Hours of your life

Pre-s: I’m opening up a limited number of 1 Hour Strategy Session slots this summer. One hour of pure focus where we strategy, design, and creatively engage your data challenges.

Book here.

100% money-back guarantee if the call wasn’t worth it.

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People want AI, LLMs, and buzzwords.

But.

Actually, no. Most people want hours of their lives back. They spend hours of their week manually exporting reports, cleaning Excel files, entering data, or copying and pasting from one document to another.

I meet with these people regularly. They aren’t data professionals (yet). They are accounting teams, customer service managers, and operations team leads.

Their daily job is filled with low-hanging fruit. They spend hours a week performing manual tasks that humans are terrible at.

In the last month, I sat down with a customer service manager. He spent 45 minutes every morning pulling reports manually from different systems. With modest effort, I removed 95% of that task.

Last week I sat down with a CFO and her team. I spent several hours together with their team training them on a new tool to remove a significant portion of the manual work they spent their days on.

“This will save us several hours each month”, she told me.

AI is coming. You won’t be able to avoid it.

But most companies have no capacity for AI right now.

They simply need computers to do the things that computers are best at (repetitive tasks).

So they can do the things they are best at - thinking strategically about their team and work.

I’m here,

Sawyer

p.s. If you want help building reporting solutions that give you hours of your life back, hit reply.

Your data lies to you

Pre-s: Free Live Stream Webinar today at 10am/1pm PT/ET

Data Roundtable: Architecture Design Sessions. Join me and James Serra as we talk about how to run Data Architecture Design Sessions. James is a Technical Architect at Microsoft and the author of the O’Reilly book Deciphering Data Architectures.

——————

Data tells lies all the time.

You can make it say whatever you want.

Here’s an example I saw from LinkedIn this week.

Wow! That chart looks terrible!

Home prices have increased 462% but income has only increased 215%.

Sure looks like millennials have it bad. How are we millennials supposed to survive in these terrible economic times? Our parents had it so good. They can’t understand the hardship we are going through right now.

What a great narrative for the news headlines to grab.

The problem? This chart is lying to you.

Lying by omission. They left out a crucial piece of information.

Mortgage interest rates.

Average interest rates in 1985 = 13%

Average interest rates in 2022 = 3.5%

Which dramatically changes the narrative. Here’s what home prices as a monthly payment look like

1985: $83,200 house at 13% interest = $920 monthly payment

2022: $468,000 house at 3.5% interest = $2,102 monthly payment

So while housing prices increased by 462%, a monthly mortgage only increased by 128%. Still sound like a big increase? Well, income increased by 215% during that same time.

In 1985 the median American family paid 46% of their income toward their mortgage and interest.

In 2022 the median American family paid 33% of their income toward their mortgage and interest.

Thats right. By this measurement, millennials in 2022 had access to cheaper housing by percentage of income than their boomer parents.

The narrative completely flips

Data is not objective. It is very bias based on the story you want to tell.

To be “data-driven” means not only that you know how to make decisions with data, but you know how to determine when data is lying to you.

I’m here,

Sawyer

Center the decision, not the data.

Pre-s: Free Live Stream Webinar tomorrow at 10am/1pm PT/ET

Data Roundtable: Architecture Design Sessions. Join me and James Serra as we talk about how to run Data Architecture Design Sessions. James is a Technical Architect at Microsoft and the author of the O’Reilly book Deciphering Data Architectures.

——————

When “data is the new oil”

And “data drives everything”

it’s easy to miss the main point of data.

To make better decisions.

When you start with data

• You get distracted by collecting lots of data

• You get enamored with interesting data.

• You get isolated by data technology.

• You get lost in data questions

When the decision comes first

• Data serves the business instead of the other way around.

• You know exactly why you are looking at the data.

• Your action item or next step is easy to uncover.

• Relevant data matters more than data volume.

This is the importance of decision-driven data. Center the decision, not the data.

I’m here,

Sawyer

Tinkering with existing sketches

The artist isn’t scared about the act of creating something.

That’s when most people find themselves most alive. When the making is happening. Something new is coming into existence. They are in the zone. In a flow state. Distractions disappear for a period of bliss.

That’s what most people long for and hope for more moments like that in their work.

The scariest part of producing art is that people will ignore it. That new idea, innovation, or image you birthed into the world might be welcomed into the world with utter silence. No one notices. No one cares.

