McKinsey Report Summary: Seven Characteristics of a Data-Driven Company

Read highlights of a report by McKinsey Digital outlining the seven characteristics of the DataDriven enterprises in the future and the key enablers behind them. 

McKinsey Digital published a report earlier this year outlining the characteristics of the Data Driven enterprises in the future—2025, to be exact—a short three years from now. In “The data-driven enterprise of 2025”, McKinsey describes a data-driven company as one that has “rapidly accelerating technology advances, a recognized value of data, and increasing data literacy.” 

While their prediction that “by 2025…most employees will use data to optimize nearly every aspect of their work” seems a bit pie-in-the-sky, the point they’re driving at is valid: those companies that can transition to being data-enabled the fastest stand to leverage data to their competitive advantage as well as boosting their income by lowering costs through better use of that data. 

The report outlines seven characteristics of data-driven companies and the key enablers behind them. This blog discusses some of the highlights of that report and the impacts they can have on businesses: 

Data is embedded in every decision, interaction, and process. Most organizations today apply data-driven principles (such as AI and automation) inconsistently, which offers some benefits but also leaves some rich data unused. By 2025, data-driven organizations will apply data to day-to-day activities and decision-making. For example, stores can identify loyalty-program customers and direct them to their preferred items.  

Data is processed and delivered in real time. Today’s companies are limited by how much data they can collect and analyze in real time due to legacy technology architectures and the computational demands of real-time data processing. In the future, companies will have access to new technologies and architectures, such as more powerful edge-computing devices and high-bandwidth networks, that can perform real-time analysis and yield faster, more effective insights. In this way, maintenance staff can leverage IoT sensors to identify equipment service needs in real time. 

Flexible data stores enable integrated, ready-to-use data. Currently, most data must first be structured in order to be analyzed using relational database tools, requiring time and technical expertise. Future data-driven data analysis will use various database architectures that offer flexibility in how data is stored, retrieved, and used. Using AI and ML, meaningful relationships can be identified between disparate data types faster. One example is using unstructured data from various sensors on buildings to improve building design and construction.  

Data operating model treats data like a product. By viewing data as a “product” within the organization, data from multiple sources can be organized and delivered in a form that can be used and reused to meet various business needs. 

The chief data officer’s role is expanded to generate value. Here, the CDO and team go from being a cost center to a profit center by monetizing their data services. For instance, bank CDOs can use data on fraud monitoring for use by government organizations and their partners. 

Data-ecosystem memberships are the norm. In today’s environment, data in most organizations is often siloed to specific departments. Certainly, data is rarely shared between industry competitors. However, future data-driven companies will benefit from exchanging their data with others in the same or related industry to produce a more comprehensive data resource. One example is manufacturers adopting a standard data model to share information that empowers their peers and partners to understand their supply chains better. 

Data management is prioritized and automated for privacy, security, and resiliency. Rather than view data security as a compliance problem, future-thinking organizations will treat data privacy, security, and ethics as a required competency. Retailers, for instance, would inform online customers about the data they wish to collect and request customer consent before collecting data for personalized services.  

The report also gives additional examples and key enablers for each characteristic, as well as how companies can get started to become truly data driven. 

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By |2022-10-24T13:53:28-05:00October 24th, 2022|Business Intelligence (BI), Machine Learning/AI|0 Comments


Director of Manufacturing and Field Service Marketing, HSO

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