The Data Daily
Less than 2 minutes to read each morning.
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5 days a week since May 1st, 2023.
Our data team is swamped
“Our data team is swamped. We need to hire”
Hiring is a hammer that sees everything as a nail. It’s often a lazy solution to the real problems.
Before we add more analyst to the team, you have to answer a key question.
Why is your data team swamped with reports and data requests?
Many data leaders I know will be curious like this:
Maybe there are too many ad hoc reports sent their way without any filters.
Perhaps there are other reports available that could meet this need.
How long will this report be used for?
But far fewer data leaders ask these questions:
Are we swamped because the reports are taking a really long time in development?
Because of bad data quality?
Because of minimal processes?
Because of poor requirements gathering?
Because of lacking skills?
Adding more headcount to your team can only irritate and increase these issues.
The beliefs and behaviors of your team are more foundational to the success of your team than the number of engineers and analysts.
Do you need to hire? Perhaps.
Do you need to adopt practices and processes that will allow your team to execute better? Always.
I'm glad you are here,
Sawyer
from The Data Shop
Are you making progress?
You might have a clearly defined end goal or business outcomes, but how do you know if you are making progress?
When business goals are shaped and achieved over quarters or years, how can you check progress from on the ground? Amid the work and grind, how do we know if we are making progress?
From the Agile Manifest: "Working software is the primary measure of progress."
I interpret “working software” to mean two things.
It’s in the hands of users. It’s shipped. Deployed. Available for use.
It does what the business needs it to. It’s solving a problem, providing insight, or enabling a workflow.
That might be modified/refined slightly for data teams to be something like: "Working data products are the primary measure of progress."
Your north star is business outcomes. That's the only way that we can ultimately be successful.
But on-ground progress very often is as simple as shipping useful data products.
I’m here,
Sawyer
from The Data Shop
Quit
You should quit.
But you should do it for the right reasons.
Most of us quit for the wrong reason.
We quit based on the present vs the future.
Most people quit based on the difficulty of present circumstances.
It's hard. Too hard. So we quit.
Instead, you should quit based on future circumstances.
Is the likely outcome of this work still desirable?
Look a couple of weeks, months, or years down the road. Be honest and realistic about the likelihood of the outcome.
If the likely result of your work is still an outcome you want - then push through and work hard.
But if the future prospects look grim or the likely outcome no longer matches what you want, you better quit.
That data project.
That career path at a company.
That education or training initiative.
That data product idea.
Quit Now.
As soon as possible.
You have too little life and energy to waste working toward something you don't want.
I’m here,
Sawyer
from The Data Shop
Just the facts
“Just the facts ma’am”
We think about “cold hard facts” when it comes to numbers and data.
That’s what the executive claims to want.
That’s what we often assume we deliver.
“Just the facts ma’am”
Sounds simple, right?
But then someone asks how many active customers we have.
Or what our donor churn rate is.
Or what percent of our donors are new this year.
Or how effective our content distribution programs is.
And the “cold hard facts” turn into - “it depends”.
Or more likely, three or four (or more) different answers based on which department you ask.
This is the soft side of data.
To answer any of those above questions (or any number of permutations), things will get much softer. With more nuance. With an understanding of the history of the question. And a prediction for how the answer will be used.
“Facts” are rarely a land we get to play in.
We tell stories.
We shape data in and around humans.
And those results are rarely in cold, hard, or simple facts.
I’m here,
Sawyer
from The Data Shop
The side effects
If you start connecting your work to business outcomes, prepare for potential side-effects.
Like the end of the pharma ad for the newest life changing pill, here’s your disclaimer about what you might encounter.
As you continue this journey of connecting your work to business outcomes you might experience
renewed purpose
clarify of priorities
increased connection with business teams,
improved support and sponsorship from leadership.
But also as your data team helps achieve business outcomes, leadership will begin to view your data team differently.
The accountability shifts.