It’s terrifying.

When we create, we pour out our time, effort, imagination, dreams, and emotions into making something that didn’t exist before. Then with a small (or large) amount of trepidation, we hold out our new creation to the world around us - hoping someone will notice.

Being ignored doesn’t just feel like our creation wasn’t any good. Rather, it feels like our dreams, hopes, efforts, imagination or creativity don’t matter. That there’s no place for it in the world. That maybe, just maybe, there isn’t actually a place for us in this world.

Just the idea of being ignored scares most artists off from even starting.

And so, we recycle old and trite ideas. Tinker with existing sketches. Too afraid - at a survival instinct level - to create something that might lead to being ignored.

What does this have to do with data?

AI is rapidly changing the world of technology, information, and communication. The activities that you used to spend most of your day on - coding, spreadsheets, writing documentation, gathering requirements - will slowly fade away from your responsibility list.

What’s left are opportunities to do what humans can uniquely do. Create. Be an artist. Innovate.

Build something that might not work.

Write an idea that might change how we think.

Tell a story (with data) about how the world works.

If you dare, you can make something new.

With enough courage, excitement, or maybe naivety, you can create.

You are an artist. Or at least you can be.

I’m here,

Sawyer

What is a data platform?

Data teams and data professionals build, manage and use data platforms.

But what exactly is a data platform.

Short Answer:

A data platform is a place where data is stored and accessible to end users. The most basic version is an Excel workbook stored in a shared location (OneDrive, Sharepoint, Google Drive).

Medium Answer:

A data platform is a computer system (and often a collection of computer systems) that stores all relevant data for the enterprise or organization. Data is stored with resilient, reliable and scalable technology (like a database). In addition to storage, it has tools to allow users to query, visualize, and interact with that data to support decision making.

Long Answer:

For a data platform to function as the home for enterprise data, the data has to be collected from various source systems. ERP, CRM, Accounting software, HR software, and any other application that houses your company data. Move data from source systems into the data platform is a key function of a data platform. This movement is facilitated by orchestration and scheduling tools as well as custom scripts that define how, when and what data gets moved.

When the data lands in the data platform, data teams focus on storage, quality, and modeling of the data. Storage requires considerations of data size, access requirements, structure and format of the data. Most organizations use databases, data lakes, and data warehouses to manage storage.

Using the data effectively is often the hardest step. Surfacing the data to users in understandable, flexible, and scalable ways presents numerous challenges. Defining key metrics (”revenue”) or entities (”member” or “donor”) is a constant collaboration between data teams and business users. Most often data is access through Business Intelligence analytics applications.

Books have been written about each area of a data platform, and vast numbers of software tools exists to solve problems in each area.

When embracing complex topics (for the first time, or the thousandths time), start with the forest and slowly move to the trees.

Have a great weekend,

Sawyer

How to make sure your reports don’t collect cobwebs.

Three ways requirements gathering fails:

• It doesn’t happen.

• It only happens in the form of a support ticket (i.e. “Build this report for me”)

• The data person asking the questions has no idea what to ask.

Three ways report documentation fails:

• It doesn’t happen.

• Only a small part of the process is documented but then never updated

• The data person writing the documentation has no idea what to document.

Before you solve technical problems you have to solve process gaps. Requirements gathering and documentation are two thorns on the side of every data team I’ve worked with.

So, yesterday I sat down with my friend Ahmad for a Data Roundtable conversation to offer practical tools and perspective on how we solve these chronic issues.

If you want a long-term and strategic view of business intelligence reporting, this webinar is for you.

Watch the reply here:

Data Roundtable - Power BI Leadership

I’m glad you are here,

Sawyer

Data and Digital Transformation in the Northern Woods of Michigan

Free Webinar this afternoon on Power BI Leadership - Join the LiveStream or on LinkedIn.

————

Episode 02 of Making Data Matter is live!

This is a special episode for me. We sit down with guest Jim Bennett - Director of Business Operations and Technology at Camp Barakel. Jim played a huge role in my career and has been a good friend/mentor for several years.

In this episode we discuss:

• Jim's background in enterprise consulting and how he ended up at a small camp in Northern Michigan

• How Jim has tackled strategic digital transformations for a historically low-tech organization.

• What skills are needed to be successful in data and consulting

• How Jim took a risk hiring an early career Sawyer.

and more.

