Chartio | Blog

Chartio is joining Atlassian

2021-02-25T00:00:00-05:00

Dear Chartio customers,

For over 10 years Chartio has been working with you toward our mission to JOIN teams ON data. We want to see a world where companies are truly informed and where anyone in a company (not just the data analysts) can intuitively query, visualize, and collaborate on the information that runs your business.

Taking a look at the data today - over 10.5 million charts have been created for 540k dashboards by 280k users pulling from over 100k data sources. That’s just incredible. Your usage, feedback, subscriptions, stories, and evangelism, have all fueled this and we’re enormously grateful.

Today we’re excited to announce that the next chapter for Chartio is joined with Atlassian! Late last year they reached out with a proposition for us to combine our efforts and join them in their more broad mission to Unleash the potential in every team. While we’ve been proudly independent for years, the opportunity to team up our technology with Atlassian’s platform and massive reach was incredibly compelling. Their product-led go to market, customer focus and educational marketing have always been aspirational for us. Over the past few months I’ve discovered even more similarities in their culture, values, care for the customer and most importantly mission.

Moving forward, our team needs to be singularly focused on the mission of delivering powerful cross-product analytics and visualization to the Atlassian platform. We will be communicating directly with our customers with details around the next steps for you and Chartio.

On behalf of the team and me, it has been and continues to be an honor to serve you. Please reach out and chat with your account representatives with any questions you may have. Thank you for all you’ve done and your faith and support in Chartio and our mission.

How to Improve Data Accessibility as a Data Analyst

2021-02-10T18:17:17-05:00

What if you could advance your career by getting others to do your work? When you, as a data analyst, improve data accessibility for your organization, you might get exactly that. You empower others to find the answer they need, and thus do your work for you.

Data accessibility is the degree to which people in your organization can use data. That is to say that data isn’t just available, but usable — even for people with little to no experience working with data.

As a data analyst, you have an opportunity to change how your organization uses data by taking a few specific steps to improve data accessibility. In the process of changing how your organization uses data, you will not only help your coworkers use data to improve their decision making, but set yourself up for a great career.

Lean On Self-Service Business Intelligence

A self-service business intelligence (BI) platform empowers your coworkers to take care of their own ad hoc data requests. When implemented well, a self-service BI platform allows anyone in your company to build dashboards and reports for themselves on-demand, no matter their level of expertise.

A self-service BI platformprioritizes ease-of-use for the end business user. There are numerous quality-of-life and automation features these platforms might have to improve data access, including low or no-code SQL query builders (like Visual SQL), automatically generated visualizations, and drag-and-drop dashboard creation.

These features combine so that anyone can query data, perform data analysis, and create their own dashboards and reports with little to no effort on your part. As an analyst, you likely receive a lot of ad hoc requests for dashboards or quick reports. All of thosead hoc requests for a dashboard or quick report will go away (or at least be drastically reduced) because your coworkers can help themselves.

Alex Knowles, Director of Strategic Initiatives at DataRobot, says that he and other leaders at DataRobot (who have gone all-in on accessible data) tell employees to “Take an afternoon and teach [the process of creating dashboards] to yourself. Don’t complain about analysts not having time to help you, just go figure it out [with Chartio].”

Music to a data analyst’s ears. But the best self-service BI platforms go even further.

A good self-service BI platform is quick to implement and provides an easy way to connect multiple data sources and datasets, from Google Sheets to Amazon Redshift. Power-user features, like a robust SQL editor and schema management, also won’t fall to the wayside with a good self-service platform.

The best of the best will have built-in (but removable) guide rails for people who don’t have extensive experience working with data. These guide rails help data newbies stay on track when creating visualizations and can even teach best practices.

Embed Dashboards Into Your Teams’ Tools

When you need a standardized dashboard to serve as a source of truth, embed it into the tools your coworkers use. Embedding dashboards that answer common questions where your coworkers spend the most time will go a long way to improving data accessibility.

Some self-service BI platforms allow you to embed dashboards wherever there is an HTML iframe. There are some tools out there that focus solely on embedding analytics, but with a self-service BI tool (like Chartio 😉), you can embed dashboards in addition to all the other features. With a platform like Chartio, not only is the dashboard easy and quick to create, you can place it wherever it’s needed — on your website, within a tool, and even within your product.

When you embed a dashboard in a commonly used tool, for instance, your coworkers are able to find the information they need to make data-driven decisions without leaving that tool.

