What are they looking for?

How can you make it easy to find what they are looking for?

Petoskey stones are a local passion and curiosity in Michigan. A passion and curiosity shared outside of Michigan only by enthusiastic rock collectors.

For the uninitiated, Petoskey Stones are a unique fossilized coral that washes up on beaches along the northwest section of Michigan.

This summer my wife and I spent a day at one of these beautiful sections of Lake Michigan. Throughout the day we had seen people wandering up and down the beach looking for these stones amongst all the other scattered rocks on the beach. No one seemed to be finding anything.

We tried the same. Wondering around on the sand and inspecting any rock that looked promising. We found nothing.

Then we took a step into the water and started sifting through rocks sitting in a few inches of water.

We quickly found our first stone. Then another. By the end of our day, we had around a dozen Petoskey Stones.

Here’s the thing: Petoskey stones look like normal limestone when dry. It’s only when they are wet (or polished) that the beautiful fossil pattern appears. Trying to spot them among the dry rocks on the sand is looking for a needle in a haystack.

We were looking in the wrong place for value. It would have taken 2-3x the effort to find what we wanted looking among the dry stones. We might have found one or two. But it’s difficult to predict the outcome or when we might find another one.

The role of the data team is to throw water on the rocks for your business teams. To not just know that they want rocks, but specifically what kind of value they seek and where to find it.

Delivering a pile of data in front of your business stakeholders and telling them “It’s in there somewhere” leads to unpredictable outcomes and unhappy business teams.

It also leads to data teams muttering complaints about a lack of "data literacy".

Pay attention to what your business wants and understand your data well enough so that you can show them the unique places where that insight is found.

Instead of making them sift through identical limestone

you can splash water across the rocks

and find a beautiful Petoskey Stone.

I’m here,

Sawyer

from The Data Shop​

Whose literacy?

I hear it from data people all the time. “The business team doesn’t understand data. They are data illiterate.”

I hear it from the business people all the time. “The data team doesn’t understand our business. They are business illiterate”

If you wish the the business team had more data literacy.

Just know

That they wish you had more business literacy.

And unless your company actually makes money with data, then understanding the business is preeminent.

And maybe the reason the business doesn’t understand your data.

is because the data doesn’t make business sense.

I'm here,

Sawyer

from ​The Data Shop

Where growth hits you

You are growing.

Congrats!

Pop a bottle. Light a cigar. Or pour yourself a glass of milk.

When you’ve savored the moment a little bit, you will come to terms with how growth is straining, stressing, or cracking your data team.

Here are four ways growth knocks your data team off balance:

• As business units grow the demand for analytics, reporting, and data will grow. Developing a mature business intelligence plan will help your team scale the insights and data products you can deliver.

• New business divisions and software platforms mean new data sources. ETL processes that can scale to integrate with new systems rapidly will quickly determine whether your data team is drowning to thriving.

• When a production pipeline fails more people notice. Downtime becomes more costly as more people become more dependent on your data. Having change control and DevOps processes established can minimize downtime.

• The team that got you here may not get you there. The number of seats and arrangement of the chairs changes when rapid growth hits. Determining how talent and team will adapt to growth is crucial.

You might be moving so fast you don’t see these things. Until you hear (or read) them out loud.

If that feels familiar.

If you need a sounding board,

someone to vent with,

or a glass of milk.

It’s good to be back,

Sawyer

The Consumer Driven Method for Effective Dashboard Development

Thanks again to Ahmad Chamy for sharing his knowledge and insights this week. This is the final installment of his series on effectively communicating data. Check out the emails from Monday, Tuesday, Wednesday, and Thursday​

I'll be back on Monday. Have a great weekend.

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In the past, analysts developed dashboards following the waterfall project management methodology. A stage can only start once the prior stage has been completed and each stage is generally not revisited once it is complete. Given the impact that data can have on people and an organization, the lack of feedback and inflexibility to change makes the waterfall methodology less than ideal for data projects that involve an end user interface.