Rather than being accountable to
“keep the lights on”
“respond to user tickets”
“collect and deliver business requirements”
(which is how they used to view you).
And they will begin to hold you accountable to outcomes.
Now leadership realizes what a data team can really do to accelerate business goals.
You’ve started telling a new story to leadership and it sounds like this: “here’s how we contributed to these key business outcomes.”
Don’t be surprised when the expectations flip, and they start asking you: “How are you contributing to these key business outcomes?”
Pretty soon, budget, headcount, technology initiatives, and access to “be in the room” is dependent on that question.
You’ve been warned. If you begin delivering on key outcomes that move the business forward.
Leadership will like it.
And they will expect more of it from you*
I’m here,
Sawyer
from The Data Shop
*That’s where we all want to be anyways. So maybe it’s a feature and not a side-effect.
A data leader might
The only thing worse than being lost…
is not knowing if anyone is looking for you.
Data leaders can feel
…isolated from other business teams.
…unsure about the success of your team.
…insecure about the quality or meaningfulness of your work.
…fearful about cost reductions and layoffs.
…skeptical about the difference data makes in your company.
…stuck feeling like you can never do enough.
…misunderstood by their leadership.
You might feel lost.
Leaders, perhaps more than others, are prone to cycle through these feelings throughout their days, weeks and years in leadership roles.
The only thing worse than feeling isolated, fearful or insecure, is not knowing a way out.
You might feel lost. And wonder if anyone is looking for you.
Start 2024 acknowledging if any of those feelings are yours.
And embracing that people are looking for you. And ready journeying with you.
Connection and community are waiting.
I’m here,
Sawyer
from The Data Shop
p.s. I’m building something to create and empower data leaders. Announcement coming next week. If you a data leader and want a sneak peak at what’s coming, hit reply.
The business doesn’t want
The business team never wants a dashboard, pipeline, or ML model.
If you are asking them what dashboard you should design then they will often be wrong.
If you ask them what metrics they want in an ML model they will often be wrong.
If you ask them what data sources they need incorporated in the data model they will often be wrong.
But.
If you are asking them what the business problem they need so solve is then they will very often be right.
If you ask them what decisions they need to make. And what information would help them make that decision, then they will very often be right.
If you ask them what the biggest barrier is to hitting their business goal this quarter, they will very often be right.
If you are frustrated that “the business doesn’t know what they want”, then it might very well be the case that you are asking about the wrong wants.
Requirement gathering fails when we stop asking questions.
I’m here,
Sawyer
from The Data Shop
From scratch
Best case scenario?
You architect your data platform before you even think about technology.
Architecture is strategic and visionary before it's technical. Design first for your needs, use cases, and growth.
Then take an open mind to technology solutions, what's currently available, cost considerations, and talent gaps.
In reality, you will have numerous constraints and presuppositions about tools and tech stacks. But the more you approach architecture as a tech-agnostic exercise, the better chance you have of architecting around the business instead of your favorite tech tools.
I’m glad you are here,
Sawyer
from The Data Shop
The journey
It's early in 2024 and new adventures are ahead.
If you are new to The Data Daily, here’s a bit about my journey.
2011 - Finished college (Religion/Theology Degree)
2013 - Began career in business operations and logistics at a small travel company (<100 employees).
2014 - Finished Master's degree (Religion/Theology Degree)
2015 - Was hard coding values in the Excel files for a project for my boss. He sent it back and said “learn how to use Excel formulas. This is awful”. Tried to learn Excel formulas for the first time.
2016 - Realized how much I loved business, strategy, innovation, and leadership. But didn’t think I had the skills or capabilities to have a career in this world. I thought the only path was more schooling (MBA).
2017 - Found a job as a software support analyst. Learned Select * From for the first time. My boss/mentor kindly forced me into more technical work each month.
2018 - First Data focused role, building reports and sales analytics for a media company. Began data consulting on the side (Google Data Studio FTW)
2019 - Began full-time data consulting with a Microsoft Partner firm. Wrote a lot of bad code. Had great architects who showed me a better way.
