Guest post by Kevin Smith
For a product owner, one of the biggest fears is that the product you're about to launch won't get the necessary adoption to achieve success. This might happen for a variety of reasons— two of the most common are a lack of fit to the customers' needs and confusing design (it's just too hard to use!).
To combat the possibility of failure, many product owners have adopted the "agile" approach to building products that have enough functionality to meet to minimum needs, but are still lean enough to facilitate easy change.
As a data product builder — someone building customer-facing analytics that will be part of a product — the needs are no different but achieving agility can be a real challenge. Sure, every analytics platform provider you might consider claims that they can connect to any data, anywhere, but this leaves a lot of wiggle room. Can you really connect to anything? How easy is it? How hard is it to change later? What about [insert new technology on the horizon here] that I just heard about? If you want to build an agile data product, you've got a tough road ahead... as I found out.
Recently I started working on the analytics strategy for a small start-up firm focused on providing services to large enterprises. As they delivered their services, they wanted to show the results in an analytics dashboard instead of the traditional PowerPoint presentation. It would be more timely, easier to deliver, and could be an on-going source of revenue after an engagement was completed. As I spoke with the team, a few goals surfaced:
- They wanted to buy an analytics platform rather than build from scratch. The team realized that they would be better off developing the methodology that would differentiate them from the competition instead of creating the deep functionality already provided by most analytics platforms.
- The system had to be cost-effective both to set-up and to operate. As a start-up, there simply wasn't the cashflow available for costly analytics platforms that required extensive professional services to get started. The product had to be flexible and "configurable" by non-Engineers. With little to no budget for an Engineering staff, the team wanted a BI platform that could be configured easily as customer needs changed.
- Up and running quickly. This company had customers ready to go and needed a solution quickly. It would be essential to get a solution in front of the customers NOW, rather than try to migrate them to a new way of operating once the dashboards were ready. Changes would certainly be needed post-launch, but this was accepted as part of the product strategy.
None of this seemed to be impossible. I've worked on many data products with similar goals and constraints. Product teams always want to have a platform that's cost-effective, doesn't strain the technical capabilities of the organization, is flexible, and is launched sooner rather than later. It was only after a few more conversations that the problem arose: uncertain data sources.
Most data-driven products work like this: you've got a workflow application such as a help desk application or an ordering system that generates data into a database that you control. You know what data is flowing out of the workflow application and therefore, you understand the data that is available for your analytics. You connect to your database, transform the data into an analytics-ready state, then display the information in analytics on a dashboard. The situation here was different. As a services company, this business had to operate in a technology environment dictated by the customer. Some customers might use Salesforce, some might use Sugar CRM. Still others might use Zoho or one of the myriad other CRM platforms available. Although the team would structure the dashboards and analytics based on their best practices and unique methodology, the data driving the analytics product would differ greatly from customer to customer.
As we examined in part 1 of our Data Monetization blog series, the first step to increasing revenue with data is identifying who the analytics will be surfaced to, what their top priorities are, what questions we need to ask and which data sources we need to include. For this blog, let’s take a look at what tools we will need to bring it all together.
With our initial example of a VP of Sales dashboard, fortunately the secondary data sources (NetProspex, Marketo and HubSpot Signals) all integrate fairly seamlessly with the Salesforce CRM. This should allow for some fairly straightforward analytics built on top of all the data we’ve aggregated. If we pivot over to our CMO dashboard, things get a bit murkier.
Although our Marketo instance easily integrates with Salesforce, the sheer volume of data sources that can provide insight to our marketing activity makes this project a much more daunting ask. What about our social channels, Adobe Omniture, Google Ads, LinkedIn Ads, Facebook Ads, SEO as well as various spreadsheets. In more and more instances, especially for a team managing multiple brands / channels, this number can easily shoot into the dozens.
When a company thinks about monetizing data, the things that come to mind are increasing revenue, identifying operational inefficiencies or creating a new revenue stream. It’s important to keep in mind that these are the results of an effective strategy but can't be the only goal of the project. In this blog series, we will exam these avenues with a focus on the added value that ultimately leads to monetization. For this blog, lets look at it from the perspective of creating executive level dashboards at a B2B software company.
Who will be consuming the data and what do they care about?
Before we jump into the data itself, take a step back and understand who the analytics will be surfaced to and what their challenges are. Make profiles with their top priorities, pain points and the questions they will be asking. One way to get started is to make a persona priority matrix listing the top three to five challenges for each (ex. below.)
Once the matrix is laid out, you can begin mapping specific questions to each priority. What answers might help a VP of Sales increase the effectiveness of the sales team and ultimately revenue?