An agile or iterative approach works far better for dashboard development projects. A tried-and-true agile method is to decompose large dashboards into stand-alone reports that can be deployed as working components of the larger dashboard.

Why is this method so important?

• Deliver Small Wins: Clients shouldn’t wait to the end to provide feedback. This allows for the development team to pivot in their work as feedback loops occur.

• Feedback is Key: Clients want to feel like their needs are being heard. Leveraging the agile methodology creates opportunities for them to have visibility into the development process and offer crucial feedback along the way.

• Development silos lead to dashboard disasters: Often, what developers hear from the clients vs. what they build are two fundamentally different things. Rather than arriving at the destination and realizing that you were off mark, seek feedback along the way and incrementally pivot.

By looking at a dashboard as a series of sub-reports, we can incrementally develop them sprint by sprint. Each report would follow a sequential process as seen below. But the dashboard at large will be made up of a series of sprints.

Thanks for your reading this week,

Ahmad,

for The Data Shop

Making Data Resonate

This week my friend Ahmad Chamy is taking over The Data Daily emails. He’s the Founder/CEO of ​D Cubed Analytics​ a Healthcare Analytics firm specializing in Power BI and Microsoft suite of technologies. This week he’s writing about how to communicate effectively with data (either in Healthcare or anywhere else). Check out the emails from ​Monday​, ​Tuesday​, and ​Wednesday​.

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Have you ever tried to communicate an important statistic or highlight an alarming trend but it seems to have no impact on your audience?

If so, then your audience might be experiencing a phenomenon called “psychophysical numbering” - whereby they become desensitized to the number as the number gets larger.

An example of this, is an increase from 10 to 20 to feels significant but moving an equal distance from 450 to 460, even though it is the same amount of increase it feels less significant…or “numbing”

Let’s take a look at one more example, 1 billion - 1,000,000,000 is a BIG number. We might think we understand it but that many zeros actually fogs up our brain and make it harder to truly understand. Instead if we said, person X’s net worth is $1,000,000,000 that could translate into, if person X had a full-time job spending $50,000 every day - this money would last him a total of….55 years. By reframing the large number it became palpable and real.

Let's take a real life of example to solidify this concept.

In the 1850s a figure emerged during the Crimean War that would change the course of the war for the British with effective methods of communicating numbers to leaders. During this time the British had formed an alliance with European and Turkish troops to dissuade the Russian invasion of Crimea. For the British the war had been a disaster, they suffered high rates of mortality not from the battlefields but in the military hospitals. The conditions were so dire in the hospitals that wounded soldiers were “left to expire in agony” The Times of London wrote.

Florence Nightingale, 34 at the time came forward and proposed to the army that she go to the frontlines and help in the hospitals. She and her team worked tirelessly to improve the conditions the wounded soldiers were in. Throughout this whole process she was collecting data and gathered evidence that her initiatives to improve the hospital’s sanitary conditions lead to a significant decrease in mortality. Following the end of the War Nightingale was determined to ensure that any future wars would not suffer from the inevitable disorganization. She was calling for substantial reform.

For Nightingale who understood the language of numbers fluently the statistics were clear. But she knew these dry statistics would not be able to motivate and overcome the inertia in the system. The numbers needed to be translated into a more emotional form that would spur people to act. She translated the statistics, in the first 7 month, 7,857 troops died out of 13,095 into

“We had, in the first seven months of the Crimean campaign…from disease alone, a rate of mortality which exceeds that of the Great Plague of London.”

Comparing the death rates to that of the Plague, an unforgettable historical event for Londoners made the numbers more concrete and vivid.

Ahmad

For ​The Data Shop​

p.s. If you find this topic as fascinating as we do then you'll want to grab this book! Book Recommendation ​“Making Numbers Count” by Chip Heath & Karla Starr

Effective Healthcare Dashboards – a solution to information inundation

This week my friend Ahmad Chamy is taking over The Data Daily emails. He’s the Founder/CEO of D Cubed Analytics a Healthcare Analytics firm specializing in Power BI and Microsoft suite of technologies. This week he’s writing about how to communicate effectively with data (either in Healthcare or anywhere else). Check out the emails from Monday and Tuesday​

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A dashboard is a tool that decision-makers use to rapidly monitor current conditions that require a timely response to fulfill a specific role. Dashboards, as opposed to conventional reports, are action-oriented vs. strictly informative.

