Creating Intelligent Narratives with Narrative Science & Keboola

Intelligent Narratives are the data-driven stories of the enterprise. They are automated, insightful communications packed with the information that matters most to you—specific to your role or industry—written in conversational language, and at machine scale. By giving your employees and your customers a richer, more nuanced understanding of your business, they can make more informed decisions and realize their greatest potential.

Narrative Science is the leader in advanced natural language generation (Advanced NLG) for the enterprise. Quill™, its Advanced NLG platform, learns and writes like a human, automatically transforming data into Intelligent Narratives—insightful, conversational communications packed with audience-relevant information that provide complete transparency into how analytic decisions are made.

As we all know, one of the biggest barriers to successful data projects is having the right data in the right place; that's why Narrative Science and Keboola have partnered to bring the next generation of analytics to you faster. Automate data workflows, reduce time and complexity of implementations and start gaining new insights now! Leverage this app, powered by Narrative Science, to produce machine-generated narratives of data ingested by Keboola. 

Example below:

Dimensions used: country, region

Measures used: Freedom

Input:

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Output:

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By utilizing this Advanced NLG application, you can focus on higher-value tasks that improve your business by simply reading what is most important within your data. Any situation in which someone is manually analyzing, interpreting, and communicating data insights at scale is prime for automation with the Narrative Science app. The use cases for Intelligent Narratives are vast - but here are a few examples:

  • Generate Top-level narrative for dashboard embed, pushed to dashboard at the same time as data update.

  • Send weekly status update via email to executives (Drafted custom python component for Mailgun)

  • Create push messages (Pushbullet, Pusher, Twilio, Intercom, etc.)

  • Filter data based on business rules via transformations/custom science components/apps and send such filtered data to narrative science for human-readable comments.

Whether you are a new or existing Keboola user, learn how you can get more from your data with Narrative Science by clicking below:

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Thanks and enjoy!


Freethink + Keboola: Understanding cross-channel video analytics

Video is one of the hottest trends in digital marketing. YouTube, which has expanded more than 40 percent since last year, reaches more 18-49 year-old viewers than any of the cable networks and has a billion users watching hundreds of millions of hours every day. 

Freethink, a modern media publisher, uses online video to tell the stories of passionate innovators who are solving some of humanity’s biggest challenges by thinking differently. While telling important stories is their primary focus, data underlies all of their decisions. As a publisher, they need to understand how well each piece of content performs, as well as how that content performs across platforms (they currently publish videos on their website, YouTube and Facebook.)

Prior to working with Keboola, collecting and combining data for cross-channel video analysis was a time consuming, manual effort (particularly because Facebook has separate APIs to track page content and promoted content.) In addition, this process made performing time-over-time analyses a real challenge.

The goal was to provide a dashboard solution for the team to have better visibility into their data. Keboola Connection (KBC) was able to overcome this by leveraging existing API connections to get data from Facebook and YouTube. In addition, Keboola utilized its partnership with Quintly (social media analytics) in order to pick up cleaned and verified data from their API.  All this data is combined additional data sources including Google Sheets to provide additional metadata for advanced reporting and segmentation. This blended data enables universal reporting across platforms to get a 360-degree picture of each piece of content.

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Freethink now has all their data populated in Redshift, where Chartio is able to connect to create beautiful dashboards for reporting. They are able to go into the Keboola platform and manually adjust and run configurations to get exactly the data they need. The biggest gains have been in time saved, being able to show change over time and freeing the team up to focus on more complicated analyses. This also opened up data access to the broader team, promoting collaboration and data driven decision making.


"Keboola really helped simplify and automate the process of collecting and combining data. Working together, Chartio and Keboola Connection deliver a full stack solution for modern analytics, taking full advantage of the cloud. I’m able to give my team better insights into our performance and make better decisions, quicker."

-Brandon Stewart, Executive Editor at Freethink


Thanks,

Colin


The Best Tool for Your Data Product Journey? A Good Map

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For anyone creating an analytics product, the pressures of engaging customers and generating revenue while protecting your core product and brand can be overwhelming, especially when aiming to hit so many goals on the horizon:

  • Does it target users effectively?

  • Will it guide users to a solution to their business problem?

  • Can it scale to many customers?

  • Will it deliver real results that customers are willing to pay for??

Fortunately, we've been there, done that, and understand what it takes to build a great data product. That's why we've created a map to help you navigate your way to success, built on the experience of countless voyagers who have sailed the same seas before you; the Data Product Readiness Assessment.

Why your data product needs a good elevator pitch

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In recent years, a term started appearing across the technology world: “data monetization,” turn your data into dollars.. (as we mentioned in a previous, post, you can Find Gold in Your Data!) Businesses reacted to the hype, started spending on every solution under the sun and then… Nothing. Nada. Zilch. In many cases the revenues never materialized, buyers became frustrated with the lack of results and blamed the whole concept of data monetization. The problem is, you’ve got to avoid certain mistakes... and they’re silent killers.