Say you embed an interactive dashboard directly into Salesforce to help your sales development reps. That dashboard can include relevant data about warming up a lead while they’re speaking to that lead.

Embedding dashboards is a great way to improve data accessibility without asking your coworkers to do more work. You can create a dashboard once, place it wherever it needs to be, and that data will become available and usable for everyone to collaborate with.

Facilitate Data Literacy Throughout the Company

Improving access to data includes making sure data gets used well. Create a plan to teach your coworkers data literacy so they can interpret and use the data you’ve given them access to in an effective, accurate way.

As intuitive as a self-service BI tool can be, and as convenient as embedded analytics can be, there is still the potential for data stakeholders to misunderstand or misrepresent data. Concepts like selection bias and the difference between relative and absolute change may come easily to someone who works with data daily, but not so much for a salesperson who wants insight into the success of their cold email strategy.

To promote data literacy, you could hold monthly workshops, create a collaborative Slack channel, build an internal knowledge base, or read through Chartio’s Data School.

Clever, one of our clients, has a Slack channel called the “Number Munchers” filled with data newbies sharing tips and tricks for using Chartio and their data better. Creating a Number Munchers equivalent channel may be the easiest starting point for you.

Increasing the accessibility of your data is an ongoing process, and by promoting data literacy, you can start to create a culture of data collaboration that starts to feed into itself. Over time, you’ll help build a long-lasting culture of collaboration using data.

Embrace Your Changing Role

As you make data more and more accessible, your role will start to shift from the person who delivers insights to the person who helps others find and use their own insights. This shift is a part of a much larger change happening in the world of data that, as a data analyst, you can lead within your organization.

Improving data accessibility has become so important in businesses big and small that it’s starting to change how people think about data analytics. The goal for improving data access varies for each business, but usually revolves around getting people the right data, at the right time, so they can make the right decisions.

A new data role is starting to emerge (which was first noticed by the folks at dbt) that can serve as a next step for your career as a data analyst. That role is the analytics engineer, whose responsibility it is to address the more technical aspects of making data accessible, like data modeling, data management, standardizing metadata, ensuring data quality, and establishing methodologies and data-based workflows.

The new analytics engineer role is just one part of this larger change happening with how businesses use data. Modeled data is higher-quality data, which, in the hands of your data stakeholders, makes your self-service BI platform more and more powerful. This creates a self-feeding loop, where your increasing data quality improves data access, which allows you to focus even more on quality.

As more data becomes available and easier for the end business user to use, businesses of all sizes will increasingly rely on data as a sort of central nervous system for collaboration.

Start Making Data Accessible Today

No matter what, you need a self-service BI tool to start the process of improving data accessibility. That way, you can begin by making everyone their own data analyst, then start embedding dashboards once they move beyond the need for ad hoc dashboards.

And wouldn’t you know it, we have a suggestion to help you get started. Chartio is a self-service BI platform whose mission is to “make data accessible to anyone.”Start a free trial right now and start making data more accessible within your organization today.

Don’t Blame Excel for Your Data Mistakes

2021-02-08T18:17:17-05:00

Earlier this year, the British government announced that almost 16,000 people in the UK were infected with the coronavirus but weren’t being counted—and it was all because of a spreadsheet.

The problem happened because British government was tracking infections using an older version of Excel (.xls), which is pretty limited in how many rows of data it can handle. As labs sent back test results in .csv files, eventually the Excel spreadsheet hit its limit. At that point, additional cases weren’t counted.

Whether you’re a data analyst or CTO, when you saw that story you probably wanted to go around and (virtually) shake some of your users, saying, “This is why you should stop storing our data in your spreadsheets!”

I get it. But that understandable reaction overlooks an important question: why do so many people use spreadsheets in situations where everybody in tech knows it’s a terrible idea?

I don’t think the problem is spreadsheets. I think the real problem is our data platforms. If we’re going to get people to stop using spreadsheets in ways that don’t make sense, tech companies need to start building data solutions that are both as powerful and as easy to use as spreadsheets are.

What’s so bad about spreadsheets?

Spreadsheets are an awesome solution for some problems. But it’s easy to outstrip their limits. Here are just a few of the problems that crop up when spreadsheets act as your data repository, source of record, and analytical tool.

