Personal blog of Yzmir Ramirez

By 2025, 463 exabytes of data will be created each day, according to some estimates. (For perspective, one exabyte of storage could hold 50,000 years of DVD-quality video.) It’s now easier than ever to translate physical and digital actions into data, and businesses of all types have raced to amass as much data as possible in order to gain a competitive edge.

However, in our collective infatuation with data (and obtaining more of it), what’s often overlooked is the role that storytelling plays in extracting real value from data.

The reality is that data by itself is insufficient to really influence human behavior. Whether the goal is to improve a business’ bottom line or convince people to stay home amid a pandemic, it’s the narrative that compels action, rather than the numbers alone. As more data is collected and analyzed, communication and storytelling will become even more integral in the data science discipline because of their role in separating the signal from the noise.

Yet this can be an area where data scientists struggle. In Anaconda’s 2020 State of Data Science survey of more than 2,300 data scientists, nearly a quarter of respondents said that their data science or machine learning (ML) teams lacked communication skills. This may be one reason why roughly 40% of respondents said they were able to effectively demonstrate business impact “only sometimes” or “almost never.”

The best data practitioners must be as skilled in storytelling as they are in coding and deploying models — and yes, this extends beyond creating visualizations to accompany reports. Here are some recommendations for how data scientists can situate their results within larger contextual narratives.

Make the abstract more tangible

Ever-growing datasets help machine learning models better understand the scope of a problem space, but more data does not necessarily help with human comprehension. Even for the most left-brain of thinkers, it’s not in our nature to understand large abstract numbers or things like marginal improvements in accuracy. This is why it’s important to include points of reference in your storytelling that make data tangible.

For example, throughout the pandemic, we’ve been bombarded with countless statistics around case counts, death rates, positivity rates, and more. While all of this data is important, tools like interactive maps and conversations around reproduction numbers are more effective than massive data dumps in terms of providing context, conveying risk, and, consequently, helping change behaviors as needed. In working with numbers, data practitioners have a responsibility to provide the necessary structure so that the data can be understood by the intended audience.

The Atlassian platform is chock full of data about how a company operates and communicates. Atlassian launched a machine learning layer, which relies on data on the platform with the addition of Atlassian Smarts last fall. Today the company announced it was acquiring Chartio to add a new data analysis and visualization component to the Atlassian family of products. The companies did not share a purchase price.

The company plans to incorporate Chartio technology across the platform, starting with Jira. Before being acquired, Chartio has generated its share of data, reporting that 280,000 users have created 10.5 million charts for 540,000 dashboards pulled from over 100,000 data sources.

Atlassian sees Chartio as way to bring that data visualization component to the platform and really take advantage of the data locked inside its products. “Atlassian products are home to a treasure trove of data, and our goal is to unleash the power of this data so our customers can go beyond out-of-the-box reports and truly customize analytics to meet the needs of their organization,” Zoe Ghani, head of product experience at platform at Atlassian wrote in a blog post announcing the deal.

Chartio co-founder and CEO Dave Fowler wrote in a blog post on his company website that the two companies started discussing a deal late last year, which culminated in today’s announcement. As is often the case in these deals, he is arguing that his company will be better off as part of large organization like Atlassian with its vast resources than it would have been by remaining stand-alone.

“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,” Fowler wrote.

As for Chartio customers unfortunately, according to a notice on the company website, the product is going to be going away next year, but customers will have plenty of time to export the data to another tool. The notice includes a link to instructions on how to do this.

Chartio was founded in 2010, and participated in the Y Combinator Summer 2010 cohort. It raised a modest $8.03 million along the way, according to Pitchbook data.

The rumors ignited last Thursday that Salesforce had interest in Slack. This morning, CNBC is reporting the deal is all but done and will be announced tomorrow. Chances are this is going to a big number, but this won’t be Salesforce’s first big acquisition. We thought it would be useful in light of these rumors to look back at the company’s biggest deals.

Salesforce has already surpassed $20 billion in annual revenue, and the company has a history of making a lot of deals to fill in the road map and give it more market lift as it searches for ever more revenue.

