While companies continue to realize the importance of data science and its ability to positively impact revenue, scaling it across an organization continues to be a challenge. A new survey released today reveals a new leading factor to success — creating a positive, model-driven business culture among employees. This insight is one of the findings from a survey of data and analytics professionals sponsored by Domino Data Lab, provider of the leading open enterprise data science management platform trusted by over 20% of the Fortune 100.

Conducted by DataIQ, the leading membership-based forum for connecting, educating and supporting the data and analytics community, the survey curated a research panel of influential data and analytics professionals across a wide range of industry sectors and company sizes in the UK. Seniority ranged from senior managers and heads of department to global directors and chief officers.

The survey found that one in four businesses¹ expect data science to impact topline revenue by more than 11 percent. However, the survey indicates a challenge with company culture, suggesting a positive, model-driven culture is difficult to build and still needs to be developed. 39 percent want a clearer definition of needs from stakeholders, 38 percent recognize the need to train business users in data science concepts, and 32 percent identify the need for a more positive relationship with stakeholders.

“Many companies begin their data science journey by hiring a few data scientists, but overlook the importance of building a model-driven culture that aligns with business users and their needs,” said Nick Elprin, CEO of Domino Data Lab. “This survey highlights the impact that the lack of positive culture can have on identifying proper use cases, setting appropriate expectations, and ultimately delivering a measurable impact to the business. Understanding these challenges is important for companies at all stages of maturity so they can course correct and successfully scale data science operations across their organizations.”

Additionally, 40 percent of respondents indicate that weak understanding or support for data science in business is one of their biggest challenges. One out of three organizations (34%) indicate that conflict between data science and IT is one of their biggest challenges. Even companies that describe themselves at the “advanced” and “reaching maturity” levels in terms of their adoption of data science and analytics are not free of culture conflict. For both of these groups, half (52 percent and 50 percent of both groups respectively) indicate that conflict between data science and IT is their biggest challenge.

Some other findings from the survey include:

• More than half of all organizations (57 percent) expect a revenue uplift of under five percent, showing that the failure to embrace data science contributes to low expectations.
• One out of five businesses (21 percent) are gaining a major competitive advantage through the use of data and analytics tools across their enterprise.
• Sixty-seven percent have grouped their data scientists together as a central function or department (e.g., a Center of Excellence), rather than federating them across the business.
• One out of three organizations (32 percent) need months to get models into production. This latency must be addressed, because market conditions can change quickly and models trained using outdated data will make suboptimal recommendations.
• One in 10 organizations (10 percent) have adopted a superior automated form of model monitoring that provides proactive alerts when models are starting to decay. Data scientists can then address potential model issues before they impact business results.

“For data science to deliver real value to the organization, a positive culture needs to be created in which business stakeholders and data science practitioners have a close bond and common goals,” said David Reed, Knowledge and Strategy Director at DataIQ. “As the survey results show, that’s easier said than done. Four in ten organizations identify a weak understanding or support for data science by the business as their biggest challenge, which creates a vicious circle that leads to one in eight failing to create compelling use cases.”

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Microservices architectures (MSA) break down domain-level problems into independent modular capacities so they become easier to manage and deploy, which is great for many situations. When that system is organized around events — an event-driven microservices architecture (EDM) — you are streamlining microservices into event-defined clusters, which then function faster and with improved productivity than they did in a monolithic formation. This event-driven microservices architecture offers benefits that you won’t find in other MSA patterns.

There are challenges, too, however. Scaling an MSA, for example, is more complex than scaling a monolith because the microservice design has so many more moving parts. While you may want to simply duplicate your entire function in a new setting, you may also only need to scale elements of it. Both cases require a careful analysis of how the scaled elements will impact the existing system so you can design around their particular concerns.

 Why Event-Driven Integration Architecture (EDA)?

“Event emitters,” “consumers” and “channels” deliver the event-driven integration functionality that can’t be found in traditional point-to-point software architecture. Their flexibility and scalability improve performance within high-performance computing (HPC) and high throughput computing (HTC) clusters and are especially useful in cases where:

• Multiple subsystems must process the same events.

• Real-time processing is required with minimum time lag.

• Complex event processing is needed, such as those projects that involve aggregation over time-windows or pattern matching.

• There is a need for swift processing of high volumes and velocities of incoming data.

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