Spreadsheets can’t handle large data sets

As the UK government learned the hard way, throw enough data at a spreadsheet and at some point it will buckle. Go much over 1 million rows of data and you’re definitely in trouble, but power users know that smaller data sets can cause serious loading and processing delays.

And when a spreadsheet hits its limit, it isn’t designed to tell you that it can’t handle the load. The only way you’ll find out your spreadsheet is in trouble is when something goes horribly wrong.

It’s hard or impossible to find out how data has been changed

If you’re working with a data platform, you can usually find out how the data ended up in the version you are using. Most major transformations are the result either of scripts that were run on the data or an input process built into the data platform. Scripting bakes in transparency: you can examine the script to find out exactly when and how data was transformed (e.g., changing an entry’s formatting from “sunday” to “Sunday” or applying critical business logic to a core metric).

With a spreadsheet, however, it’s nearly impossible to figure out how the data has been manipulated. Sure, you can keep a data change sheet to document the process, but that’s fraught with potential pitfalls. Along with requiring users to keep multiple files updated, applying transformations that way is more permanent which makes them difficult to tweak or undo.

And what about users that manipulate their spreadsheet data by hand? That’s a road to ruin. Any time you’re repeating a process, it’s easy to make—or perpetuate—a mistake. The end result can lead to costly errors, in one case almost $6 billion in damages.

With spreadsheets, there is no single source of truth

One of the biggest problems with spreadsheets is that they practically encourage siloed data—they are quite possibly the greatest enemy of your single source of truth (SSOT).

It’s not hard to understand why: Marketing gets a slice of data from Product for a particular project they’re working on and they put it in a spreadsheet. The data turns out to be really useful, so other people start using it. Meanwhile, someone in Finance managed to wrangle similar-but-not-the-same data out of the inventory system. And other people start using that piece of data.

Both Marketing and Finance believe their spreadsheet is a single source of truth. But in reality, they’re working from different data, which means your team is using multiple resources to answer questions, none of them give the same answer, and, most importantly, it’s not immediately clear which answer is the right one.

A proper SSOT can’t be a file trapped on a user’s hard drive. They have to be accessible by others so the whole company can benefit (and work) from the same data.

So why are we all still using spreadsheets?

If spreadsheets are such a problem, why are they still so overused? I’ve learned the hard way as a CTO and cofounder of multiple startups—including one dedicated to simplifying data infrastructure—that the biggest reason people turn to spreadsheets is that today’s tech for managing data is an endless rabbit hole.

This problem is especially acute for startups because they have to be fast, decisive, and data-driven. They have to be able to strategize, execute, learn what works and what doesn’t, and quickly adjust. Startups need data solutions they can set up quickly, share with anyone on the team, and that will scale—in both data size and use-case complexity—without engineering effort.

One answer to the problem is building a custom solution that pulls in data, applies your business logic, and outputs key reports on demand. I’ve done that more times than I like to think about, and in my experience they not only cost more money than they should but were also a drain on our company’s most critical resource: time.

The time engineers spent maintaining data infrastructure instead of building their core products.
The time analysts and business owners spent waiting for the data they needed, instead of asking and answering business questions.
The time the business spent taking uninformed, indecisive action due to lack of readily accessible data.

Instead of focusing on our core products, we became bogged down with data maintenance tasks. At the end of the day, building data infrastructure became an opportunity cost, and again and again, we were forced to make an impossible tradeoff between dedicating development time to our core product or our data-driven culture.

And that’s why spreadsheets are so pervasive…and so powerful.

Anyone from the CEO to a new marketing hire can grab data and immediately work with it in Excel or Google Sheets. There’s zero learning curve, plus, users don’t have to wait for engineers to modify your business’ complex data infrastructure to get the data they need.

Is it any wonder that startups end up relying on spreadsheets more than they should?

Data platforms need to learn from Henry Ford

Why is Henry Ford famous? It’s not because he invented the combustion engine, or the wheels of a car, or a car itself. It’s because in a moment when everyone else was selling parts that buyers were supposed to DIY into a drivable car, Ford sold the final product, making it possible to start driving immediately.

For all of its advancements, data tech still hasn’t had its Henry Ford moment.

As a result, companies waste a ton of time assembling and maintaining all of the different parts of a data stack—pipelines, storage, governance, BI tools—instead of actually using the data. They’re no longer stuck creating DIY components for the stack, but they’re not getting ready-to-drive data, either.

