According to Forrester Research, 85% of enterprises are planning, implementing, or expanding the use of predictive analytics. There is a simple reason for that: predictive analytics results in more knowledge. There are plenty of ways to use analytics and ways to embed them in applications, but you aren’t able to do it efficiently unless you operationalize predictive analytics.
In a recent webinar, Mike Gualtierri, principal analyst at Forrester, and Lars Bauerle, Chief Product Officer of RapidMiner discussed the need for organizations to operationalize predictive analytics to maximize business outcomes. A few of the key points are summarized below:
Operationalize predictive analytics to make your applications smarter
Analytics is about information and insight that human decision makers can use, but it’s also about applications. We’re talking about how we can take those insights and those models and inject them into applications so we can make these applications smarter.
There are plenty of ways to use analytics and embed them in applications, but you can’t do that unless you can efficiently operationalize those analytics. The good news is that data scientists know how to create advanced analytics and these models. They use a combination of statistical and machine learning algorithms in conjunction with predictive analytics tools to find patterns and create models. Data scientists have an amazing tangible business impact. They are being judged not on the model, but on the business set of skills needed to be able to create these models which have the potential for great business value
Bearing the burden of deploying a model
A model that a data scientist creates often has to be translated to code that will run within the target application. So if there’s a beautiful model that needs to run an ERP system, can the ERP system accommodate the code of the model to do the scoring in real time? If it’s a web application, how is that model going to be run? PMML is a standard language that some people have used to deploy models, but that can often limit the methods and the algorithms used to find the most accurate model, right?
No data scientist is just going to hand over a model and say, “This model is good forever.” Once you deploy, a model it has to be monitored and retrained on a frequent basis.
Streamlining the process
The solution really is to streamline this entire data science process – everything from the process of discovering, to deploying, to monitoring and retraining that model. And so there are a few requirements that need to be met. Those models need to be able to scale to handle high-volume applications and streaming data. For example, an e-commerce application has to scale to tens, hundreds, even millions of users. However that model is deployed, it is required to be able to handle scale and score at high volume. It also has to be able to access all of the data that is needed at the time of scoring. It can be very difficult to extract those inputs from the underlying systems and the underlying applications where they originate in a flash.
RapidMiner solves this issue by doing everything from deploying the models, scheduling, and managing the models over time. We do this through a platform that can access many different data sources, provide a rich layer of data preparation tools and capabilities, streamline the ability to model and validate them to really figure out what your models can do. Our platform makes it easy for you to build predictive models, and then operationalize predictive models to deploy these models into business systems in a variety of ways.
Watch the recorded webinar to learn more about why you should operationalize predictive analytics.