Creating a collaborative analytics culture across the enterprise

Data is driving a business revolution as new analytics-based innovations arise across industries. Companies are moving beyond traditional analytics as use cases for machine learning, robotics, and automation become real. But companies must ensure their internal teams are receptive to those technologies to ensure companies achieve their overarching analytics goals.

I recently shared that the global data analytics market is on track to reach almost $78 billion in 2023. But this growth obfuscates a real problem. Nearly half of analytics teams continue to spend more time accessing and preparing data than doing actual analysis.

“The scale, scope, and complexity of the data and the businesses that use it have bypassed the ability of humans to process it without intelligent automation,” Bain & Company reports. “Advanced analytics is simply not an option—it is an imperative for any large organization.”

Business leaders’ excitement over new data technologies cannot get ahead of employees’ data literacy and proclivity for new data technology. Advanced or ‘augmented’ analytics require company-wide strategies that prioritize usability and practicality over data complexity alone. That’s why successful platforms accommodate a variety of users and their objectives, rather than just the enterprise.

In fact, transforming your organization’s culture is as critical to business success as leading analytics platforms themselves. Cultural transformation “can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes,” McKinsey reports. In their 2018 analysis, McKinsey analysts were “struck” by “the competitive advantage unleashed by a culture that brings data talent, tools, and decision-making together.”

But if you’re like most companies, you struggle with siloed data that prevents collaboration, impractical data tools, and even data technology avoidance among your employees. This makes scaling analytics operations impossible, robbing you of the business value advanced analytics provide. In this article, I’ll explain how organizations can transform their culture to ensure collaboration and analytics success across the enterprise.

The truth about modern data culture

“The best advice I have for senior leaders trying to develop and implement a data culture is to stay very true to the business problem... Solving business problems must be a part of your data strategy.” - Rob Casper, Chief Data Officer, JPMorgan Chase

In their report, Bain & Company shared findings from their survey of over 300 executives. They found that while over two-thirds invest heavily in Big Data—and are optimistic about their investments—30% “lack a clear strategy for embedding data and analytics in their companies.”

This represents an oversight made common by familiarity with traditional analytics. In traditional models, dashboards and visualizations were designed to help executives and decision makers. As organizations democratize data access to a variety of user types, these models must evolve to support a greater variety of internal objectives.

As I described in my article for Dataconomy, it’s common to see the functional requirements for analytics platforms weighted disproportionately toward users at the opposite ends of the spectrum—while the core user base is left with tools that aren’t suited for their own responsibilities.

In these cases, organizations fail the “last mile” analytics objective of empowering their technology’s end users. Yet, it’s in those moments alone when users might finally understand the data and put it to use—the moments when business decisions of all types are truly made. Sadly, failure at this stage negates the value modern analytics platforms add for companies.

New analytics initiatives need more than leading technologies—especially when those technologies incorporate machine learning, robotics, and automation intended to empower users. They require strategic direction, and an organizational roadmap for adoption and success. As Bain & Company recommends, “Put business science before data science. A company’s advanced analytics goals should reflect the company’s broader aims, allowing it to amplify its most profitable products, services, and processes.”

A culture of analytics that thrives on collaboration

A successful analytics culture is a collaborative space, where users embrace an adaptive and universal environment that accommodates all skill sets. At Pyramid, we’ve identified some key characteristics that exemplify a successful advanced analytics implementation:

  • Practical access to data is democratized.  Data should flow into all areas of the organization and have the relevance and impact to get employees excited at their individual touchpoints. Ibrahim Gokcen, Chief Digital Officer of A.P. Moller – Maersk, describes their success in this regard: “We see a lot of oxygen in the organization, a lot of excitement about what is possible... Our people now have the ability to act on their innovative ideas and create value.”
  • Analytics support a culture of decision-making. Successful analytics means users across the enterprise are leveraging data insights to take action on clearly established KPIs. Business leaders should identify instances where team members at all levels of the organization already make decisions and prioritize analytics that empower them in those moments. “The best analytics solutions emerge when data scientists and business stakeholders work together,” Bain & Company describes. “Set success requirements early and keep end users central to decisions.”
  • Governance focuses on conforming access to individual users’ needs. Users needn’t access all of a platform’s capabilities to optimize their own decision-making. Users on the periphery needn’t concern themselves with building data models and dashboards, for example—a responsibility that falls to data scientists. Leaders who adopt an ‘empowerment’ over a ‘restrictive’ mindset realize prioritizing useful features and excluding non-useful ones for individuals accomplishes their objectives for control.
  • Platforms align employee excitement with business results. Successful organizations adopt platforms that support collaboration across departments with shared tools—which are accessible based on what each user experience requires, but aligned with overarching company initiatives. This increases the likelihood of adoption and collaboration, which are essential to supporting long-term analytics culture at scale.

"You have to figure out how to really democratize the data-analytics capability, which means you have to have a platform through which people can easily access data. That helps people to believe in it and to deliver solutions that don’t require an expensive data scientist.” - Ted Colbert, CIO, Boeing

Ultimately, every employee needs to embrace an analytics culture. Leading analytics platforms help organizations begin this process by ensuring each user extracts real value from those platforms. With the right solutions, companies needn’t hire highly technical personnel to ensure both employee and organizational needs are met.

Conclusion

Company culture is binary—it’s either an enabler or a detractor of business value. Start with an analytics platform whose core features are central to your long-term business objectives. Ensure the platform can support a broad set of user experiences from the start, and that its user touchpoints align with users’ needs at critical moments in decision-making processes.

Most analytics platforms don’t support this off the shelf. At Pyramid, we developed a model that is both adaptable and configurable. Developers and admins central to analytics functionality can create unique user-based environments, ensuring the right type of functionality reaches the right user types based on their individual needs.

Here, we’ve developed a technology that embraces the usability of traditional analytics—once particular to company elites—with the philosophy of advanced analytics, where users interact with sophisticated capabilities on their own terms. It’s in this way that knowledge workers will engage the critical machine learning, robotics, and automation capabilities that are necessary to power the analytics of tomorrow.