Listen on:

Spotify

Apple Podcast

Join me on a roadtrip

Pre-s: Tomorrow at 1 pm ET at a free live webinar: Data Roundtable - Power BI Leadership. If you work with Power BI at your organization, you will find this a valuable conversation.

Here are four fundamental measurements you will encounter in your organization's progress toward their goals.

If you are paying attention to data these are important to identify.

Destination, Waypoints, Signposts, and Gauges.

Consider this a road trip. In a couple weeks I’m driving my family 20 hours south for a vacation, so imagine for a min you are joining us.

First, we pick a Destination. You will know if you made it there by measuring your location against the desired location (you could use latitude and longitude). What’s the distance between current location and destination?

Next, along the way you will encounter Waypoints. These are key progress points along a journey. These could be mile markers on the highway, or key rest stops along the way. You know if you made it to a Waypoint that you are making progress toward your destination.

You will also run into Signposts. Different than Waypoints which track your progress, Signposts give you more contextual information. What city are we in? What’s the name of this road? Is this a good place to stop for lunch? They don’t tell you anything specific about your progress toward your destination, they are useful to help orient you so you can make the next step toward the next Waypoint.

Finally, you have Gauges. These are small objects that you track at different points in the journey. How much gas (or charge) do we have in the vehicle? What’s our speed? How much traffic are we stuck in? Gauges can be important but aren’t worth constant attention. Your fuel will last for a few hours, staring at it constantly won’t help your progress to your destination. Speed is relevant to your progress, but on an interstate, you likely will stabilize in cruise control and forget about your speed for long stretches of time.

——————————————————-

Here’s how this matters for data.

These are all metrics in your organization.

Destination metrics will never change. They are terminal metrics and core to why your organization exists.

Waypoints rarely change. They are the core way you measure progress to the destination metric. Only through substantial business model changes do Waypoints change.

Signposts are constantly changing. They help you know what’s going on around the business (employee morale, economic conditions), but they don’t track your actual progress to the destination.

Gauges are only checked intermittently and often change. How fast a pipeline runs might matter a lot one week, but after optimization you can ignore it for a while. The initial launch of the campaign matters a lot, but as it goes on it requires less monitoring.

Data teams live in the land of metrics. As an exercise, it’s valuable to label the metrics you are responsible for to one of the above categories.

Revenue? Probably a destination metric.

Customer Churn? A waypoint.

Customer satisfaction? Signpost.

Production bugs? Gauges.

A few questions to ask:

• How many destination metrics do you have? If you end up with 17 destination metrics, you are confused and need to start over. Organizations have 2-3 destination metrics at most. You likely only have one.

• How much time are your spending staring at gauge metrics? You are probably missing key waypoints.

• When a new metric request comes in, how should it be labeled? Answering this question will equip you to prioritize the request.

I’m here,

Sawyer

You will get slower before you get faster

Last week talked about how your data team can move faster. Source control, Environment strategy and CI/CD.

Here’s the harsh truth if you focus on any of these areas.

You will get slower before you get faster. New processes will take a while to formulate, longer to build, and even longer to fit smoothly into your team’s work. It requires admitting and owning that your processes aren’t going as well as they could.

Being resilient through slow work sets disciplined teams apart from the rest.

Removing friction requires sanding. The capacity of your team to scale with demand, bug fixes, and enhancements is on the other side of the grit of sandpaper.

I’m here,

Sawyer

Getting it on the table.

This week we are talking about how your data team can move faster. So far we’ve covered source control and environment strategy.

Today we are looking at how you deliver data products to your data consumers.

All the prep work, detailed configuration changes, optimizations, enhanced visualizations, and new metrics somehow have to make it into the hands of the users.

It’s about delivery.

Moving food from the kitchen to a guest’s table is the process of delivery. It requires integrating food from different parts of the kitchen (protein, vegetables, starch, etc.) onto the same plate. Then that plate goes through a series of checks before it’s delivered to the table. The nerds among us will call this Continuous Integration (CI) and Continuous Deployment (CD).

When you only have one element of your solution - perhaps an Excel file - then the integration part is easy. There’s nothing else to integrate.

But nearly all of your data solutions will include multiple parts, which means changes to one could impact several other parts. A process to test and integrate changes to these solutions in a designated location is crucial. Did that pipeline update break our BI report? What about the view definition change? I wonder if changing this database table name will disrupt anything?

