Why Innovation Teams Need a Data Sandbox

It’s impossible to be on an innovation team today without getting hit with data science buzzwords daily. Leadership wants to know how to incorporate AI, Big Data, algorithms, and machine learning into their next board update. Innovation teams need to understand, embrace, and apply the concepts quickly to explore new business solutions and products that will differentiate their company now and into the future.

To succeed in innovation, especially data-driven innovation, teams need the ability to explore data in an environment that doesn’t impact daily operations or tie them to existing business infrastructure. Innovation teams are expected to analyze multiple sources of data together in an environment that allows priorities and targets to change quickly. By using an existing data source export or creating connectors to backups of production systems, data engineers don’t need to prioritize the support of fast-paced requests from the innovation team over BI changes and issues. This provides the innovation team the flexibility of fast data discovery across multiple systems, all while not impacting production or waiting for significant data engineering work.

A Data Sandbox brings the ability to explore data, prototype algorithms and data solutions that customers and internal operations are requesting. Products require smarter features which means innovation teams need the capability to integrate intelligent solutions. A Data Sandbox can help you produce solutions as rudimentary as a report of a demographic analysis that informs product development or as robust as a prototype of an algorithm that predicts what customers will leave you in the next 30 days.

The key to innovation is exploration. Give your innovation team a way to explore data.

 

Data Exploration

Data Exploration is an important part of innovation. Making the treasure trove of data available for analysis will quickly unearth both problems and opportunities. Reviewing the data’s story with fresh eyes and sharing it back with the organization will spark great ideas and confirm areas of improvement. Both can be tied directly to cost savings or ROI. By having a deeper understanding of problem statements, business areas can actually make data-driven decisions instead of just giving the term lip service.

Innovation teams have the unique position of breaking down data silos between business areas of the organization. Where a business unit may not be able to justify the expense of a data science proof of concept for their area alone, an innovation team can use one or more departments as an example of building a company-wide competency in data science.

Most importantly, does your innovation team have a question that needs to be answered? Will this answer immediately provide value to the business (cost savings, revenue, process improvement savings, etc.)? Equally important, is there data available to answer that question?

If the business can provide one or more data sets they trust for analysis, it’s time to get started with a Data Sandbox.

 

Data Sandbox

There are 4 critical features you need to have in a Data Sandbox:

  1. Visual Tools to Clean, Enrich, and Join Data: Not every innovation team is going to have access to a data engineer. What you likely have are top-notch analytical thinkers that are exceptional in Excel or even SQL. Your Data Sandbox should allow these analytical thinkers (we call them Citizen Data Scientists) to join data, enrich data through formulas, and clean data all through a visual interface.
  2. Easy Dashboarding for Collaboration: It often falls on innovation teams to educate the business and leadership along the way. Dashboarding brings data to life through frequent demos and constant feedback.
  3. Auto Machine Learning (Auto-ML): Not every innovation team is going to have access to a data scientist. But that doesn’t stop you from getting requests for predictive solutions. As the innovation team, in many cases you are looking to prototype or prove these predictive solutions. With a feature like Auto-ML, you can visually build algorithms for quick prototyping and proving out viability.
  4. Path to Production: While the innovation team may be focused on prototyping, there shouldn’t be a huge leap to production. Using a tool that has production capabilities will minimize time and cost of implementation, which means business value (ROI, cost savings, etc.) right away.

Is your innovation team getting asked for more intelligent solutions? Are you interested in a Data Sandbox for your innovation team? We can help. Let's chat.