Characteristics of effective dashboards are:

• Action Oriented

• Purposeful Visualization

• High Impact

• Interactive

Here are 8 Best Practices for Effective Dashboards:​
​ 1. Leverage Pre-Attentive Attributes: A preattentive attribute is a term for things that people notice without even noticing they've noticed them. In data visualization – especially when dashboarding - this means viewers will instantaneously see specific visual cues designed to leverage attributes of preattentive processing. Some examples of Pre-Attentive Attributes are (1) Color (2) Form and (3) Spatial position.

2. Understand your Audience: Gather Business and Data Requirements using stakeholder and end-use interviews and form personas to appropriately capture the audience you are building your dashboard.

3. Leverage an Iterative Approach to Dashboard Development: Apply agile Project Management principles to tackling dashboard development.

4. Use the Guided Analytics Approach for Dashboard Layout: Understanding your audience’s mental models and building dashboards that follow their decision-making process will mean your dashboards will be used and not be shelved in your data platform.

5. Appropriately Select Visuals: There are lots of data visuals to choose from. Understanding how quantitative and qualitative data should be visualized is crucial.

6. Design User-Friendly Charts: Eliminate Chart Junk and build simple yet impactful data graphs.

7. Avoid Data Visualization Pitfalls: Learn how data visualizations can distort and skew data findings causing confusion to avoid them.

8. Set Goals for Dashboard Benefit Realization: Dashboard development projects’ success does not just depend on the delivery of the product but rather the active use of the dashboards in decision-making. It's important to have strategies to train users on the dashboards and to measure the adoption and usage of these dashboards.

In tomorrow’s post, we will talk about some strategies for making your numbers more impactful and tactics to ensure data is properly understood by your business users.

Ahmad

for The Data Shop

Why Data Visualization Matters

This week my friend Ahmad Chamy is taking over The Data Daily emails. He’s the Founder/CEO of D Cubed Analytics a Healthcare Analytics firm specializing in Power BI and Microsoft suite of technologies. This week he’s writing about how to communicate effectively with data (either in Healthcare or anywhere else). Check out Monday's email.

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Yesterday we surfaced the issue of information Inundation. Today we will talk about why data visualization is such a powerful tool to deliver data effectively to your audiences.

The Picture Superiority Effect refers to a phenomenon where people recall pictures better than they remember the corresponding words. To put it more simply, pictures are superior to words when it comes to recalling and recognizing information.

For example, a group of researchers at the University of Rochester in 1970 studied the ability of participants to recall pictures by exposing them to more than 2,000 pictures for between 5 to 10 seconds at a time. After three days had passed, the participants could still recall over 90% of the images (Standing, Conezio, and Haber, 1970.)

Not only are pictures more memorable, but their benefits also extend beyond that.

Another group of researchers at Michigan State University examined the effects that pictures had on health-related communications. In their 1966 experiment, the researchers assigned participants either a full text or illustrated instructions on wound care to 400+ patients who visited the ER for lacerations. They found that patients who were given illustrated instructions outperformed text-based ones. (Delp & Jones, 1966)

The patients who received illustrated instructions:

• Were more likely to have read the instructions (98% vs 79% in the text-based instructions group)

• Were more likely to understand the instructions (46% vs. 6%)

• Acted on the wound care advice more often (77% vs. 54%)

Communicating effectively with images and visualization is one of the best tools in the dashboard developers’ toolkit. In fact, communicating with images is six times more effective than words alone. Studies have shown that whereas we remember only 10 percent of what we hear and 30 percent of what we read, we remember a whopping 80 percent of what we see.