What do our highest velocity deals look like (vertical, company size, who’s involved)?
What do our largest deals look like?
Where do our deals typically get stuck in the sales process?
What activities and actions are our best reps performing?
Data can be vast and overwhelming, so understanding the different types helps to simplify what kind of numbers we are looking for. Even with the treasure trove of data most organizations have in-house, there are tons of additional data sets that can be included in a project to add valuable context and create even deeper insights. It’s important to keep in mind what type of data it is, when and where it was created, what else was going on in the world when this data was created, and so forth. Using the example of a restaurant, let’s look at some different types of data and how they could impact an analytics project.
Numerical data is something that is measurable and always expressed in numerical form. For example, the number of diners attending a particular restaurant over the course of a month or the number of appetizers sold during a dinner service. This can be segmented into two sub-categories.
Discrete data represent items that can be counted and is listed as an exact number and take on possible values that can be listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity (making it countably infinite). For example:
Number of diners that ate at the restaurant on a particular day (you can’t have half a diner.)
Amount of beverages sold each week.
How many employees were staffed at the restaurant on a day.
Continuous data represent measurements; their possible values cannot be counted and can only be described using intervals on the real number line. For example, the exact amount of vodka left in the bottle would be continuous data from 0 mL to 750 mL, represented by the interval [0, 750], inclusive. Other examples:
Pounds of steak sold during dinner service
The high temperature in the city on a particular day
How many ounces of wine was poured in a given week
You should be able to do most mathematical operations on numerical data as well as list in ascending/descending order and display in fractions.
Its that time of year again and there are so many gift options to choose from. Be it hover-boards (that may explode,) drones or Star Wars’ own BB-8 remote control droid, there’s been quite a boom in tech gadgets this year. At Keboola we love all things data, so to get you in the holiday spirit, we wanted to share some cool gift ideas that use data to make your life easier (or at least a bit more interesting.)
Similar to the gadget seen in the Progressive commercials, the Automatic Adapter is basically a fitness app for your vehicle. It provides a full report on behavior through an app or a web interface regarding where you’ve been, driving behavior and even tag routes for business travel expenses.
The Economist Intelligence report Big data evolution: forging new corporate capabilities for the long term published earlier this year provided insight into big data projects from 550 executives across the globe. When asked what their company’s most significant challenges are related to big data initiatives, maintaining data quality, collecting and managing vast amounts of data and ensuring good data governance were 3 of the top 4 (data security and privacy was number 3.) Data availability and extracting value were actually near the bottom. This is a bit surprising as ensuring good data quality and governance is critical to getting the most value from your data project.
Maintaining data quality
Having the right data and accurate data is instrumental in the success of a big data project. Depending on the focus, data doesn’t always have to be 100% accurate to provide business benefit, numbers that are 98% confident is enough to give you insight into your business. That being said, with the sheer volume and sources available for a big data project, this is a big challenge. The first issue is ensuring that the original system of record is accurate (the sales rep updated Salesforce correctly, the person filled out the webform accurately, and so forth) as the data needs to be cleaned before integration. I’ve personally worked through CRM data projects; doing cleanup and de-duping can take a lot of resources. Once this is completed, procedures for regularly auditing the data should be put in place. With the ultimate goal of creating a single source of truth, understanding where the data came from and what happened to it is also a top priority. Tracking and understanding data lineage will help identify issues or anomalies within the project.
Collecting and managing vast amounts of data
Before the results of a big data project can be realized, processes and systems need to be put into place to bring these disparate sources together. With data living in databases, cloud sources, spreadsheets and the like, bringing all the disparate sources together into a database or trying to fuse incompatible sources can be complex. Typically, this process consists of using a data warehouse + ETL tool or custom solution to cobble everything together. Another option is to create a networked database that pulls in all the data directly, this route also requires a lot of resources. One of the challenges with these methods is the amount of expertise, development and resources required. This spans from database administration to expertise in using an ETL tool. It doesn’t end there unfortunately; this is an ongoing process that will require regular attention.
Ensuring good data governance
In a nutshell, data governance is the policies, procedures and standards an organization applies to its data assets. Ensuring good data governance requires an organization to have cross-functional agreement, documentation and execution. This needs to be a collaborative effort between executives, line of business managers and IT. These programs will vary based on their focusbut will all involve creating rules, resolving conflicts and providing ongoing services. Verifications should be put into place that confirm the standards are being met across the organization.
