API which is a capability of a software platform that allows customers to connect to the platform and extract (and in some cases write) data.

Part 3) Data Warehousing

Having a central location for your data is the next foundational component. Depending on the volume of data, you may be able to use a standard database such as MySQL or SQL Server. If the volume of data you’re receiving is large enough (500 GB or more to start with) you may need to consider a Columnar based database such as Snowflake or Azure Synapse which is designed for large data sets. When you’re storing a lot of data from sensors across your operation, you are likely going to get larger data volumes sooner than later. The amount of data a sensor puts out varies quite a bit, so be sure and discuss with your vendor before signing up for the sensors. The key though, is to start with smaller data sets that are useful right away vs starting big by storing everything and having to support too much at once. You will be paying for storage monthly, so having too much unused data will just drive up the cost vs data you can put to work to justify what you’re investing (keep in mind though, storage is relatively cheap in the cloud so it takes a lot of data to drive up the cost of storage). 

It’s also recommended to host your data in the cloud, and to get as much of your ecosystem into the cloud as possible. Though you likely have data hosted locally today, which in some cases is required for business continuity, having a plan to get as much into the cloud as possible has a number of benefits. These include automated back-ups, easy scalability (you pay for what you use, and can increase the size of your server as needed), and streamlined pricing along with the convenience of having the services managed by the cloud provider vs handling onsite servers yourself among several other benefits.

Part 4) Data Analysis

Once you are measuring the right information, and then extracting / storing it in a central location, you need to consider what metrics you are going to track. Having ten or so metrics from which to measure your biggest cost drivers (such as labor or parts) means you can get actionable insights faster, versus guessing what might be useful. It will also help you understand what data to collect first, instead of boiling the ocean and gathering all the data at once. Using a business intelligence tool such as Tableau or PowerBI will allow you to point to your data warehouse and begin to construct these metrics to begin building automated reporting.

It’s recommended to work with an experienced IT resource that has worked with these tools in the past to get your reporting up and running, although if you’re technically inclined and on a budget, there are great BI tutorials available online. Once you have these metrics established, you can look at other capabilities under categories such as data science, artificial intelligence, or machine learning. However, that will require specialized expertise to build solutions using these advanced methods. The good news is these capabilities have been growing over the years, so it’s a lot easier to find expertise with these skills than it used to be a couple years ago. And the tools to roll your own advanced analytics system are getting better every day. 

Next Steps

Though this is a very simplified view of a data ecosystem for your farming operation, I hope this provides you with the 30,000 foot view of the pieces essential to a modern data-driven architecture. Understanding where AgTech vendors fit is critical to ensure you don’t have a bunch of isolated (or “silo’ed”) point solutions that you aren’t able to bring together into a single set of metrics.

The next step is to learn more about each, which I’ll be covering in future blog entries. Of course, if you have any questions, feel free to reach out directly or leave a comment.

The Hidden Ingredients of a Beer

When you think of a beer, what ingredients come to mind? Water, a source of starch such as malted barley, yeast, and hops all may be on the list. Different types of beers, of course, all have different types of ingredients along with the many varieties of ingredients such as hops (around 130 varieties exist currently, for brewers to chose from).

Regardless of how it’s made though, there are several hidden ingredients in beer that you might not know about. Recently I’ve had the chance to move back to Yakima, Washington from Seattle after 15some years living and working there. I grew up in eastern Washington, about an hour east in the town of Prosser so it was in a lot of ways me coming home.

What I wasn’t aware of, though, was just how vibrant the culture of hop growing and beer making was in the valley. Did you know Washington grew 77 million pounds of hops last year, bringing in $427 million dollars and its all within driving distance to Yakima? That’s 77% of the total hops grown in the United States, on 75% of the total US hop acreage. Yakima even has shirts available on iheartyakima.com that proudly declares “We Grow Your Beer”.

It was in getting to know the folks at Bale Breaker, a craft brewery here in Yakima with a heritage in hop farming dating back into the early 1900’s that I really got to know craft beer. In so doing, those hidden ingredients that lie within each beer became evident.

So what are those hidden ingredients?

