Application and Dashboard Developers
Application and dashboard developers are often on the receiving end when it comes to machine learning solutions. They take results from data scientists and analysts and weave them into new or existing applications and dashboards for use across the enterprise or external customers.
One of the major challenges facing enterprises is the ability to deploy machine learning solutions in production. Even when data science projects are successful in solving business problems, enterprises may not realize the benefit because of the difficulty integrating with existing systems or meeting time-to-market requirements. Additionally, some enterprises, like those dealing with fraud, need to refresh and redeploy models very quickly, e.g., within hours of detecting a problem. As such, having a well-integrated software stack enables realizing intelligent solutions faster, as does the ability to leverage R and Python-based solutions easily in combination with enterprise software. Since most applications and dashboard tools interact with relational databases using SQL, the ability to invoke R and Python seamlessly using SQL greatly simplifies embedding machine learning into applications and dashboards.
Lastly, C-level executives recognize the importance of and need for world-class data management technology and support. Mission critical applications demand scalability and reliability, whether on-premises, private cloud, cloud at customer, or public could. Increasingly, automation of many standard database activities is not a nice-to-have feature, but a must-have capability. Today, the ability to apply security patches to 10s or 100s or database systems quickly can mean the difference between a minor nuisance and a viral data breach news story.
Executives also see the value in empowering knowledge workers across the enterprise with machine learning technology to enable better data-driven decisions. In effect, this democratization of machine learning across the enterprise helps a broader range of users to look at data not just as static content to query and summarize, but as a new corporate asset that can be used to understand customers better, determine the root causes of problems, predict demand, and recommend actions, just to name a few.
Also critical for executives is the ability to take these new insights from across the enterprise and deploy them faster to realize return on their data science investment—reflected in data, people, software, hardware, and Machine Learning in Mining.