Having a successful big data project requires a combination of planning, people, collaboration, technology and focus to realize maximum business value. At Keboola, we focus on optimizing data quality and integration in our goal to provide organizations with a platform to truly collaborate on their data assets. If you’re interested in learning more you can check out a few of our customer stories.
The Keboola Data App Store has a fresh new addition. That brings us to total of 16 currently available apps, three of which provided by development partners.
This new one is called “aLook Analytics”, and technically it is a clone of our development project, a “Custom Science” app (not available yet, but soon!). It facilitates connection to a GitHub/Bitbucket repository of a specific data science shop, which you can “hire” via the app and enable them to safely work on your project.
The user of KBC does not have direct access to the script, protecting aLook’s IP (of course, if you agree with them otherwise, we do not put up any barriers).
Very soon we will enable the generic “Custom Science” app mentioned above. That means that any data science pro can connect their GitHub/Bitbucket themselves - that gives you, our user, the freedom to find the best brain in the world for your job.
Why people and not just machines?No “Machine Learning Drag&Drop” app provides the same quality as a bit of thought by a seasoned data scientist. We’re talking business analytics here! People can put things in context and be creative, while all machines can do is to adjust (sometimes thousands of) parameters and tests the results against a training set. That may be awesome for facial recognition or self-driving car AI, but in any specific business application, a trained brain will beat the machine. Often you don’t even have enough of a test sample so a bit of abstract thinking is critical and irreplaceable.
As soon as we saw it, the Keboola team thought, “What an exciting way to use data from Keboola Connection - if only we could send data to it immediately to test it!” The app is built to accept .xls and .csv files that are physically present on the iPad it runs from, so at a glance, it is completely and utterly off-line. We immediately wondered if Keboola Connection - due to its integration with DropBox and Google Drive - could make Vizable the ultimate, on-the-go data visualization app.
(a little bit of frantic testing later)
Yeah! We can easily schedule pushing data into the iPad using our existing integrations. We didn't have to write a single line of code and already during the conference we were able to play with #data15 mentions we’d pulled in through Keboola Connection, with fresh data being automatically pushed into the iPad every 30 minutes.
We eagerly shared our success with the Vizable team and started showing conference attendees and members of the Tableau team just how we’d made it all happen! It was great to receive a string of visits from the whole Vizable crew all the way up to Dave Story, VP of Mobile and Strategic Growth, and Chris Stolte, the Chief Development Officer. What a thrilling way to educate the Tableau folks on all the cool stuff Keboola does with their tool and for their customers.
Get in touch with us if you want to know more!
During my midnight oil hours and rumbling through out our internal systems, I have come across the ZenDesk tickets that our data analysts are closing for one of hour clients - H1 agency (part of GroupM).
My arrival at Business Intelligence (BI) and eventually consulting for Keboola was not through your standard Statistics or Programming route. After completing a Bachelor’s degree in Business & Communications, I had several stints coordinating corporate marketing efforts in various industries from Automotive to Gaming. I found that no matter what capacity I worked in - from tracking call-to-actions, to analyzing performance reports from suppliers and conducting market research - I could not hide from data.
So in my never ending quest for efficiency I also started to ask myself why am I doing this and how meaningful is it? Or simply put, am I wasting my time coming up with tweets no one reads. #HashtagAllTheThings
Traditionally data analysis was never a core a competency of marketing, someone else from purchasing, finance, IT etc. would tell you if your campaign was successful. But with the shift towards digital marketing there’s been an increase in data availability and now more control over how marketers themselves can measure KPIs.
It was this trend that made me first curious about making the switch from Marketing to Analytics. The organizational gap was so apparent to me, but I had no idea what that translated to in terms of a job description. I was stuck between working in marketing where (for obvious reasons) the primary focus is on campaign implementation before measurement vs. a highly technical position (which I didn’t even have the qualifications for) that would stifle my creative side.
Caught in the middle, I came across a job posting at Keboola for a “Data Analyst”. At the time I had no idea what I was applying for, but through my experience in the past year I now see that the job description couldn’t have been clearer. Keboola like me is somewhere in the middle. With a pragmatic approach, we provide real solutions to our clients’ very real business issues.
What I love about working here is that we help companies integrate both Market Intelligence (MI) and Business Intelligence (BI) for data-driven decision making.
In my role, I provide the (BI) tools to answer the “why” behind my clients’ marketing decisions and then visualize those findings so they can make more informed decisions (MI). What I’ve found through this experience that there are common problems afflicting marketers, for which I feel there is actually a solution already under your nose. My job at Keboola is to translate these observations into something actionable so marketers can be empowered to work with their data and spend their time creating something meaningful … less hashtags in the next tweet perhaps?