In addition to the constant integration of new changes (every couple hours or at least once a day), your team needs to regularly ship that code. Land it in the hands of users.

That’s deployment/delivery. I’ve worked with some data teams where deploying new changes is an hours or even days-long process and they only deploy once a month or so. The best data teams deploy multiple times a day.

The simplest form of CI/CD is refreshing the data in an Excel file and emailing it to a business user. But most solutions are more complex than that.

Your team needs to invest time and energy into this area if you ever want to scale, deliver quickly, or ensure data quality.

If you need help talking through this process for your team…

I’m here,

Sawyer

Is Your Data Team Chopping, Cooking, and Serving All at Once?

This week we are focused on how to move faster as a data team. Yesterday we talked about source control and managing changes to your data solutions.

Today is about environment strategy.

But it will be more fun if we talk about a restaurant and food.

Every restaurant has a kitchen and a dining room. A place where food is prepped and a place where food is served. These are ‘environments’, often called Development (Dev) and Production (Prod).

Dev is the land where the staff works. Vegetables are chopped, steaks seared, and soup simmers in the “back of the house”. This is also where the dishes are cleaned. Unless specifically planned, guests do not see what happens back here.

Instead, guests stay in the dining room, where the experience is particularly designed for their enjoyment. No piles of messy dishes, frantic cooks, spilled oil, or burned asparagus.

In order for your data team to operate and deliver effectively you need separate environments. In the most minimal of data teams, this looks like just Dev and Prod. But in more advanced and complex organizations (or fancy restaurants) there will be more environments.

A Dev, where code is written (food is cooked). A Test, where changes are integrated with the existing code base (each element of the dish is plated). A Pre-Prod or Staging, where it goes through a final review before release (picture Gordon Ramsey meticulously reviewing a dish before it is served to guests).

Some companies have 7 environments. Most of 2-4.

But what happens if you skip this step? You are chopping, cooking, and plating all at your guest's table. Which works great except when it doesn’t. The restaurants that do this to create unique experiences are both exceptionally skilled and only choose the activities they are 100% confident they can get right every time. Everything else is still prepped in a backroom.

A basic environment strategy for your dev team looks like:

• A place where the data team can make changes in safe ways that won’t affect end users.

• A place where those changes and new features can be integrated with existing features to make sure nothing else breaks.

• A place where required quality assurance and testing can take place (before away from end-user experience).

• A place where users can consistently access high-quality data products without fear of bugs or data quality failure.

This is all about moving faster. Clean and well-thought-out environments make data teams more confident in development and deployment.

What do your data environments look like?

I’m glad you are here,

Sawyer

Making some changes

This week we are talking about moving faster as a data team.

It’s your job to make changes, add new things and remove old things.

Reports, dashboards, database tables, python scripts, and ETL pipelines. You and your team will touch some or all of these objects every day.

But who touched what? When did that report get changed? Two people tried to change that at once, which change counts?

When you are a team of one (or a part-time data person), it’s easy to assume you can just keep track of everything on your own. Until someone asks you about a code change you made 6 months ago (or 6 weeks ago) and you can’t remember why that occurred. If you are like me, you’ve slept a few times since then and obviously forgotten everything.

But the pain points become clear as the complexity grows.

• Two team members and three reports.

• Five team members, two databases, and thirty-five dashboards.

• Three teams, a data lakehouse, ML models, and way too many dashboards.

This is why source control is fundamental for data teams.

At its most basic form, source control keeps version history of your files (like Sharepoint, Excel or Google Docs might do). But for software and data teams, source control allows multiple team members to work with multiple files at once in isolated environments, enabling them to integrate their changes together, while keeping a record of what changes occurred and when (with notes about the change). And roll back changes when needed.

One of the highest ROI activities for your team is taking a small step toward source control. This week,

• Review existing source control solutions. Do you have anything? Are they being used or ignored? What % of your code solutions do they cover?

• If you are starting from zero, pick one area of your work and find a source control solution. The quick and dirty solution is Sharepoint. But plan to pursue more robust solutions in the near future.

• Work with your team to revise or establish processes for integrating working with source control into their development work.

Then hit reply and tell me about your progress or barriers.

I’m here,

Sawyer

from The Data Shop

How to move faster as a data team.