Data tech needs a paradigm shift. To do that, though, data platforms will need to provide:
Easy syncs from all of your data sources, such as internal production DBs and external APIs and services
Seamless, trustworthy, real-time, and scalable collaboration like you have with documents in Google Drive, tasks in Jira, or code on Github.
Simple to use transformations and aggregations which let you quickly produce a dataset that makes analysis easy to do so your entire team will want to pull data from your startup’s SSOT.
Flexibility in the analytics tools you can use. That could mean Excel or Google Sheets, but it should be equally simple to use SQL, Visual SQL, Python notebooks, or even ML.

There’s no reason data management should be more complicated than that. And there’s no reason companies should have to spend time messing around with data infrastructure instead of what they really care about: figuring out what the data is saying and strategizing about the action to take.

That’s why Panoply and Chartio are such a good match. We get why Excel is so alluring, but we’re also committed to creating a better way. We don’t just want to build better pipelines or prettier charts; we want to put users in the driver’s seat of their data.

This guest post is written by Roi Avinoam,CTO and Co-Founder at Panoply, a company that’s dedicated to making it easy to sync and store your data. Roi believes that data is essential to every organization and has been working for years to make it as accessible and easy to use as Slack.

“Making Data Beautiful and Easy.” A Beginner’s Review of Chartio

2021-02-08T13:08:18-05:00

Any fans of Chartio know that their mission is to democratize data, making it accessible for professionals of all levels within an organization. For a business to be data-driven, and to plug in data in every department, you need either to have every single employee be a data scientist (impossible) or to have a platform that’s user friendly for even those with the most basic data skills.

That’s me.

As a member of the marketing team for Product School, data informs a lot of our decisions. We need to know what content people need the most, what they respond well to, and how we can better serve the needs of our community. As any good Product Manager will tell you, users don’t always know what they need, you have to go into the data and find out for yourself.

But I’m no data scientist, I’m a digital creator. I can’t spend a huge chunk of time on ‘SQL’ (whatever that means) and going through tough excel sheets I only half understand. So I started my journey with Chartio, in the hopes that their user friendly interface and plug-in-and-play charts would make my life a little easier.

If you’re trying to choose your new data visualization tool, or you’re trying to find something beginner-friendly for all of your teams to use, here is my beginner’s review.

Onboarding

The signup process was incredibly simple. For those of you who are concerned about privacy, you’re given a test set of data to play around with before you decide to commit and plug in your own data. Chartio supports an incredible number of data sources, so you’re ready to go whether you’re using a fancy database like Oracle, or you just have a simple .csv file. No need to mess around converting or exporting files.

I’m a simple woman, and I appreciate a useful onboarding email, which I believe are very underrated.

Chartio’s onboarding email gave me a variety of options depending on my goals for the platform, and also my level of skill. I could either jump in and build my first chart, schedule a data connection session, or join Chartio for virtual office hours.

Being the brave marketing daredevil that I am, I jumped straight into building my first chart. The first thing Chartio does is present me with a video on learning visual SQL. It’s a great quick-bite video that makes SQL easy to understand for beginners, and having these learning materials available in-platform is much simpler than having to Google things for myself.

Chartio has options to work in visual and regular SQL, meaning that more advanced users will feel equally comfortable with the platform.

Usability

So now it’s time to run my first query. At the moment, when I have a question about data, I usually have to ask someone else if it’s not something I can learn from glancing at Google Analytics. So my hope is that Chartio is simple enough to use, that I can get my own insights and go down any rabbit holes by myself.

After watching the helpful how-to video, it only took a few clicks to create my first chart. The UI is very intuitive, so after being shown around by the onboarding process I already feel fairly confident in building my first chart.

When you’re not used to spending all day working with data, being able to visualize it while you are querying it goes a long way in helping to make sense of it. Unlike other data tools I’ve used in the past, which gave me unhelpful spreadsheets that might as well have been ancient hieroglyphics, Chartio makes the instant connection between the data you input and visualization. This makes answering the questions beginner-users have about their data much easier to answer.

More Cool Things

Now I’ve gotten to grips with the basics of Chartio, I wanted to highlight some of the features of the product that really stood out to me.

1. Embedding

One of the few parts of data I’m familiar with, is the need to share data visualizations with people outside a company. As a content creator, being able to share data dashboards and have them embedded within a website or product is incredibly exciting. With Chartio’s new interactive embedding, we’re able to embed fully interactive dashboards onto our website in order to share information with our community.

