Is there untapped value in your data?

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Embedded analytics, data products, data monetization, big data….there are plenty of buzz words we can use to “categorize” the idea.

IDC reports that the big data and business analytics market growing at a rate of over 11% in 2016 and at a compound annual growth rate of 11.7% through to 2020. This rapidly growing area of investment can’t be for naught….can it?

Let’s look beyond the hype at some specific approaches for extracting additional value (and ultimately dollars) from your data.

According to Gartner, Data Monetization refers to using data for quantifiable economic benefit.

The first thing that may come to mind is outright selling of data (via a data broker or independently.)  Although a potentially viable option, with increased data privacy policies and the sheer amount of data needed to be successful with this approach, it can be quite limiting.

There are many other approaches to monetizing your data, such as:

How Keboola Switched to Automatic Invoicing

We’ve been assisting people with data and automation for a while now, helping them become “data-driven.” Several months ago, we had an exciting opportunity to automate within Keboola itself. After we lost our two main finance wizards to the joys of childcare, we decided to automate our internal business processes.

There was a lot of work ahead of us. For years, the two of them had been issuing invoices one by one. Manually. Hating unnecessary manual tasks, we were eager to put the power of our platform — Keboola Connection into work and eliminate the manual invoicing.

We expected approximately 2-3 mandays per month to be cut down. We also wanted to get much better data about what’s going on.

As our sales activities have been taking off around the globe, we would need to automate this process anyway. Otherwise soon we would have to hire a new employee just for invoicing and that is a no-go for us. Plus we didn’t want to overload Tereza, our new colleague, with this tedious work and take away her weekends from her. 

When it comes to data, we often preach the agile methodology: Start small, build quick, fail fast and have the results in production from day one - slow Kaizen style improvement. This is exactly what we did with our invoicing automation project. We didn’t want to have someone write a custom app for us. We wanted to hack our current systems, prototype, fail fast and see where it would lead us. We wanted to save Tereza’s time but didn’t want to waste it 10x in the development of the system. :-)

Our “budget” was 3-4 mandays max!


Step 1  —  Looking for a tool to use for the invoicing

We were looking for a tool which can handle all the basic things we need: different currencies (it’s Europe!), different bank accounts, with or without tax, paid or unpaid, and a handful of other features. Last but not least, the tool HAD to have a nice RESTful API. After some trials we opted for a Czech system – Fakturoid. They have great support, by the way. That’s a big plus.

Step 2  — Getting data about customers from Fakturoid into Keboola Connection

First, Padak took all clients we already had in Flexibee, our accounting tool, and exported them to Fakturoid. Then we added all the necessary info to the contacts.

Great. Now we had all the customers’ records ready and needed to get the data into Keboola Connection. It was time to set up our Generic Extractor. It literally took me half an hour to do it! Check it out here:


Keboola Generic extractor config for getting clients’ info from Fakturoid into Keboola Connection

Step 3  —  Creating two tables with invoices and their items for uploading into Fakturoid

There was only one more thing to know. Who is supposed to pay for what and when? We store this info in our Google Spreadsheet. It contains basic info about our clients, the services they use, the price they pay for them, the invoicing period (yearly, quarterly, monthly), and the time period for which the info is valid (when their contract is valid; new contract/change = new row). To be able to pair the tables easily, we added a new column with the Fakturoid client ID.

Finally, we set up our Google Drive Extractor and loaded the data into Keboola Connection. Once we had all the data there, we used SQL to create a Transformation that took everything necessary from the tables (who we bill this month, how much, if out of country = don’t put VAT, add info about current exchange rate, etc.) and created clean output tables.

Part of the SQL transformation which creates an output table with items to pay for Fakturoid.

Step 4  — Sending the tables into Fakturoid and letting it create the invoices

This step was not as easy as exporting data from Fakturoid. We couldn’t use any preprogrammed services. Thankfully, Keboola Connection is an open environment and any developer can augment it and add new code to extend its functionality. Just wrap it up in Docker container. We asked Vlado to write a new writer for Fakturoid which would take the output tables from our Transformation (see Step 3) and create invoices in Fakturoid from the data in those tables.

It took Vlado only 2 hours to have the writer up and running!

Now when the writer is completed, Keboola Connection has one more component which is available to all its users.

Step 5 — Automating  the whole process

It was the easiest part. We used our Orchestration services inside Keboola Connection and created an orchestration which automatically starts on the first day of each month. Five minutes later, all the invoices are done and sent out. #easypeasy

Summary:

It is not a complicated solution. No rocket science. We believe in splitting big problems into smaller pieces, solving the small parts and putting them back together just like Lego bricks. The process should be easy, fast, open and put together from self-contained components. So when you have a problem in one part, it doesn’t affect the whole solution and you can easily fix it.

Saving Tereza’s time, this is the springboard for automating other parts of her job. We want her to spend more time doing more interesting things. And the process scales as we grow.

It took us:

  • 4 hours to analyse and understand the problem and how things are connected,
  • 1 hour to export clients from the accounting system,
  • 1/2 hour to write a Generic Extractor from Fakturoid,
  • 2 hours to write a Transformation preparing clean output data,
  • 2 hours to develop a new Writer for Fakturoid, and
  • 1-2 hours to do everything else related to the whole proces.

