How is SAS improving data models with ModelOps?

data models

Data models refer to a mathematical equation that describes the relationship between variables in a historical data set to estimate or classify data values. The purpose behind the model is to draw the connection between seemingly disparate datasets to predict outcomes. These models are crucial for optimising analysis because they deliver clean, usable data to the end-user. It covers all stages of the process beginning with structuring databases for analysis. Therefore, if data models fail to deploy, then businesses cannot glean the immense value of data analytics. It’s for this reason that we are going to take a look at why data models fall through, and what SAS, in particular, is doing to address the topic.

The problem with deploying data analytics models

Did you know that over 50% of data models fail to deploy? Contrary to popular belief, data analytics is not foolproof and is prone to failure much like any other project. Data models have been known to go through several stages and stumble at the final one. These models go through the planning stage, the validation stage, the testing stage and the production stage, but fail at the deployment stage. There are several business and technical reasons why analytics models are stumbling at the last block.

Some models fail for business reasons. One of the problems we as technical-minded people have is that we get so caught up in the development of complex data analytics models that we forget to ask ourselves the question: How is this relevant to the business? This leads to failures on multiple fronts. Do we understand the business problem at hand and are we taking steps to solve it? Are the models actually relevant to the day-to-day running of the business? We incorporate complex algorithms into the model without stopping to think whether these models would even work for the business. The failure to truly understand business needs is one cause for the failure of the data model.

Data models can fail due to technical reasons. Some data analytics models are just too unstable for use, requiring significant revision or a stop to the project. Other times, models dependent on dynamic variables present problems because they bring in values that are not accounted for in a scoring window, spoiling the accuracy of the algorithm. Then, there is the issue of false positives. As any data analyst knows, a false-positive variable is an assumption that a variable is a net positive for the business when in reality, they are not. False-positive variables are dangerous because they can distort readings, which prompts an incorrect business solution. Techincal issues like this can undermine the effectiveness of the data analytics model, leading to failure in deployment.

What is ModelOps?

ModelOps is the approach used to bring data models rapidly through the different stages of the analytics life cycle. The aim is to get the models through the development stage to deployment, as quickly as possible, without compromising quality. ModelOps is not just about tech, it embraces concepts related to model development. It covers culture, work processes and technology, to make the process as smooth and efficient, as possible. One of the things ModelOps does is a foster dynamic collaboration between IT and analytics teams. Hence, you can say that ModelOps is similar to DevOps except the focus is on developing models as efficiently as possible.

How does ModelOps solve the problem?

With over 50% of data models failing, ModelOps can improve the chances of models deploying for public use to expand lifespans. Since ModelOps changes so many aspects related to model development and deployment. It brings several positive changes to the development process, like cutting development and deployment from months to a matter of hours, significantly improving deployment speed. The reason behind the speed is better collaboration between IT professionals and data scientists.

ModelOps also brings better data security to the equation and a resultant stronger security infrastructure helps with model deployment, given that data scientists and IT professionals can work together, to create models with different deployment scenarios factored in. Hence, developers simplify data processing with trackback information and data lineage, making government and audit compliance much easier to accomplish. Data scientists access data from a trusted source aligned with privacy and security standards so analysts don’t have to go back and rework the model to meet every scenario.

Is SAS incorporating ModelOps into data analytics models?

SAS is bringing ModelOps to their list of offerings, in the form of SAS Model Manager. The software is designed to help companies optimise the deployment of data models and overcome the hurdles in AI and machine learning. With a little help from the SAS Model Manager, organisations can ensure that their model is healthy for years to come because data models also need maintenance, like your very own car. The support service package is designed to support organisations using both SAS and open source analytics. Support services are also necessary for running data models and carrying out data analytics services with efficiency.