We are now in an era where consumer preferences are changing at lightning speed, social media is making the reviews transparent, and new competitors are appearing out of nowhere. Digitalization is starting to rule the world and employee attrition has increased. To surf these new waves of business challenges, organizations are getting driven by technologies and deploying a variety of new enterprise applications like ERP Systems, CRM (customer relationship management) tools, Big Data, and Cloud-based solutions. Also, organizations are relying on data science technologies and algorithms to be intelligent and proactive.

Data science, which is a relatively new term in the Business and IT industry, has hit the mainstream for investment in the last few years. This is because it provides organizations the opportunity to gain insights about their business and drive proactive planning. Although analyzing data for information or insights has been there since a decade ago, proactive planning is relatively new. Organizations are now able to predict what might happen in the future, which helps them to formulate strategies and plan in advance for possible roadblocks. Statistical models and quantitative analysis sit at the core of data science and measure past performance to predict the future.

But, this is not the end of the story. To keep up with everyone’s expectations, organizations need to attend to the customers as soon as they need service, retain employees before they resign, or identify frauds before they happen. They also have to be operationally efficient and at the same time cost-effective. This requires the ability to apply analytical insights to business operations at the right time. The only way to stay ahead of everyone is to be agile at data-driven, decision-making capabilities. This is possible when algorithms are brought near to the origin of data such as databases and data science routines are automated and integrated to business process workflows.

Data science involves a wide array of technologies and statistical algorithms. This makes it difficult to automate each and every aspect of it. However, there are some areas in data science that can be automated using scripts and workflows.

Oracle Advanced Analytics: Bringing Automation to Data Science

Oracle Advanced Analytics, an analytics product from Oracle, helps to address problem-specific automation and repetitive-tasks automation. It has features like automated data preparation (ADP) and workflow that integrate data science modules to business operations databases. It also has a feature known as predictive queries, one of the forms of automated statistics, for professionals with no statistics knowledge to be able to perform predictive analytics.

Oracle Advanced Analytics comes up with a powerful combination of Oracle Data Miner and Oracle R Enterprise. Oracle Data Miner is a flagship product from Oracle that provides in-database execution of statistical algorithms to make predictions and discover insights. However, it is packaged with a limited number of algorithms. If we want to execute new advanced algorithms in Oracle Data Miner, it requires coding those algorithms in PLSQL, and it is time-consuming. This reduces the flexibility of Oracle Data Miner to implement advanced algorithms quickly.

To overcome this challenge, Oracle engineered open source R to Oracle R Enterprise. This engineered feature complemented the limitations of Oracle Data Miner and also made open source R scalable and enterprise ready for Big Data. This also helped Oracle Advanced Analytics to tap into the resources of the huge contributions of the latest libraries by the open source R community. 

You can learn more about Oracle Advanced Analytics from Oracle's Advanced Analytics page. Of course, you can also check out my book Data Science with Oracle Data Miner and Oracle R Enterprise. It aims to help you learn about Oracle Data Miner and Oracle R Enterprise which are the two components of Oracle Advanced Analytics. The book focuses on database embedded automation workflows for business use cases and provides an overview of the most commonly used data science techniques for business applications. You’ll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to your business systems. The goal of the book is to give you a head start to drive automation and implement data science using Oracle Advanced Analytics. It also contains solutions to some practical data science problems, so that you can learn by example. Please check it out and let me know what you think!