In tomorrow’s post, I will share 7 tips for effective healthcare dashboard design.

Ahmad

For The Data Shop

Information Inundation

This week my friend Ahmad Chamy is taking over The Data Daily emails. He’s the Founder/CEO of D Cubed Analytics a Healthcare Analytics firm specializing in Power BI and Microsoft suite of technologies. This week he’s writing about how to communicate effectively with data (either in Healthcare or anywhere else).

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Healthcare executives today spend roughly 85% of their workdays on meetings and phone calls receiving status reports and updates. They suffer from the growing pressure of being inundated with information. If you are an analyst responsible for communicating important information to these executives, you need to be aware of the forces that are working against you and optimize your role and mode of communication.

Dashboards are incredibly useful tools to deliver key information to executives in an effective way...when done right.

A good dashboard meets these criteria;

• Action Oriented

• Purposeful Visualization

• High Impact,

• Interactive.

Stephen Few in his book “Information Dashboard Design” defines dashboards as “... a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance”

A good dashboard helps executives rapidly monitor current conditions without inundating them with unnecessary information.

A dashboard is a visual information display that people use to rapidly monitor current conditions that require a timely response to fulfill a specific role. Dashboards need not be overly creative, nor should they be tabular displays of data.

In the coming days, I will provide specific tips on how to ensure your data dashboards are effective.

See you tomorrow,

​Ahmad Chamy​

For The Data Shop

Jet fuel or system failure

Within the first few minutes on the phone with a potential client, I usually land on some form of this question:

“Why are we talking today?”

That gets to the heart of things pretty quickly. Why are we talking today instead of 6 months ago or 6 months from now? What about your business or data has landed you here?

One theme pops up regularly.

Growth.

Prospect from two months ago: “We are going to grow 3-4x in the next 2 years”.

Prospect from three weeks ago: “Our assets under management are 10 times what they were a year ago”.

Prospect from last week: “We expect to grow 25-30% each year for the next 4-5 years”.

We always stop for a moment and just applaud that progress or trajectory.

Then I return to the same question. “Why are we talking today?” What does business growth have to do with this conversation?

Then we usually hit gold.

“Our data system is beginning to crack under our rapid growth. We are seeing more production failures”

“With the growth ahead we expect a large increase in data requests from new business units. Our team and processes aren’t ready for that”

“We are growing fast, and we have no idea how to leverage data to help us. We are reaching a breaking point”.

Growth is great. But growth has growing pains. And growth exposes weaknesses and flaws.

If you are on a rocket ship of growth, data can be jet fuel or a system failure.

If you are growing and need ideas

for making data into jet fuel

hit reply.

I’m here,

Sawyer

The value on your TV Screen

It’s fall in the USA and American football is back. TV Networks pay around $12 billion this year for rights to broadcast NFL games. College Football is similar. The Big Ten Conference alone receives over $1 Billion a year from TV deals.

TV Network executives aren’t fools. There is a huge appetite for viewing football broadcasts.

The event itself is extremely valuable. Watching the football game live (or very close to live) is the peak experience for viewers that brings in all the advertising dollars and billion-dollar contracts. But, that value quickly expires. While a few viewers want to watch highlights and analysis later, it’s a small percentage of the value available during the 3-4 hour window when the game is live.

Very few fans are interested in rewatching a football game days, weeks or months later. The value is extremely high during the game but with a short lifespan.

Compare that with another form of content on your big screen - a TV series.

The USA Network show Suits originally aired from 2011-2019. The show received only modest attention and it's best described as a niche show from a second-tier network.

However, this summer Suits landed on Netflix and quickly broke streaming records. Industry commentators are scrambling to explain why it’s hit massive mainstream appeal 4 years after its series finale. Some attribute the rise to Megan Merkle (who skyrocketed to fame in 2017-2018 by marrying Prince Harry), starring on the show but that only partially explains the popularity explosion happening now.

The value of a TV series has modest appeal on its initial air date, but the show's full value can extend (and even expand) for decades.

