Analytics

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Data

Data today is fast, big and complex. Analytics solutions have to analyze data of any type, including traditional structured data and emerging formats, such as streaming sensor data, images and video.

To access, prep, clean and govern that data, you also need a data management strategy.

How will you collect, clean and store your data? Data preparation is estimated to take up to 80% of the time spent on an analytics project. Could that time be better spent building models?

An intelligent analytics platform streamlines data preparation with native access engines, integrated data quality and data preparation tools that automate time-consuming tasks with AI.

Finally, data governance ensures your data can be trusted because you know the source and content and can monitor data quality. Data governance also makes it easy to protect data when appropriate.

Discovery

Discovery is all about exploration, visualization and model building. Finding the right algorithm is often a process of trial and error. But when it’s easy to document, save and compare those trials, the process works the best it can.

Choosing the right algorithm depends on several factors, including data size, business needs, training time, parameters, data points and much more. Even the most experienced data scientists can’t tell you which algorithm will perform the best before experimenting with multiple approaches.

In fact, it’s common in the discovery phase to compare different models written in a different programming language with different data features.

For example, a recent analytics project that used object detection to identify tumors in medical scans of livers began with an exploration of several neural networks and a few weeks of comparing and documenting the results of different models.

This collaborative process works best when data scientists with different skill sets can write code in the language of their choice, and nonprogrammers can use a visual point-and-click interface to explore the results of different analytics approaches.

Deployment

If you want your analytics efforts to pay off, you need to deploy the results of your discoveries and put them to use. Machine learning and other models are not meant to sit on the shelf – you must use them to get the business value. Yet the deployment phase is where most organizations struggle the most.

Whether you’re building a single model or thousands, moving from selecting models to deploying models requires model management. Model management provides version control and helps you register, validate and centrally manage your models. It helps you develop procedures and rules for model deployment and monitoring. And you also get transparency about data and model use.

Your goal should be to build models once and deploy them anywhere – to executive dashboards, right into operational systems or built into other apps through APIs.