This week is a series of quick hits to help your data team deliver data more effectively. Outcome-driven data teams know how essential the speed and efficiency of your team is.

If you have at least one full-time data person on your team, these are fundamentals to make you successful. If you have multiple full-time data people, then you are losing ground if you ignore any of these areas.

At the basics, a data team should operate with three things:

• Source Control

Data teams work with code and files. They are constantly changing those files and code bases. Source control is a type of software that allows you to keep a details record of changes and versions of your code base.

• Environment Strategy

A restaurant has a kitchen and a dining room. A place where food is prepped and a place where food is served. These are environments, often called Development (dev) and Production (Prod).

• CI/CD

Moving food from the kitchen to a guest’s table is the process of delivery. It requires integrating food from different parts of the kitchen (protein, vegetables, starch, etc.) onto the same plate. Then that plate goes through a series of checks before it’s delivered to the table. This is called Continuous Integration (CI) and Continuous Deployment (CD).

In the coming days, I’ll go into more detail about how these operate, why they are important, and how you can take your first step.

It was good to see today,

Sawyer

from The Data Shop

Here's your chance to "Draw insights from peers and experts in the field."

Registration for the 2nd cohort of the Technical and Strategic Data Leader is now open!

Spots are limited. We have to restrict the cohort size to give you the best learning experience and maximum engagement from the mentors.

Several spots have already been filled by the early bird waitlist!

Why are people joining the Technical and Strategic Data Leader?

Here's what cohort members are saying about why they joined:

"Draw insights from peers and experts in the field."

"Collaboration with fellow cohort members, strategy sessions, and learning from the collective wisdom of other who may have had

similar challenges in the data space."

"Continuous improvement, networking, and peer-to-peer collaboration"

"I believe the course will help me discern strategic objectives for my career as I take on more leadership responsibilities in my role."

"Curiosity, exchange of experience, inspiration"

Block your calendar!

The next cohort runs July 16th-August 20th on Tuesdays from 9-11 am PT / 12-2 pm ET.

We are opening registration 2 months early so you can block your calendars out now. This is an intensive cohort. We meet for two hours and there are a variety of synchronous and asynchronous discussions that will happen throughout the six weeks.

Why run a 2nd Cohort?

We learned some things from the first cohort. The biggest piece of feedback we heard? People want more discussion and Q&A time. We don't want to cut back on the content, but we are building different ways for you to interact with your peers and mentors both during sessions and in between. I'm excited to share more details as we get closer.

What else did we learn? Strategy Sessions were enormously beneficial. One manager identified and implemented a large technical upgrade based on the recommendations and guidance from Strategy sessions. Another Director used the sessions to take a critical look at their team's processes and bottlenecks - and get valuable clarity about their challenges.

It was clear - the cohort members who joined at the Silver or Gold Tier were positioned to launch from the cohort in significant ways.

But the main reason we are running a 2nd cohort - every member of the first cohort shared valuable ways the content, community, and commitment of being in the cohort empowered them to lead better with data.

That's why we knew another cohort needed to happen.

Register now!

​Click here to register.​

We were 25% full ​after the first 4 hours of waitlist registration! Starting right now, registration is open to the general public! I don't know how long these remaining spots will last.

I'm here,

Sawyer

from The Data Shop

"I would absolutely recommend this"

The next cohort of The Technical and Strategic Leader is here.

What is the Technical and Strategic Data Leader?

The Technical and Strategic Data Leader is a six-week cohort-based learning experience with 12+ hours of live content. With an intentionally small cohort size for you, we’ve designed the experience to allow for ample interaction and Q&A with mentors and cohort peers.

Not just a technical training course (those are everywhere). Focused on the integration and intersection of business and technology for Data Leaders.

Not just content. A learning experience focused on collaboration and conversation with industry peers and mentors.

Not just another webinar.

Over 6 weeks, you will have focused conversations about Data Team strategy, Business Intelligence, Data Architecture, and Data Leadership.

What should I expect?

Content geared specifically to equip data leaders. Here's what the course outline looks like

• Week 1 - Intro and Foundations of Great Data Teams: Purpose and Strategy

• Week 2 - Data Architectures: Understanding Common Data Architecture Concepts

• Week 3 - Data Architectures: Gain a working understanding of several data architectures

• Week 4 - Foundations of Great Data Teams: Part 2

• Week 5 - Power BI Architecture: Best Practices in Power BI Administration & Governance

• Week 6 - Managing Power BI Developers: Standardizing requirements gathering, UI/UX design, and the dashboard creation process

We might even throw in an additional week of bonus content.

