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KNIME Analytics

A review of KNIME Analytics software for use in direct marketing business intelligence operations

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KNIME Analytics Platform

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Is a powerful free open source data mining tool which enables data scientists to create independent applications and services through a drag and drop interface. It can serve well as a business intelligence resource, which can be used for business intelligence and data analytics.The software is available as a free download on their website.

For the purposes of direct marketing, KNIME will allow converting multiple data sources, spreadsheets, flat files, databases, and more into a standard format. This data can be normalized, analyzed, and configured to generate visual representations. In other words, it can shape data into information. This data aggregation provides the possibility to create easy to understand visualizations.

KNIME can be used as a key component of their marketing technology stack by direct marketers to gain better understanding of the large amounts of data involved with a direct marketing operation.

Many business intelligence features are built in. There are numerous data visualization tools which can be used for creating larger applications, and with some configuration, it can create an extremely powerful dashboard for analyzing direct marketing data.

Getting started with KNIME takes some configuration; it is not an out-of- the-box solution. There are numerous templates that can be configured for a multitude of purposes, however there are no direct-marketing specific ones. That said, the functionality is certainly possible to configure to meet Business Intelligence needs for use in direct marketing operations.
The modular nature of KNIME makes it possible to create brand-new workflows which can be well-adapted to a BI dashboard. There are many useful features and modules that do not need to be built from scratch; in many cases it merely requires configuring the data itself to use pre-existing structures.

Once configured, this will enable marketers to create various different types of reports, and can theoretically help gain a much better understanding of users and target markets.

Features

Node System

To understand KNIME it's important to understand the node system. Each node represents an individual discrete function. Each one provides a different role, ranging from reading data from a spreadsheet or other format, normalizing this data, and converting it into a new format where it can be viewed. Each node is chosen from a list, dropped into a worksheet, and configured depending on its function or purpose, to create an easily understandable data science workflow.

An example workflow might look like this:

 

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In the initial nodes, KNIME makes it relatively easy to combine data and normalize it so that it can be consistently read throughout the application. This enables it to be used for various data mining and aggregation tasks. Much like in spreadsheet software, it will allow manipulation of and presentation of this data into a desired format. Once it is organized individual reports can be run.

Below is a screenshot of the main types of charts that can be generated:

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Dashboard

KNIME Analytics has no built-in business intelligence dashboard feature however this function can be built by using the rich toolset available. It is also possible to share dashboards and collaborate by licensing the KNIME server version, which has a licensing cost.

This said, one can create many dashboard-like features in the open source version, by generating views and reports off of the workbench. Each feature described below exists as a node, and creating a dashboard involves combining these into a workflow. If using KNIME server, a dashboard page can be shared. Alternately, one can create a workflow that includes many different nodes to generate different data representations.

Each workflow process can be triggered by scheduled and automated cron jobs for regular updates (as well as email alerts, with reports sent in chosen formats, such as pdf, powerpoint, etc)


Trend Indicators

The below shows an example of how a line plot can be created using features within KNIME. The image in the back demonstrates how to manipulate the data to eventually provide a graphic representation of data you desire:

 

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Interactive Reporting

KNIME has some very helpful interactive charts for the purpose of data reporting. These enable manipulation to see individual segments. Here is an example of a sunburst chart, with accompanied output data.

 

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The below image will provide a better idea of how this would look like on a Dashboard created using KNIME server:

 

 

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Geolocation

One particularly useful feature in KNIME, particularly from the point of view of a direct marketer, is geo-mapping. Below is an example created using the OpenStreetMap APIs.

 

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Predictive Tools

KNIME provides a collection of nodes which can assist with predictive analytics. These are easily output as charts and graphs. Below is an example of predicting airline arrival times; these can easily be created for direct mail purposes by including past data, and predicting (for example) response rates for various demographics on a mailing.

