RiskTech Forum

Teradata: Is analytics operations the key to successful data science?

Posted: 5 September 2017  |  Author: Christopher Hillman  |  Source: Teredata

Despite a continuing shortage of data science skills, teams do exist in businesses across many industries. Expectations are high and the promises of predictive analytics, prescriptive analytics and artificial intelligence (AI) has captured the imagination of many. Now that the role, the skill-sets and the responsibilities of data science are becoming better defined, how do we take the next step and show a true return on investment (ROI) for data science projects? How can we make positive, measurable improvements to revenue, profit and customer satisfaction using data science?

The answer could be in the automation of the production process. To this end, analytics ops is an emerging field within data science. Taking the best practices from software engineering and adapting them to the process of pushing predictive models into production in an automated way, analytics ops can free up valuable data science resources for the work they are best at: discovering new patterns in data, improving existing data models and building newer, more accurate ones.

In order to build and maintain hundreds – or even thousands – of predictive analytical models, the operationalisation of analytical models requires a repeatable process on an industrial scale. In addition, there is a requirement for a reliable architecture and robust pipelines to deploy predictive analytical models in production systems.

The critical ingredients you need within your organisation are:

Analytics ops – where to begin?

Creating the correct process and selecting the best software tools to enable automation is the key to the analytics ops process. Many of the following are already standard practice in a software engineering environment, but these standards and protocols need to be applied to the production and development of the data models that businesses increasingly come to rely on.

Is analytics ops the future?

Businesses can achieve striking productivity gains using an analytics ops approach to data science work. Using an analytics ops approach, you can say goodbye to the inefficient days of lone developers creating scripts on their laptops, maintaining production code manually with a method based on continuous integration, continuous development.

Integration and automation can bring incredible benefits to your business. Think Big Analytics has helped elevate many customers on their analytics journey, and can help with the process engineering and tool selection to make analytics ops a reality. Get in touch to see what we can do for you. Find out more here.