The biggest deal so far was the $15.7 billion Tableau acquisition last year. The deal gave Salesforce a missing data visualization component and a company with a huge existing market to feed the revenue beast. In an interview in August with TechCrunch, Salesforce president and chief operating officer Bret Taylor (who came to the company in the $750 million Quip deal in 2016), sees Tableau as a key part of the company’s growing success:

“Tableau is so strategic, both from a revenue and also from a technology strategy perspective,” he said. That’s because as companies make the shift to digital, it becomes more important than ever to help them visualize and understand that data in order to understand their customers’ requirements better.

Next on the Salesforce acquisition hit parade was the $6.5 billion MuleSoft acquisition in 2018. MuleSoft gave Salesforce access to something it didn’t have as an enterprise SaaS company — data locked in silos across the company, even in on-prem applications. The CRM giant could leverage MuleSoft to access data wherever it lived, and when you put the two mega deals together, you could see how you could visualize that data and also give more fuel to its Einstein intelligence layer.

In 2016, the company spent $2.8 billion on Demandware to make a big splash in e-commerce, a component of the platform that has grown in importance during the pandemic when companies large and small have been forced to move their businesses online. The company was incorporated into the Salesforce behemoth and became known as Commerce Cloud.

In 2013, the company made its first billion-dollar acquisition when it bought ExactTarget for $2.5 billion. This represented the first foray into what would become the Marketing Cloud. The purchase gave the company entrée into the targeted email marketing business, which again would grow increasingly in importance in 2020 when communicating with customers became crucial during the pandemic.

Last year, just days after closing the MuleSoft acquisition, Salesforce opened its wallet one more time and paid $1.35 billion for ClickSoftware. This one was a nod to the company’s Service cloud, which encompasses both customer service and field service. This acquisition was about the latter, and giving the company access to a bigger body of field service customers.

The final billion-dollar deal (until we hear about Slack perhaps) is the $1.33 billion Vlocity acquisition earlier this year. This one was a gift for the core CRM product. Vlocity gave Salesforce several vertical businesses built on the Salesforce platform and was a natural fit for the company. Using Vlocity’s platform, Salesforce could (and did) continue to build on these vertical markets giving it more ammo to sell into specialized markets.

While we can’t know for sure if the Slack deal will happen, it sure feels like it will, and chances are this deal will be even larger than Tableau as the Salesforce acquisition machine keeps chugging along.

Data and analytics service Databricks today announced that it has acquired Redash, a company that helps data scientists and analysts visualize their data and build dashboards around it.

Redash’s customers include the likes of Atlassian, Cloudflare, Mozilla and Soundcloud and the company offers both an open-source self-hosted version of its tools, as well as paid hosted options.

The two companies did not disclose the financial details of the acquisition. According to Crunchbase, Tel Aviv-based Redash never raised any outside funding.

Databricks co-founder CEO Ali Ghodsi told me that the two companies met because one of his customers was using the product. “Since then, we’ve been impressed with the entire team and their attention to quality,” he said. “The combination of Redash and Databricks is really the missing link in the equation — an amazing backend with Lakehouse and an amazing front end built-in visualization and dashboarding feature from Redash to make the magic happen.”

Image Credits: Databricks

For Databricks, this is also a clear signal that it wants its service to become the go-to platform for all data teams and offer them all of the capabilities they would need to extract value from their data in a single platform.

“Not only are our organizations aligned in our open source heritage, but we also share in the mission to democratize and simplify data and AI so that data teams and more broadly, business intelligence users, can innovate faster,” Ghodsi noted. “We are already seeing awesome results for our customers in the combined technologies and look forward to continuing to grow together.”

In addition to the Redash acquisition, Databricks also today announced the launch of its Delta Engine, a new high-performance query engine for use with the company’s Delta Lake transaction layer.

“Databricks’ new Delta Engine for Delta Lake enables fast query execution for data analytics and data science, without moving the data out of the data lake,” the company explains. “The high-performance query engine has been built from the ground up to take advantage of modern cloud hardware for accelerated query performance. With this improvement, Databricks customers are able to move to a unified data analytics platform that can support any data use case and result in meaningful operational efficiencies and cost savings.”