The first hidden ingredient is community

If there’s one thing you can pick up on walking into any tasting room is the close friendships that form between the patrons, and employees of a craft brewery. It’s really community that makes beer something that brings folks together, to share in good times and bad. You don’t have to know a brewer for very long before you come to feel like family. Beer, I’ve learned, is not only is great for bringing friends together, but helping make new friends at the same time.

The second hidden ingredient is love

There’s something special about hearing a brewer describe the beer they make. It’s the passion and pride they have for a job well done, combined with a love for making something that others can enjoy. Not only does a brewer, along with the team that supports them, love what they do but they love seeing people partake and are always willing to share how and what they do to anyone that asks. It’s a love and passion seen rarely in the office place and is evident anytime you encounter someone that works in the craft brew industry.

The third hidden ingredient is legacy

In learning about the history of beer, I discovered that there is early evidence of beer from a 3,900-year-old Sumerian poem honoring Ninkasi, the patron goddess of brewing which contains the oldest surviving beer recipe. Monks further refined the concept of brewing dating back to the 16th century, where people around the world have been refining and experimenting since then. In the Yakima Valley, Leota Mae moved to Yakima in 1932 and began cultivating hops on land still farmed by her family today (some of those Smiths went onto start Bale Breaker located on lot 41 of that very same farm). Multi-generation families grow hops all throughout the Yakima valley, with several more families growing other ingredients that make beer great. It’s a profession chock full of legacy, with more history added with each crop year.

The fourth hidden ingredient is creativity, along with innovation

Though there’s several more “hidden ingredients” I could chose from, the last one I’ll talk about is creativity combined with innovation. There’s nothing that demonstrates rapid prototyping, experimentation, and feedback better than beer. Bale Breaker has an “imagination station” where they are making small batches trying out new kinds of recipes and collaborations each day. They share it via their tasting room for instant feedback and aren’t afraid to totally fall on their faces while cranking out new experiments. The ones that make it, get their own can and sold all across the pacific northwest while the rest are shelved for the time being till they’re brought out to experiment with once again. Some experiments are seasonal, some are collaborations with other folks in the beer industry, and some are just for fun. It’s evident though, in how Bale Breaker goes about inventing new flavors though that they truly understand the importance of innovation and creativity to keep a business thriving.

At the end of the day when you add all this up, it comes down to the fact that beer is more than just the beverage. It’s the hard work, passion, and community that comes together crafting each and every type of beer that makes it greater than the sum of its parts. And that alone, is worth spending time getting to know and enjoy.

So head over to Bale Breaker if you’re ever in Yakima, or any of the other amazing craft breweries nearby. Or if you’re not nearby, you’re no doubt within driving distance to someone’s passion and hard work ready on tap and available to share with you and your friends.

Go smell, sip, and enjoy! And when you do, think about those hidden ingredients that truly make beer more than just a drink.

Fixing America Starts With Small Businesses. Here’s Why

I just read an article written by Joan Williams in Harvard Business Review titled “What So Many People Don’t Get About the Working Class.” She discussed reasons the nation is in the political state it’s in, and the reaction the middle class has had after years of job losses and an overarching sense of being left behind.

The article resonated with me because I grew up in a conservative farming town of 8,000, attended a university known for its agricultural programs, and then went on to work at The Boeing Company for five years. I now live in liberal Seattle. I left Boeing to start a company, then worked in technology and management consulting in cities across the world, and now have started another company.

Having spent half my life on one side of the conversation and half on the other, I see entrepreneurs as the ones standing in the middle. What it takes to start, run, and grow a small business requires a multitude of skills, including but not limited to:

  • Managing teams with empathy and understanding
  • Making strategic hiring decisions
  • Building a compelling story to get buyers in the door
  • Following local policies, demographic shifts, and labor issues
  • Keeping a national perspective when it comes to supply chain issues, taxes, and trend shifts

And so many people are impacted by a small business each day, whether as customers, employees, or founders. From an FAQ article written by SBA.gov in Sept, 2012:

“Small businesses make up: 99.7 percent of U.S. employer firms, 64 percent of net new private-sector jobs, 49.2 percent of private-sector employment, 42.9 percent of private-sector payroll, 46 percent of private-sector output, 43 percent of high-tech employment, 98 percent of firms exporting goods, and 33 percent of exporting value.”