Two types of content on your TV. A football game with immense value clustered around a few hours of air time. And a TV show with substantial potential value spread out over years.

Treating them the same way is foolish.

One offers synchronous and real-time value that has to be maximized and executed on NOW.

The other offers asynchronous value, that can be meticulously mined for a long time.

Your company's data assets follow similar patterns. Massive value expires quickly from your real-time events. Long-term value is available for years from your asynchronous activities.

It’s foolish to treat them the same

It’s foolish to ignore either of them.

I’m here,

Sawyer

Faking the marathon

There are certain things in life you can fake.

Things you can get by at without intentional planning or training.

Say your friend invited you to join them in a 5K charity race next weekend.

If you are an adult in average health with a modestly active lifestyle, you could tell your friend “Sure, why not?”
When the 5K run/walk event comes around a few days later, you might be able to run some of the way. You might have to walk all the way.

It might take you 45 minutes or an hour. You won’t win. You might feel a bit sore the next day. But you could do it with minimal advanced thought or planning.

A 5K is at the “you can wing it” level of planning.

But, if your friend texts you and says “I’m running the marathon next week, want to join me?”
The only correct answer is…

Not​
​A​
​Chance

Because while I could run, jog, or walk my way through a 5K on a week’s notice, my chances of completing a marathon with zero training on a week's notice are 0%. I’d never make the time cut off. I’d slog through a few miles, watch as the crowds of runners (who trained) pass me by, and wake up the next morning with a deep soreness in every leg muscle.

Your data team is used data requests like “join me for a 5K” this weekend.

You can whip it together without too much prep, and be happy with the results. It wasn’t a first-place finish, but hey, it got the job done.

Then your CEO announces aggressive growth goals. The headcount doubles in 12 months. You acquire a competitor in your market. LLMs are here and your data quality is abysmal.

All of a sudden the requests are closer to “Can you run this marathon with me next weekend?” You try to pretend it’s like a 5K and hope you can just wing it.

But you can’t. The data team hasn’t trained for this. Your change control process is scattered. The infrastructure has duct tape in more than a few places.

The best time to start training was 9 months ago. The second best time is right now.

It was good to see you today,

Sawyer

from The Data Shop

Shoutout

The other day I talked about the flavors of data teams.

Scrappy teams, legacy teams, and cloud-native teams. One thing that became clear in the replies from you, is two of those team types get a lot of attention (for different reasons) and the third is often ignored.

The cloud-native team attracts the attention and affection of the sales teams at AWS, GPCP, or Azure. With a dedicated account team standing by always available to help you increase your cloud consumption.

The legacy teams attract the attention of IT implementation firms. There is a lot of work in medium and large migrations and that is where most consulting firms make their money. Years-long projects costing 6 and 7 figures.

But the scrappy team is mostly ignored. There’s no big cloud spending potential. There’s no multi-year migration project requiring a team of consultants.

They are a forgotten fringe of data teams. However, the majority of company data teams resemble the scrappy team.

Detailed and up-close understanding of the size, shape, meaning, and anomalies within their data. Detailed and up-close encounters with the business teams they serve.

This is a shoutout to the scrappy data teams out there.

While ignored by most of the industry, I’m a big fan of your efforts.

You are seen.

And I’m glad you are here.

Sawyer

from The Data Shop

New month friction

We just hit a new month. Along with the calendar change comes data demands.

Many business teams take a breath of fresh air and energy when a new month hits.

They often turn to the data team and ask for the following:

• End of month reporting.

• Refreshed data sets.

• Forecasts for the next month.

• Reconciling data from partial weeks of a month.

• Inspecting month-over-month and same-period-last-year changes.

How long does it take you to respond to these requests?

Does it stir up dread and frustration when end-of-month data requests arrive?

These are subtle nudges at friction.

Sometimes they are neon flashing signs pointing at friction.

Friction in your data processes, data model, team dynamics, data quality, etc.

Friction that slows down your ability to respond to business needs.

Making your data (and the data team) more of a liability than an asset.