In truth, data products are a great opportunity for most businesses to engage customers and create new streams of revenue. Untapped, dormant data can, when refined properly, become a crucial resource for your company. Fortunately, we’ve worked on many analytics projects ourselves, have seen these mistakes made and have put together a guide to help you avoid making them yourself.

To provide some quick insight, we thought we’d share one of the tips we’ve found most helpful when starting to create an analytics product.

Creating an elevator pitch

What is modern business intelligence?

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Last week, Tableau hosted a session on the evolution of Business Intelligence in Portland that I had the chance to attend. Although I did review their Top 10 trends in BI when they released them earlier this year, the presentation and discussion ended up being pretty interesting. A few of the topics really resonated with me and I thought we could dig into them a bit more.  

For starters:

Modern BI becomes the new normal

The session (and report) kick off by highlighting Gartner’s Business Intelligence Magic Quadrant and the shift away from IT-centric BI over the last 10 years. Regardless of who’s discussing the trends (Gartner, Tableau or otherwise..) and if or when they come to fruition, it’s important to dig deeper. **Reports like those by Gartner are good guideposts for trends and technologies to exam; saw that mentioned somewhere recently, comment for credit.

That said, I think we can agree that the overall landscape of technology and the way that organizations of all sizes are taking advantage of it in the domain of business intelligence has improved over the last decade.

So does that mean modern BI has truly arrived?

Find Gold in Your Data

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"Data Monetization" is a term you might have heard a lot lately.  But what does it really mean for you and your business?  There is gold in your data, but how can you extract it to gain all its benefits without adding resource burdens on your business?  We collected the main approaches successful companies are using to give you inspiration and insight into how you can use data you already have to improve efficiencies, create new revenue streams or increase value and hence your wallet share from your current customer base. 

Use data to make better decisions

It is not always about the big, earth shaking decisions. What if we can empower our employees to choose better paths in incremental fashion? Which ad to place in an available space? How to utilize remaining capacity on a shipment? Those items may each mean just $50.00, or $1,000.00. But people can be easily making 50 decisions like that per day.

Keboola + InterWorks Partnership Offers End-to-End Solutions for Tableau

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We’re always keeping an eye out for BI and analytics experts to add to our fast growing network of partners and we are thrilled to add a long-standing favorite in the Tableau ecosystem! InterWorks, who holds multiple Tableau Partner Awards, is a full spectrum IT and data consulting firm that leverages their experienced talent and powerful partners to deliver maximum value for their clients. (Original announcement from InterWorks here.)  This partnership is focused on enabling consolidated end-to-end data analysis in Tableau.

Whether we’re talking Tableau BI services, data management or infrastructure, InterWorks can deliver everything from quick-strikes (to help get a project going or keep it moving) to longer-term engagements with a focus on enablement and adoption. Their team has a ton of expertise and is also just generally great to work with.

InterWorks will provide professional services to Keboola customers, with the focus on projects using Tableau alongside Keboola Connection, both in North America and in Europe, in collaboration with our respective teams.  “We actually first got into Keboola by using it ourselves,” said InterWorks Principal and Data Practice Lead Brian Bickell. “After seeing how easy it was to connect to multiple sources and then integrate that data into Tableau, we knew it had immediate value for our clients.”

What does this mean for Keboola customers?

InterWorks brings world-class Tableau expertise into the Keboola ecosystem. Our clients using Tableau can have a one-stop-shop for professional services, leveraging both platforms to fully utilize their respective strengths. InterWorks will also utilize Keboola Connection as the backbone for their white-gloves offering for a fully managed Tableau crowned BI stack.

Shared philosophy

Whether working on projects with customers or partners, we both believe that aligning people and philosophy is even more critical than the technology behind it.  To that end, we’ve found in InterWorks a kindred spirit, we believe in being ourselves and having fun, while ensuring we deliver the best results for our shared clients. The notion of continuous learning and trying new things was one of the driving factors behind the partnership.

Have a project you want to discuss with InterWorks?

Contact InterWorks or if you want to learn a bit more about the types of projects they work on, check out their blog!


Keboola #YearInReview: Customer & Partner Highlights

It’s been quite an exciting year for us here at Keboola and the biggest reason for that is our fantastic network of partners and customers -- and of course a huge thanks to our team!  In the spirit of the season, we wanted to take a quick stroll down memory lane and give thanks for some of the big things we were able to be a part of and the people that helped us make them happen!