Before Tableau was the $15.7 billion key to Salesforce’s problems, it was a couple of founders arguing with a couple of venture capitalists over lunch about why its Series A valuation should be higher than $12 million pre-money.

Salesforce has generally been one to signify corporate strategy shifts through their acquisitions, so you can understand why the entire tech industry took notice when the cloud CRM giant announced its priciest acquisition ever last month.

The deal to acquire the Seattle-based data visualization powerhouse Tableau was substantial enough that Salesforce CEO Marc Benioff publicly announced it was turning Seattle into its second HQ. Tableau’s acquisition doesn’t just mean big things for Salesforce. With the deal taking place just days after Google announced it was paying $2.6 billion for Looker, the acquisition showcases just how intense the cloud wars are getting for the enterprise tech companies out to win it all.

The Exit is a new series at TechCrunch. It’s an exit interview of sorts with a VC who was in the right place at the right time but made the right call on an investment that paid off. [Have feedback? Shoot me an email at lucas@techcrunch.com]

Scott Sandell, a general partner at NEA (New Enterprise Associates) who has now been at the firm for 25 years, was one of those investors arguing with two of Tableau’s co-founders, Chris Stolte and Christian Chabot. Desperate to close the 2004 deal over their lunch meeting, he went on to agree to the Tableau founders’ demands of a higher $20 million valuation, though Sandell tells me it still feels like he got a pretty good deal.

NEA went on to invest further in subsequent rounds and went on to hold over 38% of the company at the time of its IPO in 2013 according to public financial docs.

I had a long chat with Sandell, who also invested in Salesforce, about the importance of the Tableau deal, his rise from associate to general partner at NEA, who he sees as the biggest challenger to Salesforce, and why he thinks scooter companies are “the worst business in the known universe.”

The interview has been edited for length and clarity. 

Lucas Matney: You’ve been at this investing thing for quite a while, but taking a trip down memory lane, how did you get into VC in the first place? 

Scott Sandell: The way I got into venture capital is a little bit of a circuitous route. I had an opportunity to get into venture capital coming out of Stanford Business School in 1992, but it wasn’t quite the right fit. And so I had an interest, but I didn’t have the right opportunity.

When Tableau was founded back in 2003, not many people were thinking about artificial intelligence to drive analytics and visualization, but over the years the world has changed and the company recognized that it needed talent to keep up with new trends. Today, it announced it was acquiring Empirical Systems, an early stage startup with AI roots.

Tableau did not share the terms of the deal.

The startup was born just two years ago from research on automated statistics at the MIT Probabilistic Computing Project. According to the company website, “Empirical is an analytics engine that automatically models structured, tabular data (such as spreadsheets, tables, or csv files) and allows those models to be queried to uncover statistical insights in data.”

The product was still in private Beta when Tableau bought the company. It is delivered currently as an engine embedded inside other applications. That sounds like something that could slip in nicely into the Tableau analytics platform. What’s more, it will be bringing the engineering team on board for some AI knowledge, while taking advantage of this underlying advanced technology.

Francois Ajenstat, Tableau’s chief product officer says this ability to automate findings could put analytics and trend analysis into the hands of more people inside a business. “Automatic insight generation will enable people without specialized data science skills to easily spot trends in their data, identify areas for further exploration, test different assumptions, and simulate hypothetical situations,” he said in a statement.

Richard Tibbetts, Empirical Systems CEO, says the two companies share this vision of democratizing data analysis. “We developed Empirical to make complex data modeling and sophisticated statistical analysis more accessible, so anyone trying to understand their data can make thoughtful, data-driven decisions based on sound analysis, regardless of their technical expertise,” Tibbets said in a statement.

Instead of moving the team to Seattle where Tableau has its headquarters, it intends to leave the Empirical Systems team in place and establish an office in Cambridge, Massachusetts.

Empirical was founded in 2016 and has raised $2.5 million.

Welcome to another post in our series of interview blogs for the upcoming Percona Live Europe 2017 in Dublin. This series highlights a number of talks that will be at the conference and gives a short preview of what attendees can expect to learn from the presenter.