2021 - Accepted a Data Engineering role at Microsoft. After 6 months shifted to a Sr. Consultant role working with Microsoft customers, designing and building data platforms.
2023 - Feb 1st purchased TheDataShop.co. On Feb 14th, submitted the paperwork to incorporate The Data Shop LLC in Michigan.
2023 - May 1st began writing The Data Daily. This is the 180th Edition.
2024 - Focused on helping nonprofit data teams using Microsoft and Azure increase the value delivered to their communities.
Why am I telling you this?
I feel insecure about so many parts of my journey.
I don’t have enough experience. I’m not capable enough.
My career path doesn’t make any practical sense. I switched directions a lot.
I wasted years not knowing what I wanted to do.
I’ll never be as knowledgeable as people with a computer science background.
My company is so new, no one will want to hire me.
I’m also telling you this because:
There are parts of my journey that didn’t go as planned. But I like the direction I’m headed.
I’m don’t know everything, but I’m learning as much as a I can everyday.
My journey was wide ranging, but that diversity of education and experience make me the data professional I’m today.
The years spent studying religion/theology and years working in travel operations make me a more integrated data consultant.
Changing my mind at times has been the most honest act I’ve done.
The Data Shop is a baby company, but that’s the only place I could start. And it’s the best place to start.
Thanks for being here.
Sawyer
from The Data Shop
No data team
Pre-s:
In just a few weeks I'm launching a Data Leadership course designed for people just like Data Leaders, Data Architects, and those response for data at their company. .It's still under wraps but I want to get your feedback before we finalize the details.
Hit reply if you want to hear more.
-------------------
What if you don't have a data team?
Your organization is small. Maybe growing fast, maybe not.
But the emails I write, and the articles you read about “Data teams” fall flat for you.
What if you don’t have a data team?
What if it’s just you? And what if it’s only part of you because you have two other primary job functions outside of data?
Three pieces of advice.
Start small. There is so much you could do for your business with data. You will likely feel lost and overwhelmed trying to build a Data Warehouse. So don’t. Pick the smallest function, activity, or idea you can. There is no data use case too small to start with.
Start with what you know. There is much to learn, and you could quickly land in tutorial purgatory. Start with the tools you know and the business function you know. That might be Excel or Google Sheets. That might be finance or operations. Don’t attempt to learn a new technology or new business area - you will learn plenty just by trying to perform a data task with a known tool in a known domain.
Start to ask. If you are reading these emails you probably have an itch to work with data more. To do that, you will likely need to ask for budget and time. Some budget to buy a tool or training course or maybe one day hire someone. You will need time in your schedule, which will require reprioritizing areas of your job function. Based on your company, one of those asks will feel easier - start there.
Most companies don’t have a data team. Over half of US employees work at a company with less than 100 employees. At companies that size, formal data teams are a rarity.
It can feel lonely pursuing data by yourself at your company.
You aren’t alone.
I’m here,
Sawyer
from The Data Shop
The fastest path to disappointment
The fastest way to disappoint someone is to not give them what they expect.
The most common way that happens by not telling them what to expect.
Most of us will accomplish what we intend to do.
However, most other people around you have visions and dreams that you play a role in.
And mapping their vision of the future to what you intend to do, is the fastest way to put a smile on someone’s face.
If customer satisfaction is important to you
then sharing expectations
early and often
is baby steps.
I’m here,
Sawyer
from The Data Shop
How vs. Why
“How” and “what” are very flexible. “Why” rarely is. Partner around the why.
Some things don’t change.
In data projects where you are working with business stakeholders, the how and what are often very flexible.
How should this solution be built?
What are the elements that should be included in this dashboard?
How should the metric be designed?
What technology should we use?
If you focus your relationship, requirements, and communication with the business around the “how” and “what” you will often face resistance, confusion, and frustration.
Instead, partner with the business around the “why”.