I’m sure that the sales and leadership team will also be making the most of this feature as they share information with our partners.

2. Visualization library

Other beginner-friendly data visualization tools make the mistake of limiting how customizable the final charts are. Just because we’re mathematically challenged doesn’t mean we want to be any less accurate with how we present our information. That’s why Chartio’s new and upgraded visualization library is so impressive.

With these charts, accuracy doesn’t have to be sacrificed for beauty, as everything is endlessly customizable. You can either go into deep levels of customization to get your charts exactly how you want them, or stick with the default settings, depending on your skill level. I can easily imagine collaborating with more data-inclined team members to make dashboards and charts that work for everyone.

3.Customer service

The unsung heroes of the product world are the customer services teams! With Chartio, you’re only ever a few clicks away from being able to speak to a real human being. Not just for customer support, but for education and general queries about the platform. They really are data gurus.

Chartio’s Mission

One of the most unique features of Chartio isn’t even a feature…it’s their mission. This is a company that doesn’t just talk the talk, they walk the walk. It’s easy to say you want to democratize data and make it accessible for everyone, but it’s another thing to build a platform that actually does that. Chartio is a platform that has certainly made data accessible and easy for this beginner.

This guest post is written by Ellen Merryweather. Ellen is a Content Manager at Product School, a global leader in Product Management training with a community of over one million product professionals.

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Chartio Launches Certification Program: How to Become a Certified Chartio Specialist

2021-01-19T02:08:18-05:00

At Chartio, our mission is to JOIN teams ON data, and that entails making sure each team member has the resources they need to succeed in data literacy and exploration. Solving data problems requires entire teams to work together, iterate and focus on common goals to get the answers that they are trying to achieve.

As we continue to help thousands of teams get empowered with data, we wanted to give our users an opportunity to validate their Chartio expertise and be more confident in their skillset. Chartio has many powerful features that allow users from across the organization to connect, explore, visualize and collaborate across their data. When only one member of your team can do this, it slows down the process. Chartio’s platform focuses on bringing all stakeholders together to find answers to their data questions. We enable organizations to get up and running with their data quickly; our Visual SQL helps both data and business teams conceptualize the data needed quickly to determine the answers they need. Finally, we add in collaborative tools so that teams can work together to make business decisions they need.

Today, we are happy to announce the launch of the Chartio Certification program. As part of our commitment to enable our customers to be skillful with Chartio, this program is designed to encourage our customers to help their entire teams feel confident using Chartio. Upon completion, they can also proudly showcase their skills to the professional network. Level 1 certification is offered for free to our customers and aims to assess for basic understanding of building charts and dashboards in Chartio.

Certification Implementation

Initially, we kicked off the certification program internally. We put our feet in our customers shoes to understand how and what was needed to get Chartio up and running.

“Here at Chartio the entire team uses Chartio every day to be more informed, collaborative, and effective in our jobs. The certification process is a great way to onboard our new employees and assess where they may need a bit more assistance to be truly data-driven. As a company, having everyone Chartio certified has allowed us to be more efficient and aligned on how to measure and share metrics. We are excited to share our certification with our customers,” shared Mary Sapountzis, Head of Customer Success at Chartio.

We’re proud to announce that the entire Chartio team, from leadership, engineering, sales to customer success have all passed and are Chartio Level 1 Certified! This proved to be a valuable exercise, as our employees got to assess how much they knew and learn new ways to use Chartio along the way. This has now become a part of the onboarding process for new employees at Chartio, and we believe our customers will find it valuable too.

“I think the certification program is a good way to get new users accustomed to Chartio,” says Ken MacMann, Analytics Manager of ButterflyMX.

Lillian Rafson, Founder and CEO of Pack Up + Go, says “The certification process is great because it encourages people to dive in and build really basic reports without feeling overwhelmed. It’s an easy, low-stakes way to learn about Chartio. Having this as a new learning opportunity and skill that people can learn is super valuable.”

Once you complete and pass the certification, you will receive a certificate you can proudly showcase to your professional network, such as on LinkedIn.

How You Can Get Certified

If you are an existing customer and ready to test your skills, you can begin your certification process here. If you’re not yet a Chartio customer, you can get started today with a free trial. As always, we encourage you to reach out to us at support@chartio.com with any questions!

