The Analytical Center-of-Excellence in a World of Connected Data

From traditional back-office transactional data to unstructured information from connected products in today’s Internet of Things, the modern data infrastructure is changing old analytical paradigms. Once primarily owned by IT professionals or business analysts, this enriched pool of connected data is driving greater analytical activity in a variety of different job roles and user types. The most effective data-driven organizations today are able to combine approachable analytical technologies with relevant and timely information in order to generate a new breed of insight.

However, this analytical nirvana is not achieved in a vacuum. While users are generally becoming more savvy and adept at handling data, the richness and diversity of information available has only grown. Aberdeen’s recent report, Data-Driven Manufacturing in the Age of Insight, demonstrated that those in the manufacturing sector in particular are actively using several different types of data from the traditional to the emerging. See chart below:

A Diversity of Data in Use

Figure 1

In an effort to exploit the inherent value of this data rather than drowning in its rising tide, more companies today are considering a centralized way of managing and understanding data and its best usage – an internal organization often referred to as a Business Intelligence Competency Center (BICC) or an analytical Center-of-Excellence (CoE).

Similar concepts are explored in a recent Harvard Business Review article which did an in-depth investigation into connected devices and their impact on the operations and organizational structure of today’s companies. The article makes reference to the concept of a Unified Data Organization, which shares several of the ideals and overarching goals of an analytical CoE.

In parallel to this discussion and analysis, Aberdeen’s research examined the activities and commonalities of 33 organizations reportedly using an analytical CoE, closely tied to the Unified Data Organization discussed in the HBR article. Among other things, these organizations seemed to share four key characteristics:

  • Cross-functional representation. Rarely does a critical decision rest upon data or knowledge from one specific area of the business. An effective CoE includes active representation from core areas of the business such as IT, marketing, sales, product development, or service.
  • Data sharing and collaboration. In order to make better cross-functional decisions, users need access to a broader and richer base of information to support those decisions. Companies with an analytical CoE in place are seventy-four percent more likely to break down unnecessary barriers to information exchange and share data across functions.
  • Controls and oversight of data usage. As a vital counterpart to the sharing of information, companies need to maintain tight controls and oversight of who can access data and how it is used, lest a variety of data quality and security issues arise. Companies using a CoE are eighty-eight percent more likely to have policies and/or tools in place for better data governance.
  • Ongoing monitoring of analytical activity. Nothing makes an IT professional (or CFO) cringe more than the concept of underutilized technology (i.e. “shelfware”).  Engaging more users requires more job-role relevant data to be available and better tailored analytical capabilities. Those using an analytical CoE are more than twice as likely to track the adoption and utilization of their analytical tools.

With these puzzle pieces in place and a strong analytical CoE taking shape, companies are in an advantageous position to spread analytical activity and better data-driven decisions across the organization. In fact, Aberdeen’s research demonstrates that this enhanced analytical pervasiveness is a key benefit for organizations using a CoE. See chart below:

Spreading Analytics Across a Connected Organization

Figure 2

The HBR article makes a strong case for the value and importance of the data generated by connected products and how top companies are transforming strategy to adapt and perform in this new environment of data. The article then extends this point in talking about the value to an organization of combining this product-generated IoT data with other more traditional sources, “This new product data is valuable by itself, yet its value increases exponentially when it is integrated with other data…”

Aberdeen’s research also makes the case, not just for connected data, but for connected people and enhanced analytical activity as critical components to this environment. If a product manager is mapping out the features, functionality, and long-term road map for a particular product, she might need supply chain data to factor sourcing viability into the decision. She might need customer data to tailor the product specifications to a particular demographic or customer type. She might need financial data to perform a proper cost accounting analysis and forecast profit margins. She might also need machine-generated sensor data to take into account the performance and durability of existing products into her feature updates. Decisions like these require not only connected data from the Internet of Things, but shared knowledge, a collective data-driven curiosity, and elevated analytical activity across multiple areas of the business.

Leaning on the ideals of an analytical CoE, companies can increase data fluidity, broaden analytical activity and raise the organizational bar for decision efficiency. Time and again, Aberdeen’s research demonstrates that these qualities lead to the identification of more salient business opportunities and, ultimately, enhanced business performance.

This is the first installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

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