This is the third in a series of blog posts on analytics and intelligent industrial applications. The previous posts dealt with the value case and the data challenges. This post deals with the organizational challenges.

People issues can trump technology challenges when it comes to big data and analytics projects of all types, and building intelligent Industrial Internet applications is no different. In a recent IDC and Computerworld business analytics survey, 33% of the IT respondents identified "lack of sufficiently skilled IT staff" as a top challenge for analytics initiatives. And in the same survey, 45% of line of business respondents identified "lack of sufficient number of staff with analytics skills" as a top business challenge for analytics initiatives.  

Conventional wisdom suggests that the lack of data scientists is at the heart of this issue, but this understates the people problem. Data science is a team sport requiring coordination and collaboration across multiple functions within the enterprise. Figure 1, based on the same IDC/Computerworld survey cited above, shows the diversity of skills needed for analytics projects.  

Organizational alignment for big data and analytics success in industrial environments

For industrial companies, the skills identified by the survey are typically found in three different departments:

  • IT: Data management and hardware infrastructure skills often fall under the domain of information technology (IT). The department is responsible for the running of systems of record in the enterprise data center, if not in the cloud. Assuring the reliability of the network for connecting the devices for continuous operations is even more critical in distributed, industrial environments (take remote operations in mining or oil and gas, for example). Analytics begins with the data, and the quality of IT decisions on data management, security, and retention determines an organization's agility to measure and predict performance. Roles required in Industrial Internet scenarios include system and network management, database operations, and integration.
  • OT: Skills in business analysis and decision making (from strategic to real-time) reside in the line of business (LOB), often operations technology (OT) in an industrial scenario. OT must prioritize analytics initiatives to focus on the decisions for which there is the greatest business impact. And each initiative requires an OT line of business champion to ensure organizational commitment. OT is dependent on IT for the availability of the data and on analytics expertise for bringing the appropriate techniques to the data to enable optimal decision making. In addition, connected machines are Internet-accessible and must be secured at the level of core IT infrastructure, requiring joint work by OT and IT personnel. Many OT roles require analytics support including planners, real-time operators, as well as line and executive management.
  • Analytics: Advanced analytic and BI tech development skills are core to analytics. Many organizations have analytics professionals (data scientists, business analysts) scattered throughout business and IT functions. But, IDC has found that organizations that are high achievers in analytics have tended to form dedicated analytics organizations, bringing together IT and LOB professionals to develop, maintain, and transmit best practices via training and outreach across the enterprise. This group (including individuals formerly in IT or LOB functions) becomes a shared service, working collaboratively both with IT and LOB/OT, staffed by individuals who have demonstrated expertise on prior successful projects. 

Regardless of the exact organization, successful analytics projects require sharing information, models, requirements, and expertise. IDC research shows the best results in analytics require the alignment and collaboration across these three functions.  

Recommendations on organizational dynamics

Organizations should set their teams up for success by recognizing the existence of the core stakeholders who impact big data and analytics projects across IT, OT, and analytics groups. Ignoring any one of these groups can have a negative impact on the project outcomes. To effect a positive change:

  • Team up: Establish joint working groups and promote ongoing interactions. Several organizations that we consider to be high achievers have established big data and analytics centers of excellence as well as internal data governance and analytics committees with representatives from OT and other LOBs, IT, and analytics groups.
  • Define roles: Recognize the core competencies of each group and establish ownership and responsibilities accordingly. Analytics groups should focus on free-format discovery and experimentation; LOBs with their business analysts should focus on analyzing performance by key dimensions. IT should focus on the provisioning of the technology infrastructure for managing and integrating the data needed for analysis. 
  • Collaborate proactively: Evaluate social software tools and applications for better communication and collaboration. Several high achievers that we have come across in our research use enterprise social media tools, video conferencing, as well as regularly scheduled meetings to facilitate collaboration.
  • Play nice: Self-service has become the mantra of those working with big data and analytics technology. But, many lines of business and analytics groups pride themselves on their ability to bypass IT to fulfill their own needs for data access and analysis. Such assertions of independence can lead to data anarchy and an elevated risk level. Improved collaboration among IT, lines of business, and analytics groups can help mitigate those risks. To achieve this goal, organizations need to assess the current level of collaboration across IT, OT, and analytics, establishing methods, incentives, and tools to foster collaboration.

Keeping these things in mind dramatically improves results, so that the focus shifts from personnel to product with industrial apps that speed up the business rather than hinder it.

Check out the Industrial Internet infographic from IDC on optimizing operations with big data and analytics.


About the author

GE Digital

Driving Digital Transformation

GE Digital connects streams of machine data to powerful analytics and people, providing industrial companies with valuable insights to manage assets and operations more efficiently. World-class talent and software capabilities help drive digital industrial transformation for big gains in productivity, availability and longevity. We do this by leveraging Predix, our cloud-based operating system, purpose built for the unique needs of industry.

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