How to Create a Data Culture without a Data Team

2020-12-30T02:08:18-05:00

Your business needs a data culture, but you don’t need a data team to achieve it. A strong culture of data is one in which every person in your organization can easily use data to make good decisions — so much so, it becomes a natural part of their day-to-day routine.

The impact of this widespread use of data is massive, and it only compounds over time. Two years ago, DataRobot, a machine learning company, gave all their employees access to the company’s business intelligence platform (which just so happens to be Chartio! 👋). Within three months, they achieved an 83% adoption rate, with 1,500 dashboards across the company.

Fast forward: DataRobot recently raised $50 million in funding, bringing their total valuation to $2.7 billion. Of course, there are a lot of factors that go into a valuation that high, but when it comes to a good data culture, Daniil Bratchenko, VP of business engineering at DataRobot, says, “Democratization of access to data is super important when you see how it works, and if we didn’t have it, we would be much less effective as a company.”

But trying to be such a data-driven company can sometimes feel like a “keeping up with the Joneses” affair, where you can never quite catch up to what you’re supposed to be doing. Maybe you’ve been pondering what it’d be like to have a data scientist on the team, or maybe you’ve been hearing rumblings about this new role called an “analytics engineer.”

The truth is, you don’t need anything fancy. To establish a thriving data culture, you need the right tools and a little bit of elbow grease. No need for a data team right away. Here are three options for establishing a data culture at your business without relying on a data team.

Option 1: In-App Dashboards

Your first option to establish a data culture is to rely on your existing applications’ own reporting tools. This option naturally causes data fragmentation, which makes it not ideal for establishing a long-term data culture. But it can work in the short term if you can work around data silos.

A lot of modern applications have reporting baked into their product (sometimes relying on embedded analytics from platforms like Chartio, incidentally). A widely used example is Google Analytics, which provides insight into your website traffic and organic search presence.

These in-app reporting solutions typically have a narrow focus, gathering and displaying only data that’s relevant to its area of expertise (e.g., Google Analytics focusing on website traffic). They usually provide rudimentary search, sorting, and filtering to get the data you need, but to get granular, you need to be very familiar with the application itself. Often the only people who can spare enough time to get familiar with these products are the people who use them day in and day out.

As an example of the need for this familiarity, here is a video of someone using Google Analytics to track multi-touch attribution:

Source: How to Track Multi-Touch Attribution in Google Analytics

If you don’t already know a lot about how Google Analytics works, you might be lost within the first 10 seconds.

This requirement for specialized knowledge is what helps cause data culture fragmentation with this option. For instance, the product team doesn’t have much use for Google Analytics in their day-to-day. If there is something the product team needs to know about website traffic (e.g., traffic to a new feature’s landing page), it’s up to the marketing team to bridge that gap. And that’s not counting data the product team uses daily that would be useful for marketing to know.

Without a way to consistently communicate their data culture to each other, each team will start to develop their own “data dialects.” Here’s an example: the marketing team might make landing-page copy tweaks based on website user behavior, while the product team plans an update road map on the same feature, based on product user behavior. They’re both making data-driven decision, but separately.

This example can still be a data culture — it’s just fragmented. Each team can make data-driven decisions, but only in a way that makes sense to them. Fortunately, there are a few steps you can follow to mitigate fragmentation when using in-app dashboards to develop a data culture.

How to Implement a Data Culture with In-App Dashboards

Rely on robust tools. Focus your investment on applications that have access to a lot of data and can do a lot of things with that data. CRMs, for example, have sales data, marketing data, and some customer-behavior data, which can all be used across the company in a ton of different ways.
Incentivize their use among the teams they impact. Your marketing team should be deeply familiar with your CRM so they can use it just as well as the customer success team and the sales team. This way, you have three teams all speaking the same “data dialect.”
Promote interpersonal data sharing. Because your sales, marketing, and customer behavior data is all siloed away in your CRM, it’s up to your sales or marketing people to fill the product people in on what’s happening with that data. Make sure you promote this exchange of information as often as possible.

Option 2: Cloud-Based Spreadsheets

One way to bridge the gap between individual applications is to export and combine your data into cloud-based spreadsheets with a tool like Google Sheets or Airtable. This option can help develop a unified data culture without a data team, but it can require quite a bit of time and effort to maintain.

