In the history of the United States, the Gold Rush was a phenomenon that captivated an entire country. The promise of instant wealth led thousands of people to leave their homes in search of the coveted precious metal. However, reality fell far short of expectations, and only a fortunate few managed to strike a true fortune.

Today, in the digital age, we are witnessing a new rush: the Data Rush. Organizations are obsessed with data, experiencing a frenzied eagerness to find something valuable buried among the terabytes of information they hold, driven by the promise of business success and the firm belief that, if they collect enough data and analyze it properly, they will uncover the key to exponential growth and an unbeatable competitive advantage.

However, much like the 19th-century gold prospectors, companies that blindly dive into data hunting may face a very different and disappointing reality. Mere accumulation of data does not guarantee success. In fact, it can be overwhelming and counterproductive if not managed properly.

Challenges

During the Gold Rush, many prospectors faced the harsh realities of life in mining camps: tough working conditions, fierce competition, and scarce basic resources. Similarly, in the data hunt, companies may encounter challenges such as a lack of analytical talent, difficulties integrating disparate systems, and concerns about data privacy and security.

Moreover, just as the quality of gold varied greatly, so can the quality of data. Incomplete, inaccurate, or irrelevant data can lead to erroneous conclusions and disastrous business decisions. True value lies in the ability to transform that data into meaningful information and, ultimately, into strategic action.

Is it impossible to extract value from our data? Absolutely not. But rather than blindly relying on magical formulas, applying sophisticated machine learning and artificial intelligence algorithms to raw data without context, we must provide proper governance guidelines and apply proven data science tools for analysis.

Data does not emerge from nowhere. The activities of organizations can be formally described through processes. When these processes are properly designed and digitized, they generate relevant business information within a connected environment along the organization’s value chain. It is from this relevant data, approached with clarity, realistic objectives, and solid analytical capabilities, that true value can be derived.

Rather than succumbing to trends, organizations should adopt a more thoughtful and strategic approach. This means identifying which processes are truly important to their business and focusing on collecting and analyzing data related to them effectively.

It also involves investing in the right people and technologies to extract valuable insights from this data and make informed decisions. Data scientists combine skills in computer science, mathematics, and—most importantly—business knowledge.

Extracting value from data is not about massive accumulation of information but about transforming that information into actionable knowledge. Moving from a big data approach to a smart data approach, whether the dataset is large or small.

And what if I don’t clearly understand my processes—can data help me uncover them? Absolutely. Regardless of the information systems an organization has, activity records exist in one form or another or can be easily obtained. Using process mining techniques on tasks and communications, it is possible to discover and analyze how processes operate, their efficiency, bottlenecks, and opportunities for improvement.

So, if I can understand my processes from data, have I struck gold? Dear friend, if the data isn’t of good quality, what you’ll have is not gold, but coal.