Tuesday, July 23, 2019

Knowi’s Platform Simplifies The Process Of Compiling And Analyzing Unstructured And Structured Data

As originally posted on Tech Company News on June 28th, 2019. Click here to view original publishing
Below is our recent interview with Ryan Levy, Chief Operating Officer at Knowi:
Q: Could you provide our readers with a brief introduction to Knowi?
A: Knowi is an Augmented Analytics company. Our platform simplifies the process of compiling and analyzing unstructured and structured data. Recently, we developed an anthem to help consumers understand what we do in a simplistic manner: ‘Any, any, any, any.’ This breaks
down to any data, anywhere, any size, for anyone.
Knowi instantly connects to any data, no matter what the data is; structured, unstructured or modern/messy data. And we don’t care where the data is – on your site, in the cloud, on shore or global, our platform connects to your data from anywhere. Your data could be any size – small, medium, large, big data, massive data; our platform was made for this.
Where it starts to get most exciting is the anyone – the people. You no longer need to have technical skills or submit a request to a technical team member to provide you with insights to your data. Users simply login to Knowi and can access and view dashboard widgets or build customized reporting on the spot. Your data; your way.

Q: Can you give us insights into your platform?
A: Knowi is not just another BI or analytics tool. We are a full-fledged, end-to-end, Augmented Analytics platform. That means that we are a platform composed of multiple modules that address the needs of our customers and provide the capability and functionality they need in order to see the data the way they need to see it.
At the core of our platform is our Data-as-a-Service. This is where our ‘data science engine’ allows users to natively and instantly connect to all and any disparate data sources (structured, unstructured or everything and anything in between) and then extracts the data to create visualization dashboards. These visualizations are completely customizable based on the unique needs of each of our customer’s businesses and are easy to understand and manipulate without any technical knowledge. It doesn’t matter who you are in the organization, you have the ability to see your data how you want it, when you want it.
And if that wasn’t enough data power for you already, we then layer AI on top in the form of machine-learning (encompassing Classification, Regression and Anomaly Detection).
The whole premise around Knowi is to provide a simple, self-service BI platform that covers everything from connecting to your data, providing the AI piece, the machine learning piece, the natural language processing piece and then the actions and visualizations behind it.
Q: You’ve recently announced the latest release of Natural Language BI 2.0; could you tell us something more?
A: We are super excited about this release because now users can query their data in a “google like” search functionality. Knowi users can use their natural language, without having to understand how to write complex queries or any kind of querying syntax. In addition, you can simply ask a question of your data in real-time to get insights immediately. And just as important, we don’t restrict the data required to answer the question from a single data source, we allow you to ask questions across all your data sources.
Q: Why is now the time to change how we do analytics?
A: For decades, every company has been driven by data; whether you are the pizza shop on the corner or a billion dollar software company. We are looking at numbers, defining our return on investments, deciding what is our next move to grow – and it’s all data. But the data has become unmanageable and silo’d. We have made it easy to collect data but we haven’t really focused much on how to make it easier to extract and analyze that data.
The old way of running analytics and managing these data sources to provide some kind of visualization has been a complex process. It’s been intensive from a resource, and a cost perspective, because you’ve previously been required to use multiple tools and multiple people to do something to your data. Which means by the time you get your answer, the data may have changed entirely.
Also consider this, analytics as we know it has traditionally been a backwards view. Most tools today look at data that has already taken place and that is what they are designed to do. With Augmented Analytics, we are now combining the concept of AI and BI to be able to create models and give customers the ability to see what may happen and what actions may be taken. This is what you may have heard of as predictive and prescriptive analytics.
The reason why we built the platform that we did, and why we didn’t just build a visualization tool or a Data-as-a-Service tool, is because we want it to be scalable and to grow with the industry. We will always have data, so let’s fix the way we use it and access it now so that it continues to work for us in the future.
Q: You’re not a typical silicon valley startup – what makes you different?
A: The way the company was originally formed was an idea to address modern data architecture complexity. Built truly out of a basement in Oakland and boot strapped with no institutional funding, we waited for a substantial period of time to prove that our product was viable and could do what we said it could, before we actually recruited customers and users.
Generally, startups will raise funds based on a concept, and use those funds to build a team and then go out and procure customers. We kind of did it backwards in that respect. As a generation 3 platform, our vision is to lead the wave of Augmented Analytics solutions that will transform how enterprises are run. We believe we have a great customer base currently and have allowed ourselves plenty of room to grow.

Q: What are your plans for the future?
A: From a business perspective, we’ll continue to be a leading force in Augmented Analytics. Knowi is different than other tools available to businesses today and we will continue to focus on how our platform allows organizations to use data to transform their business states.
We will continue to focus our investment, our expertise and our go to market around not just Augmented Analytics and the view of data as of today, but really driving true value out of where this value is going to go.
In the end, it is really more about redirecting how we can help organizations understand what they can actually do with the data and how valuable the data is rather than trying to figure out how we are going to untangle it and make sense of it. And that is the core principle behind Knowi.





Learn More: For a free 21 day trial to enhance your data analytics and be a part of the movement towards Augmented Analytics click here.


Monday, July 22, 2019

MongoDB Aggregations — Part 1

As originally posted on Medium on June 25th. Click here to go to the original source

Getting Started Aggregating Data with MongoDB



MongoDB is an open-source, NoSQL database built to simplify storage of large, document-based, unstructured data. This article is the first of a 3-part series on MongoDB analytics, with the purpose of showing how to aggregate data in MongoDB and learn correct MongoDB query syntax using Knowi.