If the end of the month is a challenge for your team, take 15 minutes today and identify one point of friction you faced.

Maybe there are many more points of friction. Don’t worry there will be more month ends.

And if you need help. Or a sounding board.

I’m here,

Sawyer​

p.s. If month-end isn’t your struggle, then replace it with the challenging data season for you. New quarter, new fiscal year, new semester, etc.

The opener

Most corporate and career mentoring sucks.

I wish it looked more like the music industry.

Earlier this summer my wife and I went to a concert. It was a smaller show (~1,000) and we bought the tickets because we've enjoyed the artist's music for the last 15 years.

But at 7:30 pm when the show was supposed to start, someone else walked on the stage. NOT the musician whose name was on our ticket stub.

It was the opener.

An opener is an earlier career musician - often an up-and-comer. The opener gets less time (about 30 minutes in case) to play a few of their key songs and try to win over some new fans.

Then the opener said "It's so amazing to be on this tour with [Headliner]. I'm a huge fan of his work and he's been a mentor to me since I was 18 years old"

Think about the opportunity an opener gets:

• to play in front of far larger crowds than they could draw themselves.

• night after night watch an established and successful artist perform

• to travel with the headliner and build a personal relationship.

• make money primarily off the reputation of the headliner

No one attending knew this guy. His name wasn't anywhere on the promotional material. No one showed up for him.

The headliner is very generous to his mentee. Sharing his audience and fan base with an unknown artist. Allowing this up-and-comer to tag along on your national tour and give them the experience to play the biggest shows of their career.

Could you imagine what this would look like for you in your career? Having a mentor let you ride their coattails. Allow you to step into rooms and opportunities far bigger than you could handle on your own. Let you watch them work, day in day out, on the road, through a variety of circumstances. All the while giving you to grow your reputation and fan base.

I'd love to see more "Headliner-Opener" relationships in the business world.

Have a great weekend

Sawyer

Three flavors of data teams

The Scrappy team:

Younger company with modest or fast growth that’s assembled a data team out of necessity. They are small (often 1-5) and don’t have established data infrastructure, budget to hire, or time to take on large data platform projects. They get the job down with Google sheets/Excel, leading BI tools (like Power BI) and maybe a database. Daily work is often filled with manual tasks, managing ad hoc data requests, and generally trying to hold everything together with duct tape. Biggest need is often establishing systems and scalable infrastructure (or getting buy-in from management that data really is important)

The legacy team:

Established companies operating for 20+ years. The data team has several long-tenured team members. On-Prem systems still play a key role in the data foundations, however migrating off legacy systems is a key priority. Often these teams are caught in forever tension between cloud and migrating off old systems (i.e. they are in year 5 of a 2-year migration plan). The struggle between the old and new shows up daily as new team members and a new cloud stack fail to mesh with older tools and veteran employees

The cloud-native team:

They completed their migration or had the chance to build in the cloud from the start. This is often a mature team of 5+ data professionals. They regularly fight against large cloud bills with all the services they have running. This team often finds themselves in a data silo with little natural connection to the business teams and difficulty understanding their problems.

These are flavors of data teams, and many teams are a mix of 2 or 3 flavors - creating their own unique taste profiles.

Where does your data team fit?

What flavor would you add?

Sawyer

How big are data teams?

A couple of weeks ago I ran a survey on Linkedin asking “how big is your data team?”

Over 100+ responses gave me a perspective on the industry I found fascinating.

I’ll unpack the results more here in the future. Here’s a common growth pattern for many data teams:

0-75 employees - Often the first data hire is made around this point. Sometimes it’s an outside hire, other times a technical business user, or data savvy Software Dev steps into this role. As the company grows, the need for operational reports and some basic analytics becomes essential. Data is now someone’s primary role.

75-250 Employees - The data needs to grow beyond one employee. Two or three people are needed to maintain databases, build some core reports, and try to respond to business needs. This might be a combination of IT staff and dedicated data people.