Registration:

Registration opens Monday, May 13th at 8 am ET

The link to register will be waiting in your inbox.

Dates:

The next cohort is July 16th-August 20th on Tuesdays from 9-11 am PT / 12-2 pm ET

This is an intensive cohort. You will get the most out of this cohort if you have the time to invest in the experience. In addition to the two-hour meeting each week, a variety of synchronous and asynchronous discussions will happen throughout the six weeks.

Block your calendars now.

Company Support:

Over 80% of cohort participants received support from their company for some or all of their registration fees. Connect with your leadership today about supporting you in your growth as a data leader. This cohort is designed to accelerate your growth as a data leader and launch your team to the next level for delivering value to your organization.

Why wouldn't your company want to pay for that?

Limited spots:

We are only opening up 12 spots in this cohort of TSDL. I really don't know how long those spots will stay open.

Should you join?

Here's what past cohort members said:

"I would absolutely recommend the TSDL to anyone who is struggling with the challenges of maturing the data practice inside of an organization. The content is relatable and relevant to the issues data teams face today." - Cole

"Yes, the TSDL was a great experience where I received a professionally curated education on various topics data leaders need to know." - Javier

"Yes, to those who need a high-level understanding of the strategic place of data in an organization" - Daniel

"Yes, at the very least they will make connections with other data leaders even if they don't get anything out of the course" - Robert

"Yes, I found the course very informative and quick-paced. Good value for the money." - Brendan

👆 That's better than anything I could have said.

I'm thrilled you are here. I'll see you Monday morning when registration opens!

Sawyer

from The Data Shop

When you walk into an executive's office with a key data initiative, they listen.

Once you step into data leadership, your problems become less about technology.

The technology questions are still challenging, but even when those questions are answered, you might be stuck. The best technology and data solutions hit brick walls at the same key point.

Lack of organizational buy-in.

That brick wall shows up in dozens of ways. No budget approval. Excessively long budget approval. No availability for stakeholders to meet. Uninterested executive leadership. Refusal to consider new tools or solutions. Frustration with you when you try to introduce something new. Resistance when you pick a solution that wasn’t their first choice.

And on.

And on.

It goes.

Being an effective data leader means taking on technical challenges, in the context of an entire organization or other teams, technologies, leaders, products, projects, and customers.

This is why you must start by understanding the function, strategy, and purpose of data within an organization.

Fundamental concepts about why your organization exists, what purpose data plays, how incentives create or reduce friction, and how you move your data team into strategic alignment.

Then, you are empowered with a strategic perspective on data in your organization.

So when you walk into an executive's office with a key data initiative, they listen.

-------------

This is where we start the ​Technical and Strategic Data Leader.​ And these concepts flow through every conversation we have throughout the cohort.

Here’s what Daniel (a Solution Architect) said after our first cohort:

“I found that it helped me come up with the talking points for the next time we need to explain data projects to the senior leadership. I recommend the TSLD to those who need a high-level understanding of the strategic place of data in an organization”

You don’t just care about data technology. You care about data influencing and improving core areas of your organization.

Join us for the next cohort of starting July 16th and ending August 20th.

Expert perspective, intensive learning environment, collaborative experience.

Registration opens next week.​ Get on the waitlist now​. Seats are limited.

I’m here,

Sawyer

from ​The Data Shop

There's no such thing as a purposeless data team

“Nature abhors a vacuum” - The Philosopher (when you are as famous as him, you get titles like that)

If you leave the purpose or mission of your team undefined it won’t stay that way.

The empty space will be filled.

If, whether on purpose or accident, you fail to define for your team why they are here and why they matter, don’t worry. Your team is great at coming up with a purpose of their own.

They will likely adopt numerous purposes.

• Jane’s purpose is to get a promotion and she will do whatever it takes to get it.

• Bruce’s purpose is to relax and do as little work as possible.

• Ravi’s purpose is to learn some technical skills before jumping to another company for a pay raise.

• Annette’s purpose is to maintain her relevance in a rapidly changing technical landscape.

They’ve each adopted their own purposes, that best suit their needs and interests. Overall organizational objectives be damned. All of these people will place their individual purposes as top priority for the team - thereby filling the empty space.