Although this information is almost five years old, the significance of small business hasn’t changed much. From stories of Radiator Springs in the Pixar movie “Cars” to popular books like “Hillbilly Elegy” by J.D. Vance, it’s not hard to find insights into the middle class struggle and see how it ties to small business ownership.

In Dr. Williams’s article, she talks about the dream of owning a business and the scarcity-driven fear of feeling left out as blue-collar workers have found it harder to take care of their families, let alone take risks to get ahead.

So what does this mean for you? As a current or future entrepreneur, you have an opportunity (perhaps obligation?) to reach out in your community and help shape our nation in the coming years. The rest of this article contains some suggestions that will go a long way toward undoing the divisive rhetoric and populism facing all of us today.

1. Entrepreneurial viewpoints are contagious, so get to know someone you don’t agree with

Too often we surround ourselves with perspectives and inputs that support what we already believe. As an entrepreneur, you get even less time to take in what’s going on outside of your business, which means it can be harder to invest in a diversity of perspectives. Yet, finding at least one relationship–an actual person, please–that can help balance out bias means you’ll be able to see where others are coming from more easily.

We all want to be heard and valued. As an entrepreneur, you can reflect that balanced perspective with the business community, employees, and customers you interact with every day. Imagine the impact of treating everyone with respect and common understanding. That can begin with you.

2. Focus on employee ownership

Scarcity, fear, and the instinct to survive drive people to make decisions against their own long-term interests. Many people in the U.S. have been feeling increasingly left out of the American dream. They feel left behind and without opportunity.

Creating a path for every employee to own a piece of the business is one way to help counter that. Whether it’s allotting equity in your company or sponsoring employees to start their own ventures, ownership breeds a sense of purpose and pride that can be matched by few other things in a professional’s life. From the pride of owning something, to the additional revenue that can come from the business succeeding, degrees of ownership can help rid individuals of that sense of scarcity.

3. Volunteer for small business workshops and literacy programs

Unemployment is historically low, but underemployment can be a bigger issue for people trying to provide a future for themselves and their families. Not only that, but job satisfaction has been shown to impact everything from health and energy, to community participation and healthy family life.

I’ve had the chance to meet with people all over the world, talking about innovation and discussing people’s goals to create new things. Of the hundreds of people I’ve spoken to, many weren’t happy in their jobs and wished they had the tools or abilities to do their own thing.

Barriers to making a change can include lack of education or skills, inexperience, or lack of knowledge about existing sources of funding. The right mentorship from people who have catapulted over similar obstacles may be part of the answer. Entrepreneurship isn’t for everyone, but equipping more people with a path toward becoming one will help them create their own opportunities.

Furthermore, children of disadvantaged homes often struggle in school due to issues surrounding basic literacy. Those same children often grow up struggling to compete in everyday work environments. Consider volunteering your time to help kids get caught up. It can have a lasting impact and help each child feel like they have a shot at success.

4. Engage with your local government

Local issues lead to national campaigns. This election was won because small-town issues outweighed the opinions of urban populations. Wherever you live, there is a local community where people across many walks of life engage in issues that affect them. As an entrepreneur, you’re a part of that conversation each day, possibly without knowing it. Consider then, how you can take a more active role in supporting initiatives that help everyone succeed. These may include:

  • Fundraising for a new school
  • Participating in a business plan competition
  • Getting involved in ballot measures that locally could have a positive impact on economic growth.

Ridding your community of opportunity scarcity means we’re all better off. It also helps reduce the effectiveness of fear mongering and hate.

5. Stop considering college as the only path to entry

Education is a traditional barrier in professional environments. Yet, I’ve worked with many smart people with only a high school education. Treating college as just one route to success rather than the only one is an important step. You can do this by:

  • Supporting a trade school by volunteering for mentoring, guest speaking, and participating in hiring fairs
  • Advocating for blue collar workers (ex: consider incorporating local fabrication shops into bids for product prototypes vs outsourcing everything).
  • Considering skilled people for employment regardless of their backgrounds

People are feeling a divide, where those with formal educations are perceived as the “haves” and those without are the “have nots.” As an entrepreneur, you may have insights into how to break down this divide within your own organization.

