Ventana Research Analyst Perspectives

While covering providers of business analytics software, it is also interesting for me to look at some that focus on the people, process and implementation aspects in big data and analytics. One such company is Nuevora, which uses a flexible platform to provide customized analytic solutions. I recently met the company’s founder, Phani Nagarjuna, when we appeared on a panel at the Predictive Analytics World conference in San Diego.

Nuevora focuses on big data and analytics from the perspective of the analytic life cycle; that is, it helps companies bring together data and process, visualize and model the data to reach specific business outcomes. Nuevora aims to package implementations of analytics for vertical industries by putting together data sources and analytical techniques, and designing the package to be consumed by a target user group. While the core of the analytic service may be the same within an industry category, each solution is customized to the particulars of the client and its view of the market. Using particular information sources and models depending on their industry, customers can take advantage of advances in big data and analytics including new data sources and technologies. For its part Nuevora does not have to reinvent the wheel for each engagement. It has established patterns of data processing and prebuilt predictive analytics apps that are based on best practices and designed to solve specific problems within industry segments.

The service is currently delivered via a managed service on Nuevora servers called the Big Data Analytics & Apps Platform (nBAAP), but the company’s roadmap calls for more of a software as a service (SaaS) delivery model. Currently nBAAP uses Hadoop for data processing, R for predictive analytics and Tableau for visualizations. This approach brings together best-of-breed point solutions to address specific business issues. As a managed service, it has flexibility in design, and the company can reuse existing SAS and SPSS code for predictive models and can integrate with different BI tools depending on the customer’s environment.

Complementing the nBAAP approach is the Big Data & Analytics Maturity (nBAM) Assessment Framework. This is an industry-based consulting framework that guides companies through their analytic planning process by looking at organizational goals and objectives, establishing a baseline of the current environment, and putting forward a plan that aligns with the analytical frameworks and industry-centric approaches in nBAAP.

From an operating perspective, Nagarjuna, a native of India, taps analytics talent from universities there and places strategic solution consultants in client-facing roles in the United States. The company focuses primarily on big data analytics in marketing, which makes sense since, according to our benchmark research on predictive analytics, revenue-generating functions such as forecasting (cited by 72% of organizations) and marketing (67%) are the two primary use cases for predictive analytics. Nuevora has mapped multiple business processes related to processes such as gaining a 360-degree view of the customer. For example, at a high-level, it divides marketing into areas such as retention, cross-sell and up-sell, profitability and customer lifetime value. These provide building blocks for the overall strategy of the organization, and each can be broken down into finer divisions, linkages and algorithms based on the industry. These building blocks also serve as the foundation for the deployment patterns of raw data and preselected data variables, metrics, models, visuals, model update guidelines and expected outcomes.

By providing preprocessing capabilities that automatically produce the analytic data set, then providing updated and optimized models, and finally enabling consumption of these models through the relevant user paradigm, Nuevora addresses some of the key challenges in analytics today. The first is data preparation, which our research shows takes from 40 to 60 percent of analysts’ time. The second is addressing outdated models. Our research on predictive analytics shows that companies that update their models often are much more satisfied with them than are those that do not. While the appropriate timing of model updates is relative to the business context and market changes, our research shows that about one month is optimal.

Midsize or larger companies looking to take advantage of big data and analytics matched with specific business outcomes, without having to hire data scientists and build a full solution internally, should consider Nuevora.

Regards,

Tony Cosentino

VP and Research Director