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Probably the biggest news from a platform perspective this year came about two years after we first announced support for the “nextt” data warehouse called Amazon Redshift.  At the time, it was a huge step in the right direction.  We still use Redshift for some of our projects (typically due to data residency or tool choice) but this year we were thrilled to announce a partnership born in the cloud when we officially made the lightning fast and flexible Snowflake the database of choice behind our storage API and the primary option for our transformation engine. Not to get too far into the technical weeds (you can read the full post here,) but it has helped us deliver a ton of value to our clients (better elasticity and scale, huge performance improvement for concurrent data flows, better “raw” performance by our platform, more competitive pricing for our customers and best of all, some great friends!)  Since our initial announcement, Snowflake joined us in better supporting our European customers by offering a cloud deployment hosted in the EU (Frankfurt!)  We’re very excited to see how this relationship will continue to grow over the next year and beyond!


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One of our favorite things to do as a team is participate in field events so we can get out in the data world and learn about the types of projects people work on, challenges they run into, and find out what’s new and exciting.  It’s also a great chance for our team to spend some time together as we span the globe - sometimes Slack and Goto Meeting isn’t enough!

SeaTug in May

We had the privilege of teaming up with Slalom Consulting to co-host the Seattle Tableau User Group back in May.  Anthony Gould was a gracious host, Frank Blau provided some great perspective on IoT data and of course Keboola’s own Milan Veverka dazzled the crowd with his demonstration focused on NLP and text analysis.  Afterwards, we had the chance to grab a few cocktails, chat with some very interesting people and make a lot of new friends.  This event spawned quite a few conversations around analytics projects; one of the coolest came from a group of University of Washington students who analyzed the sentiment of popular music using Keboola + Tableau Public (check it out.)

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Guiding project requirements for analytics

In a recent post, we started scoping our executive level dashboards and reporting project by mapping out who the primary consumers of the data will be, what their top priorities / challenges are, which data we need and what we are trying to measure.  It might seem like we are ready to start evaluating vendors and building it out the project, but we still have a few more requirements to gather.

What data can we exclude?

With our initial focus around sales analytics, the secondary data we would want to include (NetProspex, Marketo and ToutApp) all integrates fairly seamlessly with the Salesforce so it won't require as much effort on the data prep side.  If we pivot over to our marketing function however, things get a bit murkier.  On the low end this could mean a dozen or so data sources.  But what about our social channels, Google Ads, etc, as well as various spreadsheets.  In more and more instances, particularly for a team managing multiple brands or channels, the number of potential data sources can easily shoot into the dozens.

Although knowing what data we should include is important, what data can we exclude? Unlike the data lake philosophy (Forbes: Why Data Lakes Are Evil,) when we are creating operational level reporting, its important focus on creating value, not to overcomplicating our project with additional data sources that don't actually yield additional value.

Who's going to manage it?

Just as critical to the project as what and how; who’s going to be managing it? What skills do we have out our disposal and how many hours can we allocate for the initial setup as well as ongoing maintenance and change requests?  Will this project be managed by IT, our marketing analytics team, or both? Perhaps IT will manage data warehousing and data integration and the analyst will focus on capturing end user requirements and creating the dashboards and reports.  Depending on who's involved, the functionality of the tools and the languages used will vary. As mentioned in a recent CMS Wire post Buy and Build Your Way to a Modern Business Analytics Platform, its important to take an analytical inventory of what skills we have as well as what tools and resources we already have we may be able to take advantage of.

                                                    

Find the Right Music: Analyzing last.fm data sentiment with Keboola + Tableau

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As we covered in our recent NLP blog, there are a lot of cool use cases for text / sentiment analysis.  One recent instance we found really interesting came out of our May presentation at SeaTUG (Seattle Tableau User Group.)  As part of our presentation / demo we decided to find out what some of the local Tableau users could do with trial access to Keboola; below we’ll highlight what Hong Zhu and a group of students from the University of Washington were able to accomplish with Keboola + Tableau for a class final project!

What class was this for and why did you want to do this for a final project?

We are a group of students at the University of Washington’s department of Human Centered Design and Engineering.  For our class project for HCDE 511 – Information Visualization, we made an interactive tool to visualize music data from Last FM.  We chose the topic of music because all 4 of us are music lovers.

Initially, the project was driven by our interest in having an international perspective on the popularity vs. obscurity of artists and tracks.  However, after interviewing a number of target users, we learned that most of them were not interested in rankings in other countries.  In fact, most of them were not interested in the ranking of artists/tracks at all.  Instead, our target users were interested in having more individualized information and robust search functions, in order to quickly find the right music that is tailored to one’s taste, mood, and occasion.  Therefore, we re-focused our efforts on parsing out the implicit attributes, such as genre and sentiment, from the 50 most-used tags of each track.  That was when Keboola and its NLP plug-in came into play and became instrumental in the success of this project.