 

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Physical Export formats

From the reporting screen, KNIME allows exporting results into the

following formats:

  • DOCX
  • HTML
  • ODP
  • ODS
  • ODT
  • PDF
  • POSTSCRIPT
  • PPTX
  • XLS_SPUDSOFT
  • XLSX

Raw Data can be exported as a CSV file.
Images can exported either as PNG or SVG formats.

Summary: Key takeaway

KNIME is an extremely powerful tool, with a tremendous amount functionality and very useful reporting features. It is versatile and extensible. From the perspective of a direct marketer, if one is willing to take the time familiarizing oneself with this software, it can be very valuable, especially as an alternative to paid licenses for other competing software. While there are no built-in templates for direct marketing, there are many BI features. Many modules may need to be built, however modifying existing examples is very possible.

For skilled professionals, creating these should not take a considerable amount of trouble. Once familiar with the tool, new functions and reports should be able to be created relatively easily.

The hardest part may be understanding how “learning trees” and various other AI-type modules work.

If one is looking for a quick out-of-the box solution, intuitive for novice users, KNIME may not be the ideal tool for Direct Marketers. However, if one is willing to take the time or expense to set up and configure KNIME, the flexibility and power make it an excellent choice.

Integrations

  • Oracle
  • MSSQL
  • Hive (Apache)
  • Avro
  • Parquet
  • ORC
  • Azure

Wyzoo Star Ratings

Overall functionality useful to a direct marketer
1 /5

KNIME is robust and feature-rich. It covers many of the features that a direct marketer would need, and while it may require some modification to make every piece work, it is a highly flexible platform that will fit well in your MarTech stack. Many useful nodes are included, with the ability to construct new ones, depending on need. By its very nature, KNIME is designed to be flexible. The only limitations are in the areas of skill and imagination.

Intuitive User Experience
2 /5

For technical users: 3.5
For non-technical users: 2.0

As might be expected from a powerful platform, designed by many users, KNIME may take some time to understand, at least at first.

For this reviewer, it took about an hour to get started with some basics (there are some fairly clear examples, with some loose directions which make it easier), and much longer to get a sense of its capabilities. However for someone with more understanding about data processing it will likely take considerably less.

Adding new modules is easy. Once familiar with the interface, it's a matter of dragging and dropping and performing configuration. Understanding how individual nodes work will largely depend on the user’s familiarity. However, if one is used to using features within popular spreadsheet software, many of the most important ones from the point of view of a direct marketer, should be fairly intuitive.

One possible drawback lies in the lack of a pre-designed dashboard, however KNIME does come with many pre-built examples to assist understanding the process. While after a while, the system does become understandable, it may take a little bit of time to become familiar with its functionality and configuration.

Active Support Community
5 /5

KNIME has a very active support community. Examination of the official user forum shows a considerable number of questions, and most show nearly immediate attempts at answering. The user community appears to be very motivated to help users get used to using the software.


KNIME official forum:

forum.knime.com
Specifically:  forum.knime.com/c/knime-analytics-platform
Many daily, each one with multiple responses. Very active and involved support.

Issues not tracked on Github.
Github:  github.com/knime/knime-core
 

Commits:

15777

Contributors:

35

Releases:

100
Watch: 27

Star:

59

Fork:

36

Commits/Contributors:

451

 

Minimal Technical Skill Required
3 /5

Understanding some of the basics of data analysis is required. Once the workflows are set up, and with some time spent going over tutorials, many should be able to become reasonably proficient. However, out of the box, it is not a tool for a non-technical person.

If there is a quality technical staff to help set the process up, this should make the process considerably easier. While there are many features that resemble functions within Excel, these are not “beginner” functions, and this only can get more complicated with different types of datasets.

Setting it up is may take a little while for beginners. For a technical professional, the ease of use is fairly solid. For non-technical users, expect to spend some time with your IT staff explaining individual pieces. Some modules are fairly self-explanatory (YMMV), however for some of the more complex but valuable functions, configuration of individual modules can take some time. There are a lot of dependencies, and error messages are not 100% clear. However once set up, it should work fairly well for end-users.

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