Enterprise software systems like ERP and CRM have reached the point of diminishing returns. The data inputs for these systems — human entry and process-based events — have hit natural limits. The value provided is restricted to internal business processes, defining how teams interact. This approach isn’t equipped for tapping crucial data sources to influence financials that move businesses and customers forward.
It’s time to link data to revenue. New business models will be formed by intelligent platforms that use AI to break down silos, connect different systems, and unlock previously inaccessible or overlooked data. This creates one source of truth for driving financial outcomes.
Heavy industry is changing. Successful companies like Caterpillar and Berkshire Hathaway Energy have broken the cycle of reliance on legacy technology. Instead, they’re tapping their own data sources, which until now have been underutilized.
It’s why industrial business executives are zeroing in on digital transformation. They recognize sizable opportunities to save time and money by dramatically increasing productivity and operational efficiency.
It’s time to link data to revenue
I’ve observed countless examples of technology stacks serving siloed purposes in heavy industry. They fail to help key functions like operations, maintenance, engineering, finance, and customer service. They also frustrate CIOs and technologists who spend millions of dollars integrating systems.
Too often, leaders fall into the trap of referring to their businesses via IT systems without asking, “What data do I need to run my business?” At best, it’s limiting. At worst, it’s damaging.
Because functional teams rely on disparate pieces of technology that rarely communicate with each other, they can’t leverage learnings from one area (stored in its system) to improve an adjacent area (making that system smarter). This includes valuable machine data that reveals financial truths about revenue-generating assets.
This old blueprint can’t support broader organizational objectives or generate the financial results businesses need. Nor does it promote the necessary flexibility and agility for winning in today’s markets. Businesses are self-limited by their speed of innovation and creative curiosity, placing a strict governor on success.
The good news: This no longer has to be the case. Businesses are overlooking priceless data their legacy systems aren’t designed to capture or use.
Businesses must separate signal from noise
In its raw form, big data is too difficult to use in a meaningful way. It’s important to understand something: Not every piece of data that can be counted actually counts, and not everything that counts can be counted. This is why humans will always be required.
For decades, discussions centered on the mountain of unused data businesses are sitting on, advancing the causes of cloud, server, and storage providers. While it’s part of the equation, it’s come at the expense of businesses accessing the right data — at the right time, for the right outcome.
An intelligent platform does the legwork. Using AI that’s purpose-built for heavy industry, it automates the complex task of preparing, cleansing, and distilling the data that really matters. It separates signal from noise, linking business KPIs to data that can change economics. It then turns data into insights, predictions, and recommendations people can act on to make informed decisions that impact financials.
Industrial AI gets smarter with every data point ingested. It also gets increasingly accurate over time. This establishes the vital closed loop that lets systems continuously learn from business value delivered.
Technology’s next great moment will accelerate financial outcomes.
In the 1990s, ERP and CRM were monumental. They created efficiencies by automating billings, handling transactions at scale and facilitating customer relationships. Businesses could stock inventory and labor for the future based on historical behavior and trends.
However, this opened doors to incremental process improvements, not bottom-line improvements that businesses need. But we’re at an inflection point. Industrial AI enables businesses to reduce costs, increase revenue, and define new investor metrics with which to measure success.
By building data counterparts that digitally represent physical machines, equipment, and components, industrial AI predicts failures and plans minimal maintenance times. Businesses can answer principal questions: “How do I optimize operations through the lens of remaining asset life? How do I optimally plan for required parts and labor based on current and future production levels?” They can run critical assets at maximum efficiency to produce more revenue.
Businesses can analyze actual versus expected output, identifying underperforming assets and examining root causes. They can compare against fleet-wide benchmarks and make adjustments that increase production levels.
By predicting, repairing, and preventing future failures before they happen, assets’ lifespans increase. This captures more value and lowers capital expenditures. With more reliable assets and safer work environments, businesses can minimize risks and even save lives.
Jay Allardyce is the EVP of Industries, Data and the Partner Ecosystem for industrial AI software leader Uptake. He oversees Uptake’s industry business lines with a focus on industrial data strategy and partner ecosystem. Before joining Uptake, Jay was Chief Product and Marketing Officer for GE Digital. He was also a founding member and Chief Operating Officer of GE’s first vertical digital business, Power Digital Solutions. Jay previously held senior leadership roles at Hewlett-Packard Software, Vertica and Adobe Capital Partners. Follow Uptake on Twitter at @uptake and Jay at @dyce1120.
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