Why operational data is the industries hidden gold?

In a world awash with data, the term ‘big data’ has become somewhat of a misnomer. For truly valuable data, it isn’t just about quantity; it’s about quality and context. The operational data found in the industrial sector—spanning across manufacturing, oil and gas, water, and energy companies—represents the lifeblood behind the scenes, the unsung hero in our daily lives. Yet, despite its pervasive importance, there’s an elephant in the room. The operational data that powers these industries is, in truth, obscenely expensive—but why? Another good read is the article

Operational data is wealth, but at what cost?

Consider your bank statement. You check it meticulously, you monitor every transaction, and you treat it with reverence because it represents your worth. Operational data should be treated no differently. Yet, in the sprawling world of industry, it’s all too often collected and then…neglected.

This negligence comes at a steep price, one that isn’t solely financial but also one that stifles the untapped potential of our industrial capabilities. The truth is, our neglect of operational data is tantamount to neglecting a bank account; only, this is a cosmic vault of untold riches, ripe for the taking.

The toll of operational data neglect

Lack of data contextualization

Operational data is so much more than a series of points on a graph. It’s a complex web of information that, without proper contextualization, is rendered practically useless. Imagine if, instead of your bank statement, you were handed a scatter plot of numbers. Without knowing what each data point represented, you’d be hard-pressed to make any real sense of it. The same applies to operational data. The lack of associated context makes drawing any meaningful conclusions a Sisyphean task, with the data often dismissed as unreliable.

Operational data silos and integration challenges

But context doesn’t exist in a vacuum—it’s reliant on the relationships between different datasets. In industry, these relationships are frequently severed by data silos, preventing the holistic view that is so crucial. Integration challenges arise not only from technological limitations but also from organizational and cultural divides that lead to disparate data governance and management practices, all of which inflate the cost of operational data by magnitudes.

Cost of data management and governance

High volumes of data require meticulous management, and operational data is no exception. The costs of storage, processing, and security are exorbitant, but they pale in comparison to the cost of poor governance. When data lacks clear ownership or is mismanaged, it can lead to compliance violations that come with their own taxing financial penalties. This cost extends from implementation through to the entire lifecycle of an asset, exponentially driving up the overall expenditure.

Limited use of operational data analytics

Data without analysis is like a book without a reader—it exists in a vacuum, devoid of purpose. Many industrial organizations are guilty of underutilizing their data analytics potential, a critical misstep that sabotages the innovation and efficiency that analytics can bring. Lacking a robust analytics strategy and the necessary tools, companies miss out on leveraging operational data for predictive maintenance, real-time process optimization, and strategic decision-making, which can significantly impact the bottom line.

Inefficient use of operational data

Even when data is harnessed for analytics, the use can be inefficient. Tactical, short-term decisions may bring minor improvements in the moment, but long-term strategic planning is where the true value of operational data is realized. By failing to think holistically and instead chasing quick fixes, companies fall into a cycle of firefighting rather than fire prevention, a mindset that does little to capitalize on the wealth of operational data.

Rectifying the costly oversight

Addressing the expense of operational data isn’t a matter of finding the lowest bidder. Instead, it’s a call to action for comprehensive reforms that tackle each of the above-mentioned challenges head-on.

Establishing a data-first mindset

Organizations must reframe their approach to data from an ancillary byproduct of operations to the critical asset that it truly represents. This shift in mindset is the foundation for addressing the management, governance, integration, and contextualization issues that have hindered us for so long.

Prioritizing integration and contextualization

Investing in robust integration solutions that bridge the gaps between different datasets and technologies is pivotal. This, accompanied by a concentrated effort to provide the necessary context for operational data, forms a dynamic duo capable of unlocking actionable insights.

Investing in analytics capabilities

Without proper tools and training, data analysis becomes an insurmountable hurdle. By investing in appropriate analytics technology and upskilling the workforce, companies can transform their operational data into a strategic asset.

Cultivating a culture of data-driven decision-making

A culture that values the role of data in decision-making is one poised for success. Encouraging a data-driven approach at all levels, from the boardroom to the plant, fosters an environment where data is used efficiently and effectively to drive sustainable business outcomes.

Leveraging operational data for long-term strategy

Strategic vision is the guiding star for any business. Leveraging operational data to inform this vision is critical for planning ahead and ensuring that the investments made today lead to future dividends.

Operational data governance: our collective responsibility

The expense of operational data isn’t an issue that falls solely on the shoulders of industry leaders or data scientists. It’s a collective responsibility that requires a multifaceted approach and a unified front.

In acknowledging the profound cost of data oversight, we can begin an open dialogue that spurs the necessary changes. We can champion advocacy for better data practices, influence policy to support data initiatives, and, ultimately, transform operational data from a burdensome cost into the wellspring of innovation and prosperity it was always destined to be. The time for action is now, and the data is ripe for the picking. It’s our choice whether to seize it or to watch, helplessly, as it slips through our fingers.

Operational data is not merely information—it is power, potential, and promise. We owe it to ourselves, to our industries, and to the future, to uncover its true value. By doing so, we don’t simply cut costs; we create opportunities. We don’t just save money; we make it. And we don’t just manage data; we govern it like our own wealth, for it is, in every sense of the word, our industry’s hidden gold.