There’s no such thing as a purposeless team. But without a singular purpose defined by the leader, you will end up with a multi-purpose team.

And multi-purpose teams face constant infighting, lethargy, and turnover.

Do you know what the purpose of your data team is? More importantly, is the vision of that purpose compelling and clear enough that the individuals on the team no longer jostle for control to have their purpose on top?

Know where you are, know where you are going, and take people with you.

That’s what leadership boils down to.

​The Technical and Strategic Data Leader focuses on solving this problem for leaders. Why does the data team exist and how do we best execute on that purpose?

If you feel frustrated, lost, or exhausted on your Data Leadership journey this is the Gatorade at Mile 20 of the marathon.

Over 6 weeks (and 12 hours of live content and discussion), you will understand:

• Why data exists and how it functions in an organization?

• What kind of data architecture and platform will best serve that purpose?

• How do you build and execute on that purpose and platform.

Spots are limited (only 12).

Registration opens on Monday.

​Get on the waitlist to get early access.​

I'm here,

Sawyer

from The Data Shop​

You don't have to be alone. Being alone sucks.

What do these things all have in common?

• The Buddy Bench at elementary school recess.

• Networking event at the local Chamber of Commerce

• Meetups for board games, hiking, yoga, or book club

• Therapists, counselors, and coaches.

• Potluck lunches after a religious service.

They exist so we aren’t alone. Or if we are alone, they make sure we don’t have to stay that way.

Because feeling alone sucks.

Yet, you feel this way constantly in our work context.

You wonder:

• Are my challenges unique to me?

• Does anyone face the same frustrations that I do?

• I’m not sure I know what I’m doing. I’m probably the only one.

• Who else could understand what I’m going through?

• Maybe I’m not cut out for this.

• Is this normal?

These are the things I hear from data leaders every week.

You don’t have peers inside your company to talk with. No one who understands both the technical side and strategic side of your work.

What happens when you connect you finally connect with peers?

• You no longer feel alone.

• Others can empathize with your experiences

• You feel more confident about your skills and work.

• You know where to go for support when you need it.

• You realize you, your challenges, and your frustrations are normal.

​The Technical and Strategic Data Leader is built for you.

So you don’t feel alone.

Here’s what a cohort participant from the last session said:

“The biggest value of this experience was connecting with other data leaders that have either been through the things I am currently dealing with or are currently sharing the same struggle”

Community. Confidence. Empathy. Connection.

This is why we are opening up a second cohort for you this summer. For six weeks starting July 16th and ending August 20th, join an intensive learning experience with industry peers.

Registration opens soon. Get on the waitlist now. Seats are limited.

I’m here,

Sawyer

from The Data Shop

You are getting the exact results you designed for

They (whoever they are) say: “Culture eats strategy for breakfast.”

You can view this in two ways for your data team.

Either view it as an excuse for why your strategy isn’t working

Or you view it as an opportunity.

Your team is far less concerned about strategy than they are about culture.

Your culture is the beliefs and behaviors of your people. How they think, act, and respond in each situation.

The culture of your team right now is designed exactly the way it needs to be to get the results you are getting.

If you want different results. It’s time to design a different culture.

I’m here,

Sawyer

from ​The Data Shop

Here's the thing leaders are lacking (hint: it's not just about data)

More important than data literacy.

than tech stacks

tech budgets

or org headcount.

While leadership might lack any skills, priority, or growth in any of those areas, that's not the problem.

An organization that takes data seriously doesn’t need to spend time on data literacy courses.

Or waste money buying new tech tools.

Or splurge on hiring the best and brightest.

The gap, for both data leaders and executive leaders, is a coherent model for decision-making.

They are constantly making decisions. About what to buy, sell, grow, disband, adjust, scale, prioritize, and eliminate.

How does data play a role in your decision-making framework?

Most leaders trust firmly in their experience and the expertise of the leaders around them. But it leaves them exposed to a variety of biases, blind spots, and bad incentives.

An organization that prioritizes data will

→ Know their outcomes

→ Make decisions that close that gap on their outcomes

→ Use data to reduce the uncertainty and risk in the decision.

If you want data to be meaningful to your org connect it to the decisions that need to be made. And connect those decisions to the ultimate outcome your organization wants.

I’m here,

Sawyer

from ​The Data Shop