Total = circa 11 hours

Spoiler alert: I’m already working on further articles from the automation series. Look forward to reading how we implemented automatic distribution of invoices to clients and the accounting company, or how we let the systems handle our invoicing for implementation work.

How to build data products that increase user engagement

Think about all the social media platforms out there, which ones do you use the most (and why)? I’m not talking about giving your LinkedIn profile a face lift before you put in a job application or searching for a long lost friend on Facebook; which of these apps are actually driving user engagement? For me, it’s Instagram; the interface is easy to navigate and more than once I’ve found myself re-opening it after I’ve just closed it. Have you thought about why many of these platforms have exploded in user engagement with many people posting to their Twitter or Facebook accounts multiple times per day? According to a recent Gartner blog, adoption rate for some of the BI tools in their Magic Quadrant are at a low but not too surprising 21%. Are people sick and tired of “all that data” or is there something more sinister at work…

We’ve thought a lot about social media platforms (and other apps) that seem to drive such high user engagement and put together a few thoughts on how you can do the same within your data product to ensure you keep users coming back for more. Before we reveal the secret sauce for building engagement in your data products, let’s take a quick look at how many analytics teams approach the problem.

Too often, teams building an analytics product for this customer’s approach the project in the wrong way, the story is oh so familiar. As we covered in a recent blog, this meant taking the reports existing in an Excel spreadsheet and web-ifying them in a cloud BI tool. It’s essentially surfacing the exact same information as before, but now with shiny new charts and graphs, more color choices, and some interactivity. After the initial excitement over the new toy in the room, the latter solution isn’t doing any better than the former at driving engagement; let alone delivering “insights” or creating a new revenue stream.

One of the big reasons customer aren’t lining up to write a check for the latest, greatest data product a vendor has rolled out is that the analytics team failed to make it engaging. Simply put, product teams need to let users know “hey—check this out,” “hey—we’ve got some important information for you”, and “hey—you should come back and see us.” Most teams do the second part, the “we’ve got insights” piece, but they fail to inform users why they need to keep coming back for more. These are essential elements of establishing engagement; not building these in is like skipping the foundation of a new skyscraper. "It's like when you see a skyscraper; you're impressed by the height, but nobody is impressed by the foundation. But make no mistake, it's important," said Akshay Tandon, Head of Strategy & Analytics at LendingTree.

Want to avoid the killer mistakes of failing to build engagement into your data product? Here’s how:

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. 

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 anyway...?

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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?

Although some ideas come to mind when I hear the phrase..

What is modern business intelligence?  

And do we all think of the same things when we discuss it….?


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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.

First Principles: The Foundation of a Great Data Product

To kick-off our new series about creating data products, we decided to write a white paper. This sounds simple, but this time it was a little more difficult than we expected.

Specifically, where do we start when we want to explain the difficulties data product teams face and how to overcome the critical obstacles? Should we begin with user personas and how to design data products that engage users? Do we kick things off with a piece about pricing data products and the finer points of ensuring future up-sell paths? How about a few words explain why data products that don’t use Keboola are doomed to fail and bring shame upon their product teams and ultimately their entire company? Hmmm... All possibilities, but none of these seemed the best way to start our series.
 
After much thought and coffee, we decided to start at the beginning with “first principles”—those foundational attributes which distinguish successful analytical applications from those that don’t quite meet their objectives. Our white paper would discuss these principles that make a data product truly great.
 
Wait—isn’t that a little vague, a little “fluffy”? Not at all. We felt compelled to start with these principles because, while not as mathematical as pricing or as black and white as dashboard design, it can be hard to know where to begin when you’re part of
a product team charged with building an analytics product. First principles act as guide post to help you stay on the right path.
 
These guide post are essential for product team because it isn’t easy trying create analytics that have a positive impact both for users and on your company’s bottom line. Do you start by setting revenue targets and determining the cost structure that
needs to be achieved in order for the data product to be profitable? Maybe you start by defining the various reports and information that you need to put in the hands of your customers to solve their problems and reduce the deluge of “more data” requests. Or perhaps you could start by brainstorming a list of all of the features that might make users engage with the analytics—requests you’ve received or functionality that is present in your competitors’ products.
 
Each of these paths is a reasonable starting point, but are any of them the best way to begin the process of building a great data product? That's where first principles come into play.
 
First principles don’t have anything to do with bar charts versus pie charts or even technology selection. Instead, they are a set of guiding beliefs about what makes a data product great. They are foundational truths and from them, everything else—features, pricing, and product strategy—follow.
 
As we start our series on creating data products, we felt that our first principles were a great place to begin and so, we’d like to share them with you in a white paper. Before you start to think that these principles will be a rehash of all the modern catchphrases such as “embrace the change” or “empower each other”—these are directly targeted at creating successful data products. They are a collection of elements that we’ve seen in great, successful analytics-based products and are the place where we always begin when considering each project.
 
We hope that you find these elements of a great data product useful in your journey to deliver analytics to your customers and, as always, we’re here to help if you’d like to build a data product together.
 
 
Thanks!