Consider how you might use these approaches in your own community, and leave comments below on other ideas that might help heal our country and world. As an entrepreneur, you’re more equipped than you know to encourage change and help fix some of what is broken in our society.

How to Sell People on Really Big Ideas

I was recently talking to someone about his plans to launch a new concept focused on data analytics in the B2B space. He walked me through his models, his research, and all the great ideas he had on where he wanted to go.

I felt like he wasn’t going to be successful, though, because through most of the meeting, he talked to me in a monotone his head down. He didn’t have the kind of jump-out-of-your-seat enthusiasm or passion behind his idea that made me say “Wow, this really is something different.” And sadly, when it comes to breaking people out of their patterns, passion matters more than ability.

Investors may say it’s all about the numbers, and that’s true. It’s table stakes. However, if an investor doesn’t think you can sell the product, or yourself, then getting the cash is going to be a long shot.

If you’ve ever sold something or been sold to, you understand what I mean. The person that comes in with enthusiasm, whether it’s a TED talk or a sales pitch, will often surpass people with more knowledge and abilities. If you’re telling me something is amazing, I want to feel like you’re amazed by it yourself. If you’re telling me something is revolutionary, I expect you’ll show that in your body language.

A failure to connect with your audience is often a question of your emotional state, of how you interact or don’t interact, and how much enthusiasm you’re able to communicate to get other people excited as well.

Here are some tips then, on how to surface passion when it comes to launching something new and innovative–no matter how much of an introvert you might be.

1. Develop questions, not lecture notes.

When it comes to a conversation on a concept no one in the room has heard of before, don’t go in with a 50-slide PowerPoint deck. Instead, focus on preparing good questions ahead of time about the audience’s pain points and other things that matter to them. If you have a strong context for your audience, you’ll be able to adapt the pitch around them.

2. Don’t talk about the product or solution, talk about the problem you’re solving.

Anyone, from an investor to an executive to the person selling flowers on the corner, would give you a dollar if the person knew he or she would get two dollars back. Since there’s no such thing as a sure bet, it takes trust and confidence to get people to part with time or money. The only way you make that connection is by helping them see how they make two dollars by spending less, or by your helping them generate more. Assume your audience knows more about their business than you do, and focus on solving the pain points that are in their way to save money or generate revenue. If your product does neither of those things, you’re probably pitching the wrong people.

3. Numbers matter, but save it for the ask.

If you’re pitching investors, numbers are king. If you’re selling an executive on the benefits of your platform, you’re going to have to quantify that in some way. Passion does matter most, but it’s a nonstarter if you don’t have numbers to back up what you’re saying.

4. Practice, practice, practice.

Passion often comes through when we are feeling our most confident. To get there, practice how the pitch going to go. Get your questions down and your talking points committed to memory. Recite whatever good-luck quotes, prayers, or mantras to feel relaxed enough to pivot in the moment.

If you do these things, you’ll not only connect with your audience more effectively but also feel better coming out of the meeting, nine times out of 10, a great feeling in and of itself.

3 Ways to Ensure That Your Idea Takes Root

When it comes to ideas, most disappear in the first hour they’re created because bringing something into the world takes serious effort. But no new thing can exist without real determination, struggle, and the effort of multiple people to see something come to life and flourish. Yet, some of the world’s best ideas disappear as quickly as they’re thought of because people shoot themselves down before telling another person what they’re thinking.

The simple truth is that often times our ideas don’t even get past the first handful of people because negative feedback is taken as justification to not do anything with the idea. What should happen of course, is that you take that feedback as a means to improve your idea and continue to iterate til it takes root.

How to do that though, can be easier said than done. Here then, are just a handful of steps that should help:

Tip #1 – Don’t let criticism get to your head

There are many successful people today living exciting lives built on their ideas, who had to fail at getting traction several times before they succeeded at getting their ideas to take off.

  • Akio Morita’s first rice cooker sold fewer than 100 units, because it burned the rice instead of cooking it. You may not have heard of him, but you’ve probably heard of his company, Sony.
  • Walt Disney was fired by a newspaper editor, because the editor felt “he lacked imagination and had no good ideas”
  • During his lifetime, Vincent Van Gogh only managed to sell one painting but painted 800 anyway, during his lifetime.

