Industrial Analytics

The Industrial Analytics Market Research Study delivers current market analysis plus a five year market and technology forecast. 

Organic Growth and Demand for Sustainability Boosts the Industrial Analytics Market

New ARC Advisory Group research reveals the massive impact of the COVID-19 pandemic on the adoption of analytics and supporting technologies, such as the cloud, for industrial operations and infrastructure. The market outlook remains positive for industrial analytics. Building upon growing adoption prior to 2020, the pandemic accelerated the use of analytics, particularly to support remote monitoring and in-plant worker health and safety. Sustainability is expected to boost the use of analytics for related use cases, such as energy management, emissions reduction, and waste. 

Industrial Analytics Strategic Issues

In addition to providing a five-year market forecast, the Industrial Analytics market research provides detailed quantitative current market data and addresses key strategic issues as follows.

Initially, It’s Not Really About the Data or the Technology

For years, a non-stop cascade of thinking has put forward the idea that digital transformation is all about unlocking value from data. While this is true, in part, it obfuscates what transformation really is about: reinventing how you serve the market with an emphasis on faster and more accurate speed of response in the face of disruption, all while minimizing the cost of doing so. That is, digital transformation is an exercise in competitive excellence. Data, and access to and use of it via technology, is certainly critical to that effort, but it’s downstream from where the real value of industrial transformation is unlocked. 

When considering transformation, begin with your customers and market signals. Outline desired outcomes for your customers and identify critical cost-to-serve key performance indicators (KPIs) associated with doing so. With that in mind, determine what prevents you from providing those outcomes and what people, processes, and data are involved. Once those issues are identified, only then consider what technology best helps you look at those people, processes, and data to determine how they can be best leveraged to ensure those ideal customer and market outcomes are achieved.

Expand your Peer Network

Analytics can, of course, serve as a lynchpin for transformation. When engaging in anything transformational, it requires learning new things and experimentation. Those requirements present additional challenges for industrial companies that are hard wired by controlled and stable operations and transactions. That instilled caution often translates to poor planning, piloting, and adoption when it comes to analytics. So, where investments are made, use cases often do not scale as anticipated and are often abandoned. 

To break that cycle, companies need to accumulate digital wisdom, learning what it takes to motivate the organization to build digital competencies, scale them, and sustain success. However, most organizations look either within their four walls or existing ecosystems to learn. That does not work. After all, if companies already knew what to do (or had ready access to that digital wisdom), they would be doing it.  By aggressively expanding their peer network beyond those they know well, digital transformation leaders are naturally forced to bring a more open perspective. This can help them quickly learn “why” others do things rather than simply “how” they are done. Instead of seeking to imitate known uses cases, competitors, or similar companies, leaders learn how to problem solve better when it comes to digital transformation. Armed with that wisdom, they are much more willing and able to tackle entrenched issues around customer relationships, organizational culture, processes, data, and the like. They are also more patient in doing so, realizing that return on investment will scale as they improve their digital competencies.

Invest in Competitive Problem Solvers and Knowledge Transfer

As mentioned, digital transformation is not an exercise in data or technology. It is a talent war based on having better problem solvers and harnessing that expertise in a way and at a speed the competition cannot. Industrial IoT data and analytics software are simply examples of the critical tools of the trade used to unleash that expertise in new ways. Once that expertise is initially unleashed, technologies such as analytics, artificial intelligence, and the like then grow in importance, helping a company sustain a market advantage over the competition.

In addition to securing a better workforce, industrial companies need to identify where their critical intellectual property (IP) resides, secure, and codify it. In doing so, the organization can then extend this differentiating knowledge across the organization where it can be applied to gain the most effective outcomes.  Technology, especially software, does become the key to this knowledge codification and transfer, and massive, practical advances have been made in this field. Some companies are turning toward analytics solutions built upon cognitive AI. Cognitive AI applies a reasoning-based philosophy to analytics that uses numeric and symbolic inputs. And it does so by intentionally applying a range of AI and other techniques, including machine learning, semantics, natural language processing, and scenario generators, to name a few.