Monday, July 17, 2017

Add Predictive Analytics on Your Dataset in 3 Steps

Knowi Predictive Analytics brings machine learning capabilities to every Data Engineer.

Add Predictive Analytics on Your Datasets
Integrate machine learning into your analytics workflows and drive data directed actions.

The platform provides two options:
  • Out-of-the-box predictive analytics capabilities that test a dataset against a variety of forecasting models to determine the model best suited to the data,  with the least Sum of Absolute Errors (SAE).  
  • Built-in Predictive and Machine Learning algorithms that can be plugged into data workflows.
This post focuses on the first option above that takes a hands-on look at how it works.  We'll take monthly stock prices for Amazon to determine predicted values over a three month period starting in July in a few simple steps. No signup is required to follow along.  

Models used include:
  • Simple Exponential, Double Exponential, Triple Exponential Smoothing Models
  • Moving Averages and Weighted Moving Averages
  • Naive Forecasting Model
  • Regression and Polynomial Regression Model
  • Multiple Linear Regression Model

The monthly stock prices looks like this:

Date, Price
06/01/16, 719.14
05/01/16, 683.85
04/01/16, 598.00
03/01/16, 579.00
02/01/16, 574.81
01/05/16, 633.79
12/01/15, 679.36
11/2/15, 628.35
10/1/15, 625.90
9/1/15, 511.89
8/3/15, 512.89
7/1/15, 536.15
6/1/15, 434.09
5/1/15, 429.23
4/1/15, 421.78
3/2/15, 372.10
2/2/15, 380.16
1/2/15, 354.53


1. Copy and paste the above dataset into

2. Click on Show me. The data will be parsed and visualized immediately. 

3. To perform predictions:

   i. Click on Analyze from the menu of the time series chart. This opens up an Analysis mode.
  ii. Drag date into the Grouping field. 
  iii. Click on 'Add a derived Field' option. Enter a name ("Predictions", for example) and in the operation, enter predict(price,date,07/01/2016,1m,3). This will choose the best model based on historical accuracy of the model to determine the projected prices over a three month period, on a monthly basis. 

That's it! In a few simple steps, you can apply predictive analytics on any of your own datasets. Enjoy!

To get started with Knowi Predictive Analytics head to and try it out free.

Additional Machine Learning Resources:

Predictive Analysis docs:
Advanced Machine Learning (AI) Capabilities:
All Documentation:

Monday, July 10, 2017

How Boku Successfully Transitioned to a Data-Driven Enterprise Using Knowi

Boku Testimonial - KnowiWe had a chance to sit down with our friends at Boku recently and talk about how they are using Knowi to provide business analytics across all their teams, including product, accounts, and business development, and have recently extended their analytics as value-added product offerings to their partners and customers.

If you are not familiar with Boku Inc. they are a global mobile payments platform enabling customers to pay for goods and services using their mobile phone number. 

THEIR CHALLENGE is to serve their growing merchant client base, the account management and business development teams needed access to a complete view of their client accounts status but the only way to access that data was to query the database.  

As a payment platform serving dozens of countries, Boku analyzes terabytes of data and the business needs results returned within seconds to serve their customers.  

THE SOLUTION in Boku’s case is a hybrid deployment of Knowi enabled a cloud implementation of Knowi connected to an on-premise instance of MySQL.  Query results are cached in Knowi’s Elastic Store, so visualizations are rendered within seconds providing an excellent user experience while ensuring the database performance is not impacted by query requests.

Boku also took advantage of Knowi’s ability to share and embed dashboards into data applications. Embedding dashboards eliminated the need for business users to have any expertise in SQL, a key goal for the team.  Business users simply go to their customized dashboard and use a drag & drop interface to run ad-hoc analysis on any dataset they are authorized to access.


"Knowi’s ability to handle our large volumes of data and let everyone here at Boku to quickly access and share information was critical to becoming a data-driven organization.  By using Knowi to leverage our data company-wide, we were able to accelerate a number of strategic goals with our partners."

Mike Cahill, CTO, Boku

Knowi was rapidly adopted company-wide to include the product management, finance, and operations teams.  Product managers use it to understand how customers are using the platform, tune the product and find ways for partners to save money.  The finance and operations teams use it to monitor company-wide key performance metrics. 

Because Boku was able to implement Knowi in place of an enterprise data warehouse strategy, they estimate up to a 70% reduction in their cost of information.  Additionally, the account and business development teams have increased upsell/cross-sell opportunities by at least 15% directly impacting the bottom-line.

Finally, the business intelligence team uncovered an opportunity to monetize Boku’s transactional data and create new data-driven product offerings to sell to partners who need the same level of transactional analytics that Boku is producing internally.  As a result, “our B.I. team that is normally a cost center can now generate revenue, so that’s a great bonus for the team” says Walter.

To read or download Boku's full story, head over to our resources page here.  

Thanks to the team at Boku for allowing us to tell their story!