The potential and ideas were there in each of these people all along the way. These ideas they had just had to either go through several iterations, or wait until everyone around them caught onto the idea themselves. In each case though, these people refused to let the ideas and concepts they had die at the hands of themselves or anyone else they encountered along the way. That’s what separated them from people with good ideas who you and I haven’t ever heard of before.

Just like getting a car out of a ditch, it takes real effort to get your idea moving initially until it takes off on its own momentum. Be careful to not be your own worst enemy, by failing to get the idea off the ground because you weren’t willing to conquer your own fears and insecurities to get the idea on paper and begin sharing it with people, while using that feedback to refine / shape the idea along the way.

Tip #2 – Don’t be afraid to alter your plan of attack

The hardest ideas to surface can sometimes be around questioning something already agreed to, or pushing back on something half way built. The truth is though, that the most difficult ideas to surface can end up leading to the biggest improvements, as those ideas can help reveal blind spots missed along the way.

I’ve worked with companies that have had to choose between incorporating the next idea, shipping the product as is, or pivoting altogether because something drastic changed since the product road map had been put in place. Though it’s never an obvious choice which direction to go, shipping something they knew would be flawed always turned out worse in the end than delaying shipment to get the product right. It’s important to consider new ideas at every stage of product development in order to ensure that the right product ships every time.

Tip #3 – Continue sharing the idea, re-calibrate with user input, and persevere

Only through doing difficult things multiple times does it become easier, and being innovative and driving new ideas certainly is a muscle we must flex multiple times to get better at it. Repeating the process of developing ideas, refining them, and getting those ideas into meaningful outcomes is certainly worth the effort, but will always take effort (either internally or externally). It will also require dedication and determination, but will produce meaningful outcomes each and every time.

You need to always be willing to bring something up and share freely so that your company can continue to reinforce a culture of innovation and collaboration.

3 Steps to take Data Analysis from “Well, That’s Interesting” to Revenue Impacting

Data is hot right now, and it seems everywhere in business these days there is another tool or framework on how to leverage data to drive a real difference in your business.

A good metaphor to understanding data effectively though, is training for an Olympic event (great timing for the metaphor, eh?). Most athletes train, not for the sake of training, but most likely because they want to stand on the podium with a medal as a statement to how good they are at the event. Anyone can train for the sake of training, but competing and winning is really the whole culmination of 4 years of tireless preparation and training.

The same is true for data visualization, in that the real goal should not be building dashboards and pretty charts, but pointing to the direct impact that data had on driving a top or bottom line impact on the company’s revenues. Yes, in larger companies it’s very hard to make a difference on the overall number, but every piece of data should tie to some positive contribution or else what is the point?

Yet, with so much data being made available, and so many people learning how to mine data for insights, there’s a lot of very pretty pictures out there which don’t move the needle at all. Yes, it’s great to hear about athlete’s and how they train, but the credibility isn’t there to the same degree as it is if they’e wearing an Olympic medal. If you’re a budding data analyst, or seasons chart builder, imagine if everything you built had a number next to it that said “this piece of data drove this quantifiable business impact”.

Though data visualization can serve qualitative benefits, such as monitoring a key business process or helping change a perspective on a topic, there should still be some way to tie even those things back to the difference it made (or could make) on the business.

Here’s three steps then, to help that mindset along

1. Understand the Reliability and Structure of the Data You’re Using 

All because you have data, and have access to data, doesn’t mean it’s useful or all that important. You having access to log files from a server, which spits out information on usage patterns of your e-commerce system throughout the day only matters if you’re able to impact that usage in some meaningful way, then tie it back to a positive revenue lift. More importantly though, you have to know the data is reliable and understand how to model it in a way that it produces accurate conclusions. Build some baseline metrics, and measure against numbers you know are correct before going into any complex modeling exercise. Once you put a chart in a slide, it’s out there. So make sure you’re starting from the right data set to begin with. 