OK, we all know about spreadsheets. They’ve been around for a long, long time and are central to the functioning of just about every business (heck, we still use spreadsheets). From a data standpoint, they’re great because you can export your data from individual applications and combine it within a spreadsheet. In this way, you can compare data from your CRM to data from your product analytics tool, bridging the gap between your marketing and product teams

The biggest innovation to spreadsheets in recent memory is moving them online and beefing up their automation capabilities. Google Sheets automatically saves your spreadsheet so that it’s always up to date no matter who accesses it. Just copy/paste the link and you’re good to go. Airtable takes this concept and enriches it with powerful automation features and visualizations (we use Airtable internally, and we love it).

Shareability and automation both make cloud-based spreadsheets incredibly malleable. You can make them do a lot of things quickly — aggregate data, calculate fields, use pivot tables, etc. — and share them wherever you need them to be shared.

But that malleability comes with a price: Maintaining spreadsheets requires a lot of work. Moving spreadsheets online and adding features doesn’t change the fact that updating data, styling tables for legibility, changing charts, etc., are all things you have to do manually. And, once done, you still need to be sure you’re presenting all that data in a way that makes sense to others.

In other words, spreadsheets can still become a language of their own. Making them mutually intelligible across the organization in order to foster a data culture is possible, but it’s an art that requires a lot of effort to master. But, again, there are a few things you can do to smooth out these issues.

How to Implement a Data Culture with Spreadsheets

Create a consistent reporting and sharing cadence. Formalize the process for inputting data and sharing the results.
Rely on templates. Use templates to standardize the spreadsheets your teams use to ensure they’re as mutually intelligible as possible.
Automate when you can. Airtable and Google Sheets provide some automation capabilities that can make managing spreadsheets easier. Use these automations if you have the time to learn and master them.

Option 3: A Self-Service Business Intelligence Platform

If you don’t want your data siloed in individual applications or managed through never-ending spreadsheets, you can use a self-service business intelligence platform to establish your data culture. The cost of investment is a little higher than the others, but the ceiling for what it can achieve with your data is astronomically higher.

A business intelligence platform helps you gather, understand, and visualize all your data. Historically, BI platforms have been very complex tools that require the expertise of data teams to use. But self-service BI platforms (like Chartio 😉) have made it much easier for anyone to use a BI platform.

Self-service BI platforms are specifically designed to empower each and every employee to help themselves to the data they need — even if they don’t know SQL, schemas, or other technical aspects of your data. Features like visual SQL query builders, intuitive dashboarding tools, in-dashboard collaboration, among many others, all contribute to making self-service BI tools so user-friendly that you don’t necessarily need the expertise of a data team to fully leverage them.

Within a self-service BI platform, your product team can find exactly the data they need from the CRM without having to know how to navigate that CRM. Nor will they have to wade through a spreadsheet created by a marketing person. The same goes for the marketing team and sales team. And all of this is possible without a data team.

This widespread use of the BI platform — sometimes called data democracy — is a foundation for a data culture that can scale. If you can do all this without a data team, imagine what you can do with a data team. Another way of thinking about it is that what used to be possible only with a data team — using a BI platform — is now a baseline that any company can achieve. When it comes time to make your first data hire, your BI platform will only get more powerful and more useful.

How to Implement a Data Culture with a BI Platform

Vet integration options. Look for direct connections to all the tools you currently use or use tools like Panoply to create your own connections.
Make sure a nontechnical user can set it up, use it, and maintain it. BI platforms rely on SQL to fetch and use data. Make sure your BI platform has the capability to query data without coding SQL, and that it makes it easy to turn those queries into interactive dashboards.
Only buy as many seats as you absolutely need (at first). Limiting the number of people who use your BI platform will create an initial bottleneck, but this smaller number will allow you to save some money and work out any kinks in the process. Then, when you’re ready, give everyone access.

When You Should Make Your First Data Hire

You should consider making your first data hire either when you need to start modeling your data or when you need to account for complex issues, like finance certification or regulatory compliance. Basically, when your needs from your data go beyond just empowering data-driven decisions, start building your data team.

For data modeling purposes, we recommend setting up your first data hires with Fishtown Analytics’ data build tool, or dbt for short. dbt can help you accomplish a lot of things when it comes to standardizing your data modeling, but one specific way is that anyone who knows SQL can use it to contribute to their company’s data model. Then, your first data hire can review, approve, and manage this code to ensure standards are met.

Before you reach this point, though, you can go a long way toward establishing your data culture without a data team by hooking up your database (or other data sources) to Chartio. And you can get started right now with a free trial.