This blog post is with Daniel Lee, a software developer at Grafana. His tutorial is Visualize Your Data With Grafana. This presentation teaches you how to create dashboards and graphs in Grafana and how to use them to gain insight into the behavior of your systems. In our conversation, we discussed how data visualization could benefit your database environment:

Percona: How did you get into database technology? What do you love about it?

Daniel: I’m a developer and my first job was working on a transport logistics system, which was mostly composed of Stored Procedures in SQL Server 2000. Today, I would not build a system with all the logic in Stored Procedures – but that database knowledge is the foundation that I built everything else on. Databases and their data flows will always be the core of most interesting systems. More recently, I have switched from Windows to working with MariaDB on Linux. Grafana Labs uses Percona Server for MySQL for most of our internal applications (worldPing and Hosted Grafana). Working with Grafana also means working with time series databases like Graphite, which is also very interesting.

I enjoy working with data as it is one of the ways to learn how users use a system. Design decisions are theories until you have data to either back them up or disprove them.

Percona: Your presenting a session called “Visualize Your Data With Grafana”. How does monitoring make DBAs life easier, and how do graphs make this information easier to apply for DBAs?

Daniel: Good monitoring provides top-level metrics (throughput, number of errors, performance) for alerting, and other lower-level metrics to allow you to dig into the details and quickly diagnose and resolve an outage. Monitoring also helps you find any constraints (for example, finding bottlenecks for query performance: CPU, row locks, disk, buffer pool size, etc.). Performance monitoring allows you to see trends and lets you know when it is time to scale out or purchase more hardware.

Monitoring can also be used to communicate with business people. It is a way of translating lots of different system metrics into a measurable user experience. Visualizing your data with graphs is a very good way to communicate that information, both within your team and with your business stakeholders. Building dashboards with the metrics that are important to you rather than just the standard checklists (CPU, disk, network etc.) allows you to measure the user experience for your application and to see long-term trends.

Percona: Why Grafana? What does Grafana do better than other monitoring solutions?

Daniel: Grafana is the de facto standard in open source for visualizing time series data. It comes with tons of different ways to visualize your data (graphs, heat maps, gauges). Each data source comes with its own custom query editor that simplifies writing complex queries, and it is easy to create dynamic dashboards that look great on a TV.

Being open source, it can be connected to any data source/database, which makes it easy to unify different data sources in the same dashboard (for example, Prometheus or Graphite data combined with MySQL data). This also means your data is not subject to vendor lock-in like it is in other solutions. Grafana has a large and very active community that creates plugins and dashboards that extend Grafana into lots of niches, as well as providing ways to quickly get started with whatever you want to monitor.

Percona: What do you want attendees to take away from your session? Why should they attend?

Daniel: I want them to know that you can make the invisible visible, with that knowledge start to make better decisions based on data. I hope that my session helps someone take the first step to being more proactive in their monitoring by showing them what can be done with Grafana and other tools in the monitoring space.

In my session, I will give an overview of monitoring and metrics, followed by an intro to Grafana. I plan to show how to monitor MySQL and finish off with a quick look at the new MySQL data source for Grafana.

Percona: What are you most looking forward to at Percona Live Europe 2017?

Daniel: Firstly, it is always great to have an excuse to visit Ireland (I’m an Irishman living in Sweden). I’m also looking forward to getting feedback from the community on Grafana’s new MySQL data source plugin, as well as just talking to people and hearing about their experiences with database monitoring.

Want to find out more about Daniel and data visualization? Register for Percona Live Europe 2017, and see their talk Visualize Your Data With Grafana. Register now to get the best price! Use discount code SeeMeSpeakPLE17 to get 10% off your registration.

Percona Live Open Source Database Conference Europe 2017 in Dublin is the premier European open source event for the data performance ecosystem. It is the place to be for the open source community as well as businesses that thrive in the MySQL, MariaDB, MongoDB, time series database, cloud, big data and Internet of Things (IoT) marketplaces. Attendees include DBAs, sysadmins, developers, architects, CTOs, CEOs, and vendors from around the world.

The Percona Live Open Source Database Conference Europe will be September 25-27, 2017 at the Radisson Blu Royal Hotel, Dublin.

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