2. Develop a Series of Hypothesis about Your Business

Once you know the data you’re working with, and have a good sense of how reliable it is, think about the business as it relates to parts you can actually control / influence. If you’re in advertising, don’t focus on product improvements. If you’re in product development, don’t worry about retention patterns on the website. Think about 3-5 gut instincts you have about how the business could operate differently, then use the data to test out those theories. Don’t simply mine the data, hoping the magical insights just pop out at you. Data, like a car, helps you get to where you’re going – it won’t take you there on it’s own. You need to at least have a rough idea of what you’re looking for, so you can build worthwhile visualization to help vet that hypothesis into an actual conclusion.

3. Focus on Low Hanging KPIs, then Expand from There 

It’s easy today, with technology making more data accessible with less effort, to try and go after ground breaking insights. However, you no doubt have areas of your business you can directly impact and know will make a sizable difference to the bottom line, that you need help in influencing. Start with thinking through 3-5 KPIs coming from your hypothesis that you can build from the data you’ve vetted, to influence key business people or back up your assertions with a new direction you’re moving your team in.

If you can measure it, you can prove it (as long as it’s reliable and true), but remember that it’s never a silver bullet or the whole story. Data can be powerful, but it can also lead people astray or get people focused on the wrong things. When done right though, using validated data tied to a hypothesis and measured in a way that drives a meaningful revenue outcome, it can make a big difference.

Why Tableau is Worth Billions: A Case Study on Becoming a Digital “Port City”

It’s appropriate that Tableau is located in Seattle, as they both became popular for similar reasons.

Seattle, started as a logging town shipping lumber down to San Francisco, then hit a big boom during the Klondike gold rush followed by a big shipping boom. It then moved into a big boom in aerospace followed by the growing influence of technology – starting with Microsoft. Access to resources, and a connector between multiple places. Seattle was big on logging, because there was an ocean that made it easier to transport lumber south, with the means to make it accessible and useful. Seattle was big during the Klondike gold rush, because you could take a ship from Seattle over to Alaska, and provided the resources and shipping to get there. Seattle was big into aerospace, because William Boeing got things kicked off here so there was the resources and buyers to set up a shop and build an aerospace business. Seattle then became big into technology, because Bill Gates changed the world with Microsoft Windows and people could come and leverage the resources that created.

Now lets look at Tableau – From wikipedia: “In 2003, Tableau was spun out of Stanford [9] with an eponymous software application. The product queries relational databases, cubes, cloud databases, and spreadsheets and then generates a number of graph types that can be combined into dashboards and shared over a computer network or the internet. The founders moved the company to Seattle, Washington in October, 2003, where it remains headquartered today” 

Tableau then, wasn’t famous because it invented data or created a better way to store data. Rather, the platform made that “digital lumber” we know as data more accessible. It became a way for an average user to reach out into the data space and extract useful information, which they can then use. In effect, Tableau is the digital “port” city for many business owners, that provides access to that raw material and the capability to make it useful.

Becoming a digital port city then, isn’t all about what the platform provides in and of itself but the material it helps you gather / process / leverage. Social media is billions of messages, but Adobe’s Marketing Cloud promises to make quantifiable sense of it all. Server log files are completely useless in and of themselves, but Splunk helps turn all that into a meaningful dashboard.

Lots of tools exist out there, promising to mine assets and turn them into something useful. But as data became a boom, and the trend grew, you could also see the rise of companies like Tableau growing along with that tide. If cities in the 1800’s decided to use clay instead of lumber, perhaps Seattle may never have taken off.

What’s important to note then, is that becoming a digital port city can produce a tremendous amount of value as long as the resource you’re accessing is growing in popularity. However, everything (even data) only stays a popular trend for so long. The hope is, then, that you’ve grown enough to sustain yourself until the next wave takes off and you can successfully adapt along with it. Tableau is in it’s first major boom cycle, as Seattle grew with lumber. As history has shown though, Seattle had many boom and bust cycles as time goes on. How many companies also rise and fall within a single hype cycle (ex: Detroit) ?