Why put data in MongoDB?

Unstructured data has become more prevalent throughout the past decade as the number of collection points have increased across most business technology stacks — with the IDC estimating that 80% of enterprise data remains unstructured.
Each new collection point provides a different lens to view an organization’s health: mobile data is growing exponentially from phones & laptops across the world, while text-based information such as customer support conversations and web-page traffic provide new ways to understand the channels of communication that drive every forward-thinking business. The amount of unstructured information across business ecosystems will continue to expand dramatically throughout the 2020’s.
Given the velocity and volume of data from these sources, MongoDB offers a premier, NoSQL solution to flexibly store, index, and query the proliferating mass of unstructured data. Unlike relational databases, MongoDB does not require a schema defined upfront; each data object is stored as a separate document inside a “collection”. Queries on MongoDB can be executed ad-hoc to return data based on fields, ranges, or regular expression using Javascript.
Perfect for building fast-scaling apps, Mongo is simple to set-up. The rest of this article will explore how to aggregate MongoDB-based data to prepare it for downstream purposes.

How to aggregate data in MongoDB

Most organizations run queries against MongoDB using the default Javascript command line client. However, MongoDB can also be queried using Python, PHP, C#, Perl, Ruby, or MongoDB Compass GUI.
Here’s an example of how to execute a query in MongoDB:


This will return all of the metrics associated to the ‘Collection to query’ specified inside MongoDB.
Aggregation is critical for processing data to return computed results. In Mongo, aggregations can be used to group values from multiple documents and perform calculations on the grouped data to return a single result. This is a vital step to prepare data for analytics, as aggregating unstructured data enables teams to find trends and correlations between data-points and prepare for downstream analytics functions.
Inside MongoDB, there are three main ways to aggregate data: the aggregation pipeline, the map-reduce function, and single purpose aggregation methods (links to MongoDB documentation provided).
MongoDB’s aggregation framework is modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into an aggregated result. A few important examples include -

  • $group — groups documents by specified expression & outputs to next stage by distinct grouping characterized by _id field. Outputted documents can include accumulator expressions as part of the grouping by _id field. This is expressed as:



  • $filter- will put a specific subset based on specified filter condition including only elements that match the condition. This is expressed as:


  • $match — can be used to filter the number of documents passed between stages. Match should be used early in the aggregation pipeline. This is expressed as:

  • $limit — limits the documents for the next stage by specified number, only passing through the amount of documents specified. This is expressed as:


  • $project — pass documents with specified fields to next stage, helping aggregate data by specific categories. This is expressed as:


These aggregations can be used to drive functionality from data including building analytics visualizations, machine-learning prediction workflows, and pushing data into applications. For more specifics on different aggregation methods possible inside of MongoDB, check out their documentation page here.

Learning & Practicing MongoDB aggregations using Knowi

Knowi is an augmented analytics platform that enables teams to create queries on NoSQL databases like MongoDB, Couchbase, and Cassandra using a point & click interface. Knowi can be used to generate queries on MongoDB, and review proper syntax for aggregating data. Let’s walk through an example of setting up a MongoDB aggregation in Knowi
First — head to Knowi’s MongoDB Querying page. From here, you can immediately access a cloud-hosted live demo of MongoDB database, start running queries, and aggregating data using Knowi on the cloud.



Second — in the “Query Builder” section — click on Collections & choose “sendingActivity”. Notice that as you changed the MongoDB collection, the native MongoDB query generator automatically built the query under the “query editor”. This is a great way to learn how to write aggregations and queries in MongoDB, feel free to try the different steps with other collections hosted in the Knowi trial database or your own MongoDB data.
Third — let’s run through an aggregation. Click the drop-down for “Measures and Groups” & click into the metrics box. Select “customer” and “sent” as the metrics to query. Notice that as each field is selected, the query automatically updates on the right side of the screen.

Double click the box for “Sent”, in the operations box choose “Sum” and Ok. In the query-editor on the right, you can immediately see that the query has been edited with the sum aggregation included to look like. This functionality can be used to see how to write MongoDB aggregations inside Knowi




Using Knowi’s UI, any MongoDB novice can quickly begin writing queries to help unlock their understanding of the data available, and the best aggregations to perform on said data. Now let’s try a grouping aggregation using Knowi. Press into the “Dimensions/Group By” box and select “date” — this will be immediately reflected in the query editor, where the $group sequence has been completed with date as the grouping id.



We’ve now performed two aggregations in MongoDB using Know including a summation of the number of sent messages in a Mongo collection & grouping of documents by date. The value of this will become apparent when you select “Show me”, as the result of aggregations can be immediately displayed.
In the upcoming MongoDB Aggregations — Part 2, we’ll explore how Knowi can help you blend collections of data in MongoDB with other sources of unstructured data like CouchBase and DataStax, as well as relational data systems like PostgreSQL or Snowflake.


Learn more about Knowi's ability to blend data in MongoDB by visiting our website and starting your own 21 day free trial


Friday, July 12, 2019

Leading the New Wave of Advanced Analytics

The modern data stack is changing quickly - is your analytics infrastructure following suit? Traditional analytics architectures don’t handle unstructured data well and often requires dreaded ETL. We’ve eliminated the ETL nonsense with native integrations into modern and traditional datasources. And for the record, ODBC drivers do not natively integrate (thought we'd save you the time and trouble).


Knowi offers drag & drop analysis, customizable visualizations, trigger notifications, embeddable dashboards and more. For advanced cases, our Adaptive Intelligence architecture blends Machine Learning with BI to drive actions.





Learn more: For a 21 day free trial click here