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There are many solutions that claim to democratize analytics, but they are really constrained
By: Progress Blog
Sep. 16, 2017 02:00 AM
True Democratization of Analytics with Meta-Learning
There are many solutions that claim to democratize analytics, but they are really constrained. A meta-learning approach democratizes without limits.
The democratization of analytics has become a popular term, and a quick Google search will generate results that explore the necessity of empowering more people with analytics and the rise of citizen data scientists. The ability to easily make better use of your (constantly growing) pool of data is a critical driver of business success, but many of the existing solutions that claim to democratize analytics only do so within severe limits. If you have a complex business scenario and are looking to get revolutionary insights using them, it’s easy to come away disappointed.
However, the democratization of analytics isn’t just a buzzword that refers to a narrow approach. It’s possible to do so much more. Let’s quickly review the current state of the market that you’re likely familiar with, and then dive into our proposed solution.
Lightweight Solutions that Oversimplify
Narrowly Defined Analytics as a Service
Cloud Environments that are only a Framework
But What about More Sophisticated Business Scenarios?
Because the Machine Learning or Data Scientist lifecycle is complex. A successful implementation includes a business requirements phase, data preparation, data modeling, and production deployment work. The last three phases are particularly resource intensive.
It’s pretty clear that this is a completely different challenge that the options described above can’t address. While there are cloud options that will manage the infrastructure, and there are tools that make the data scientist more efficient, there is a dearth of solutions that tackle the democratization of complex Machine Learning.
The need for democratization is driven by the amount of time and resources it takes to do this manually—even with a team of data scientists. And for those that don’t have data scientists, this is a non-starter given traditional tools and solutions.
Enter Machine Learning and Meta-Learning
Progress DataRPM is designed specifically to meet this need. By developing an innovative machine automated approach, we are able to automate a range of complex tasks that the other solutions above simply can’t.
This solution allows your team to focus the most strategic and actionable part of the process, which is analyzing and assessing the results. Whether you currently employ data scientists or not, it reduces the amount of time you need to allocate to evaluating and creating complex models.
Rather than constrain analytics and generate a simple or limited result, the meta-learning approach looks fully at the unique problems facing your business, is flexible enough to be adapted to new problems as they arise and is constantly improving. By automating some of the most arduous components of data analysis, you’re free to focus on delivering the insights and outcomes you need—quickly. It's all part of our cognitive-first vision for business applications. You can learn more about our platform for cognitive predictive maintenance here.
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