Becoming a digital “port city” and staying that way really comes down to 3 things

1. Don’t oversell the hype (to yourselves or your clients)

No matter how on fire your company might be today, every marketing pitch or slogan only has so much gas in the tank. Focusing on the broader industry issue (ex: revenue growth vs access to data) means you’ll continue to stay relevant long after the initial hype has passed. Take advantage of a trend’s popularity, but don’t so closely associate yourself to that one thing that you can’t exist without it – what if Kodak had focused on better living through chemistry vs film? As film declined, chemistry surely didn’t go out of style. And as it turned out, Kodak had some of the most talented chemists in the world working for them because film is a hard thing to make. What would have changed, if Kodak’s brand became focused around something that wouldn’t ever go out of style, vs a single product focus? 

2. You’ll have to think of the post-hype at some point 

Yes, it’s important to stay hyper-focused on your core competency and capability during a big sales cycle, but long term planning focusing on “what do we do when people don’t care about X trend any more” is important. Google will have to figure out ways to make money, after online advertising. Facebook may not be the hot social network 100 years from now it is today, and Microsoft is already starting to evolve in a world that cares less about personal computers. Tableau, too, has the talent and revenue to think about what’s next in the data space long after people stop caring about 2D data visualization in the form of accessible dashboards. Though we have examples, every company has to overcome it’s own culture and leadership challenges to continue to evolve and adapt. 

3. Build a foundation around the longer term trend, while capitalizing on the current hype 

Say you’re Boeing, and you’re contemplating life after airplanes, or perhaps investments that build a platform of services focused on a single brand element of your company. Do you diversify, by extending your reach into other areas of aerospace, or do you step back and say “well, our real purpose is to connect people, so lets invest in other ways to connect people outside of just flying them together”. It’s a tricky question, with no easy answer, which could mean botched acquisitions and a confusing marketing plan if you’re too broadly focused. However, tying in telepresence as part of the “connecting people together” strategy may mean infrastructure investments in aerospace communications networks, that you wouldn’t otherwise make, to allow video chats in airplanes while investing in smaller start-ups that focus on video codecs and compression algorithms that might net you a decent return down the line.

Focusing on just building airplanes though, Boeing would never invest in a Skype, but down the line will it be too entrenched to see a decline in aerospace with the will to shift their focus? Skype would have been a bad idea for Boeing, but what about investments in technologies that make it easier to transmit video which is entirely something they could leverage today? It’s not easy to do, and a lot of companies get it wrong here, but focusing your core message and internal alignment on something bigger than the immediate trend or fad is important, if you want to build a company that’ll be around 30+ years down the road.

If you do those three things successfully, whether you’re a city near the Ocean or a data analysis tool helping unlock value, you’ll no doubt continue to justify the value you bring long after that initial wave has past. It’s why Seattle continues to thrive, whereas cities like Detroit have struggled, and why Tableau is worth billions as a tool that accessed data without developing/ hosting/ managing most of the backend infrastructure that makes up those data systems. Stay beholden to only one path, or one product and you could go from the top of the pile to getting buried by your competitors. Toyota would say it’s not a car company, but a transportation company – because cars are only relevant for a period of time, but people will always need a way to get transported.

Become a digital “port city” by making a useful resource accessible and useful, then focus on continually evolving as the thing people need access to changes.

3 Reasons Why You Need a Chief Analytics Officer

Data has exploded in a way that rivals mobile’s explosion ten years ago. Everyone is out there buying masters degrees, data visualization licenses, and data scientists by the truck loads in a way that mimics corporations buying mac laptops, mobile developers, and app store branding when iPhones blew up the smart phone space.

The Analytics ‘Trend’ Isn’t New

There are a lot of great things taking place right now with all the interest around data analysis, but the funny thing is that data analysis is nothing new (neither is data science). There’s a good 30-40 years of work on data, from data architecture to database administration (not to mention the millions of excel spreadsheets that corporations are running critical business functions on) that live inside companies and create a legacy layer that this latest wave of data analysis is building on.

Other new trends, such as big data analysis and the cloud computing revolution, have further spurred companies to consider ways to extract usefulness from their existing data and move away from churn or ARPU and develop distinctly competitive analysis with phrases like “regression analysis” and “predictive analytics” becoming much more common in corporate board rooms.