There is a business objective that led the business team to come looking for data.
And that “why” is rarely going to change.
Aim to understand the why, and your path to success becomes much more flexible.
But, focusing on the why, requires asking business questions. Not data questions.
It requires business literacy. Not data literacy.
It requires empathy. Not transactional relationship.
Get clear on what doesn’t change. And you will have a lot more fun with the things that do change.
I’m here,
Sawyer
from The Data Shop
p.s. Scope creep primarily shows up when you partner around the “what” and “how”.
The thing about scope creep…
The thing about scope creep…is it going away when you have clear outcomes.
Scope creep is a boogeyman for data teams. It’s a constant point of fear and tension between stakeholders and data teams.
Fear that they will move the goalposts.
Fear that they will sneak more work into the project.
Tension because you will push back on their ask.
Tension because you don’t know who to trust.
Scope creep goes away when both the data team and the business team have the same desired outcome.
When that’s the case…
no one is trying to do as little work as possible.
no one is trying to sneak in their own agenda.
everyone agrees on the goal and will do what it takes to get there.
everyone agrees even if the scope changes the goal won’t.
Instead of spending hours in training seminars learning how to manage scope creep, instead try this.
Get really clear on what the goal is.
And you might find that the boogeyman you feared
isn’t that scary.
Sawyer
from The Data Shop
Don't be scared of Excel
In the software world, we embrace the phrase “Minimally Viable Product” or MVP.
It means you focus first on building a product that does the minimum requirements. It has the core features so that a user can find value in it. But has zero features, bells, lights, or widgets.
For a company just beginning a data journey, the goal is an MVP. Just enough to make things work. Just enough to start making your data useful. In most cases, this means Excel or Google sheets.
The data world loves to shame teams or companies who use Excel as a database. Because it doesn’t have the features, bells, or widgets that a database, or data warehouse does.
But if the goal is for data to help make the company work well, then Excel might be the perfect place to start. Don’t be scared of a company that uses Excel as a database. Be worried if you company won’t ever look beyond Excel as a database.
It’s time to start somewhere with data. And Excel is a great place.
I’m here,
Sawyer
from The Data Shop
Design the whole, build the small.
What do you do with your big ambitious in your data team?
A New Year can fill you with exciting plans. Large scale projects to launch. Innovative initiatives to kick off.
Often, these ambitions can encompass multi-year timelines.
Two common points of failure I see along the way:
Build the whole thing.
Build lots of pieces.
Build the whole thing often fails because the train runs out of steam. Eighteen months after the Enterprise Data Warehouse project began there are no useable pieces yet. The developers are growing tired. The stakeholders are tired of waiting. And finances is done writing checks.
Build lots of pieces moves quickly and gets the users key use cases early and often. But with five teams building out individual use case solutions across the business, the long term attempts to integrate fail. It ends 18 months later with a fractured ecosystem thats painful to maintain and tempting to burn down.
The stakeholders want something useful sooner than 18 months. And in 18 months you want to have an integrated and coherent foundation.
Try this with your ambitions instead:
Design the whole, build the small.
Design and plan a long-term roadmap that will accommodate the larger business. But you build very small to start. Wholistic design with phases designed to deliver in weeks rather than quarters or years.
Keep your big ambitious.
We need your leadership and your dreams.
I’m glad you are here,
Sawyer
from The Data Shop
That’s risky
We are risk adverse without vision.
And risk seeking with a clear vision.
If, when an opportunity, idea, strategy, or tactic is thrown on the table, the people around the table say things like…
“That’s risky”
“Think of what could go wrong”
“I’m not sure I’m willing to risk that”
Those statements are likely a symptom of fuzzy vision. A lack of direction.
An idea feels far riskier when aren’t sure if the end result is worth it.
An opportunity feels far riskier when you have several competing outcomes in view.
A strategy feels far more risky if we aren’t clear of the result we want.