250-1,000 Employees - A formal data team often develops at this stage. A data manager or director is hired for promotion and 2-5 individual contributors fill roles ranging from data analyst, data engineer, business intelligence analyst, and data scientist.

1,000-10,000+ Employees - Multiple data teams form at this point with different focus areas. Data teams could be broken out across data science, data infrastructure, data analytics, or business intelligence. Often each team has its own manager, reporting up to a CDO or similar executive-level role. Total data staff could range from 10-30+ employees.

While Tech and software companies tend to have a higher data team percentage than traditional industries, they will fall into the same ranges but skew to the bottom end of the range. Some industries will skew to the high end of these numbers. Hospitals, for example, will have a very high number of non-tech staff.

This is based on my experience and some general surveying of the industry. How does it line up with your company and experience? I'd love to hear from you.

I'm here,

Sawyer

Great performers

Great performers treat their work as craft.

A job feeds the family.

A craft feeds the soul.

A job requires skills.

A craft requires undue attention.

A job has a retirement date.

A craft has no end date.

What part of your work is feeding your soul, giving you energy, or tapping into a passion? It might still be exhausting and hard work, but there is a different life to the work.

That's the spark of a craft.

How can you design your work next week to create more space for your craft?

Have a great weekend,

Sawyer

The structures of data

The structures of data

Some data is structured. It’s shaped like a rectangle. With columns and rows.

Other data is semi-structured. This looks like a bullet point list. With headers, main points, and subpoints.

The rest of the data is unstructured. Pictures and videos. Documents and emails. Slack messages and medical records.

Right now, the average data teams spend their time this way:

80-95% structured data

10-20% semi-structured data.

0-5% unstructured data.

In the next decade that’s going to shift. A lot.

Unstructured data is a gold mine. But until recently the pick axes and shovels didn’t exists or were crazy expensive.

The data landscape will change. Structure data isn’t going anywhere.

But a companies skill at mining unstructured data will put them mile ahead of their competition.

Sawyer

p.s. How does your data team spend their time right now (structured, semi-structured and unstructured)?

It won’t fit in Excel

Most data won’t fit in Excel

or a pivot table

or a database

or in a reporting tool.

Most data is like this email. Words, ideas, phrases, paragraphs.

Things that don’t fit in a bar chart.

It’s the SOP manual to close out a project.

The employee handbook that holds most of your company policies.

The email exchanges with your most valuable account.

Multiple pages of reviews on the App Store for your mobile app.

What are you doing with that data? The data that could hold groundbreaking insights. Minimize significant manual tasks. Reduce the gapping knowledge hole left by the departure of a long-tenured employee.

That data.

Data teams aren’t going anywhere soon. But they will be building a lot fewer database tables.

And instead unlocking the knowledge packed into text.

If you find this interesting,

or curious

I’m here,

Sawyer

from The Data Shop

How much does data cost?

If you are thinking about how to sell your data insights to your business teams.

wondering about how much they would pay for it​

and curious about why they aren’t buying in as you hope​

you might also ask another question

How much does data cost?

Not just how much do the 0’s and 1’s cost, but how much does it cost to delivery data insights and data products to your business?

At least the following costs are in play:

• Data storage (disk or blob storage space)

• Software licensing (for Business Intelligence, data governance, databases, etc.)

• Infrastructure and cloud costs (for computing, networking, security, etc.)

• Salary and Benefits (for data analyst, data engineers, data scientist, managers, etc)

• Employee training and development (for technical and non-technical users)

• Security and compliance (for security training, regular audits and privacy reviews)

• (What else would you add?)

That list adds up to a large dollar figure. Is it worth it?

Is your data actually an asset to your company?

Or is it a liability?

Is it delivering more value than it costs?

Or is it a money pit?

You keep putting money into hoping value comes out...

If you need help answering these questions,

I’m here,

Sawyer

from The Data Shop​

p.s. Your data is valuable. I promise. My favorite part of my job is helping companies turn their data from a liability into an asset. Hit reply if you want to talk more.