Translating Data

The big problem is, as was the case with mobile, is that you have to be able to translate interesting technology into impacting ROI-laden investments that drive top or bottom line revenues (or create efficiency and lower costs of course, as well). There’s a good deal of buzz around big data being an overused term, and a hundreds of millions of dollars spent on visualization tools will, at some point, taper off when the average business user turned dashboard builder runs out of things to visualize due to saturation, bad data, etc.

So Who / What Is This Chief Analytics Officer?

A Chief Analytics Officer could be a Director of Data, or a VP of Analytics, but having someone at an executive level that can drive a centralized data strategy for the company should exist for these three reasons.

  1. Centralizing Your Data Resources Will Help Avoid Silo’ed Capabilities

To turn all this hype into profit, it means building a centralized capacity. A capacity which sites outside of the IT-to-business politics and hype to buy visualization tools, and instead focusing on building a stack of capabilities, from the data lake to the dashboards, geared around revenue generating use cases taken from business partners who need more usefulness from their data without having to build silo’ed data science teams that rely on fractured data sets.

When anything is this pumped up, every department is going to want to get involved and build capabilities, since every business group uses data in some form or another. The problem is that it takes a variety of experiences and backgrounds, along with investments, that need to be built at a corporate level with a plan to centralize some capabilities and decentralize others with a clear data strategy that everyone can get behind.

Centralizing this capability means one strategy, one leader, and limitless opportunities for everyone to participate without each department deciding their own game plan for riding this data wave.

  1. Consolidating data to maximize usefulness, while aligning that effort under a single leader

The topics around big data, and data lakes are growing overwhelming, with more and more companies working to consolidate all their data in one place to allow for both advanced analytics & traditional business intelligence functions. At the same time, a data lake built in the wrong way can cause latency along with too many executive peers building extensive requirements which ultimately brings any progress to a halt.

Bringing your data consolidation effort under a single leader, tied to a data strategy that brings the bigger outcomes into focus and alignment while leaving the smaller day to day details up to a single org unit means your company can spend less time planning & debating, and more time driving value from your data lake.

  1. Impact is prioritized, over ‘interesting trends’

Much like the millions of dollars spent on corporate mobile apps that never got traction, companies today are spending millions of dollars on real time streaming, data visualization, and corporate education on DAX programming all in an attempt to capitalize on the data analytics hype and create a stronger bottom and/or top line revenue stream through the use of data analysis.

The thing is, data isn’t a new domain for technology, nor is investing in Big data going to revolutionize your company.

There’s a good deal of effort being spent on building impressive looking visuals, which add no incremental value over the same data displayed in an excel chart. Furthermore, companies investing in hiring legions of data scientists without clear revenue-driving hypothesis will find they spend a good deal of time figuring out just what to focus on.

As is the case with any over-hyped technology, whether it’s enterprise wide tableau licensing or infrastructure to support web traffic analysis for real time personalization, the tools are only as good as the capabilities on the team and the business cases they are actively working towards.

Focusing on a single leadership structure to come up with the real tangible value for investment in data analytics means there’s a common set of goals that’s driving the spend, and a clear idea of what each department and employee is focusing on.

It’s not so much that a single team owns every analyst, but rather each instrument is calibrated so the whole company sounds like a beautiful concerto vs a number of instruments playing at different rhythms.

Furthermore, when it comes to the vendor onslaught and procurement nightmares that naturally arise in the midst of a technology boom, there’s a clear investment strategy for how the company plans to leverage capabilities such as big data or advanced analytics. This can influence everything from recruiting and training, to infrastructure and software licensing, and help ensure each investment is additive vs expensive and lacking in impact.

There’s a good deal of interesting happenings in the data space right now, but companies need more impact to back up the cost.

There are no doubt other benefits I’ve missed out on taking data seriously, and putting someone in charge who is somewhat removed from the politics and inefficiencies that come from burying the capability inside an existing org (similar to the CIO coming of age, and now no longer reporting to CFOs in most companies).

The aim is however, to ensure your data analytics efforts are making a meaningful impact, and driving the kinds of returns most companies never experienced during the mobile app boom almost ten years ago now. And in so doing, benefiting every company that invests in the great capabilities a data-driven org has at its disposal.

« Older posts

© 2022 DanMaycock.com

Theme by Anders NorenUp ↑