Teams that have a clear vision of where they are headed and what outcome they are all working towards. So when an idea shows up that is focused on pushing them closer to that outcome they are far more likely to say…
“thats worth it”
“Go for it”
“I’m all in”
Because when the outcome is what everybody knows about, and everybody wants, taking a risk is just part of the journey.
Sawyer
from The Data Shop
Don’t stop at a dollars
Value needs to be connected to people's lives. That sometimes looks like money. But a lot of times it doesn't.
Data people talk a lot about value.
Here's what it means:
…
Value is the benefit distributed to the people and places that engage your organization.
…
Let's break it down:
Benefit = Positive stuff that people want. Money. Education. Healthcare. Safety. Housing.
Distributed = It leaves the company. And goes out to others.
People and places = Employees, customers, shareholders, members, community members, students, etc.
Most definitions of value are focused on dollar figures. Profit, revenue, cost, and risk.
But an understanding of value should be far more wholistic than that.
Don't stop at dollars. Value is way deeper and richer.
Sawyer
from The Data Shop
You can only prioritize work
You can only prioritize work if you know the desired outcome.
The only way you can know what the desired outcome for the company is by talking to the highest-level leader you can.
This is how you deliver value.
Otherwise, you will remain...
frustrated at the amount of things there are to do
longing for the weak to escape the grind.
feel scattered and directionless.
disappointed in your progress
in support ticket purgatory.
When you feel like you don't know what to do next, stop and ask if you really know what the ultimate outcome is.
Sawyer
from The Data Shop
100% of kittens
For 14 months I lived with my wife and kids in a basement apartment of a large home in country. We were building a house, and it was a great landing place for us.
The owners of the home had a couple cats that lived on the property. During our stay there, there were four litters of kittens born (the original two cats, and then their kittens had kittens). Our kids loved playing with the tiny kittens and watching them grow up.
The last litter was born a few weeks before we planned to move out. Right about the time the kittens would be ready adoption is when we would be moving into our new home.
My boys put on all their best charm to convince us to adopt one.
My wife repeated a single refrain: “100% kittens turn into cats”.
Meaning, we might love the fluffy cute kitten, but it will (very soon) turn into a not-quite-as-adorable adult cat. And while we all loved the kittens, none of us were quite as excited about owning a cat for years to come.
While our kids (and maybe dad) was caught up in the emotion of a very fluffy, cute, and playful kitten, my wife provided a wise long-term perspective. Pointing out the obvious to us - that kitten won’t always be a kitten.
New data tools and technologies bring a similar level of excitement to data teams. The shiny logo, great marketing, and spinning up the tool for the first offers an endorphin rush.
This new tool/platform is great and will solve so many problems.
But my wife’s wise words apply here too: “100% of data platforms accumulate tech debt”.
Even the industry leading tools are prone to code bloat, dashboard Armageddon, and a proliferation of SQL queries running wild.
Upfront, before committing to the tool with all its excitement, commit your attention also to the tech debt that is likely to follow.
What are the plans to mitigate the tech debt.
And is this tool worth the inevitable debt that will develop.
I’m here,
Sawyer
from The Data Shop
The higher you go in leadership
The higher up you go in leadership the more you are accountable for outcomes not activities.
The most common way we measure progress is activities.
Occasionally we measure progress by outputs.
When you are an individual contributor or newer manager this is the default.
It’s what you put on your performance reviews and resumes. It’s often the primary way you are held accountable. How many hours billed, how many story points delivered, how many commits, how many presentations, etc.
But the higher up you go in leadership, the more the focus shifts to outcomes. It has to.
The VP, CIO or Executive Director can’t measure performance based activities or outputs.
So, if you want to be promoted to VP, CxO or Director
Or if you want your budget to be approved by a VP, CxO or Director
Or if you want to sell a service or product to a VP, CxO or Director.
Then you better start thinking and talking in outcomes.
Speak the language that will help them hit their goals.
I’m here,
Sawyer
from The Data Shop