Capturing Industrial Analytics ROI Remains Challenging
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Harnessing the value of industrial analytics ROI can catalyze downstream competitive advantage. However, capturing ROI remains elusive as organizations struggle to retool and recalibrate business models and culture.
Technical challenges and skills gaps continue to plague organizations. Overcoming these challenges involve nothing short of:
- Identifying viable, accessible data sources
- Eliminating gaps in infrastructure
- Identifying tools
- Building new applications which include open-source analytics tools; and finally
- Becoming architects of an IIoT tech workforce hiring strategy
The IIoT paradigm shift, or the Fourth Industrial Revolution , is re-engineering organizational structure and function. Building data-driven organizational cultures calls for cross-functional collaboration. Yet few organizations focus on crafting the type of human capital strategy that targets creation of a hybridized workforce.
Enabling Proof of Concept test beds for predictive and prescriptive analytics are starting to resemble the IIoT tail that is wagging the organizational dog.
How ready is your own organization to extract industrial analytics ROI to drive your trajectory?
Technical challenges impact the business case for industrial analytics ROI.
In my January 11 post, I reference and provide a link (free opt-in page) for the recently released Industrial Analytics Report 2016/2017. The report is a joint venture by IoT Analytics and the Digital Analytics Association Germany (DAAG). The document provides insights gleaned from 151 decision makers and data analysts polled about the state of industrial analytics readiness in their organizations.
Key technical challenges (Exhibit 26, p49) continue to impede development of industrial analytics Proof of Concept projects. Respondents perceive the following categories as either: 1) very challenging, 2) challenging, or 3) somewhat challenging. The aggregated results appear below:
- Interoperability between system components – 78%
- Data accuracy – 62%
- Gaining insights from the data – 59%
- Security – 56%
- Integration with enterprise systems – 50%
- Technical infrastructure and tools – 44%
- Data access – 42%
The following insight from the DAAG report captures the frustration of decision makers in consistently – and successfully – executing industrial analytics projects.
Today, we are observing a strong misbalance between the cost and the value structures of data analytics. The value gets unlocked in the analysis phase but the most time and resources are required in the data preparation phase prior to the actual analysis. Digital Leaders must therefore gain an understanding of how to automate, scale and accelerate data preparation in their organizations. Frank Poerschmann, Board Member at the Digital Analytics Association e.V., p 43
Are these the same factors which you wrestle with? How would you prioritize their importance to your organization?
Skills gaps continue to erode the value of industrial analytics ROI.
Upstream investment required to create an integrated, organizational-wide strategy for industrial analytics is large. First, data resources can be scattered across the enterprise, sometimes hoarded in data kingdoms. In addition, 60% of DAAG respondents observe overlapping skills with other departments.
As a result, respondents report 55% of industrial analytics projects are outsourced (p44) due to data analytics skills gaps. However, 56% experience difficulties collaborating with external partners (p48).
With these shortcomings ever-present, it is not surprising that 60% of respondents wrestle with successfully making the business case for launching industrial analytics projects (DAAG report, p48).
Consider your own organization. Does, the C-Suite wrestle with prioritizing internal infrastructure and employee investments to support a data analytics-driven culture?
If so, whose responsibility is it to champion the cause? Who is best-suited to locate and catalogue data resources and address skills gaps: the CIO, the COO or HR?
Making the case for industrial analytics ROI requires stronger CIO leadership.
One would expect to see the CIO as champion of organization-wide industrial data analytics utilization and decision making. Yet, in the DAAG study, the CEO (34%) or the COO/Head of Manufacturing (24%) leads the initiative. The CIO/CTO drives industrial data analytics projects in only 7% of organizations polled (p43).
With 55% of respondents reporting outsourcing of industrial analytics projects, why do the CIO and CTO remain out of the loop? How is that decision valuable to the enterprise?
Initially, it can make sense to involve external integrators and data scientists on industrial analytics projects. However, when each department champions their own projects, there can be multiple projects involving various integrators competing for the same resources.
Perhaps that scenario, if unaddressed, is why capturing industrial analytics ROI remains elusive.
Making the business case for industrial analytics ROI starts with an organizational plan for IT-OT convergence. However, is that collaborative structure and function suggested by the DAAG study findings?
Clearly, the dynamics of IT-OT convergence require the CIO and COO to work more collaboratively and cross-functionally. Their initial mandate? Creating governance to catalyze successful execution of industrial analytics projects.
Otherwise, capturing and harnessing industrial analytics ROI can be diluted, if not buried, within project champions’ departments. Whether intentional or not, that scenario reinforces legacy business models, mindset and infrastructure.
What is your own experience?
- Has your organization been working on industrial analytics projects?
- What was the role of the CIO / CTO?
- How have you organized projects?
- Have you involved external integrators?
- Were analytics results shared across the enterprise?