Data-Driven SMEs: The need to defeat the chimera

For years, large companies have guided their decisions through data analysis. Facebook, Amazon, Apple, Netflix, and Google have been pioneers on this path since the early 2010s. By 2014, the role of Data Scientist began to trend. Today, they invest billions of dollars in sustaining and deepening their data infrastructure. Their returns on investment are massive: increases in productivity, cost reduction, and competitive advantages to capture customers up to 23 times greater compared to those who do not exploit data analysis (1 and 2). According to Nucleus Research, the ROI is $13 for every dollar invested (3).

For small businesses, the path is harder, and that is why data analysis for decision-making is less widespread (4). Human and financial resources are not the same, and the processes can be expensive, complicated, and insecure. That is the Chimera blocking the imposing path with its hybrid threats.

With the boom of artificial intelligence, it is mandatory to start thinking about which processes in your organization can be automated or improved using this incredible set of tools. Those who don’t will succumb to the competition. Here, necessity appears like an approaching void, forcing us to face the beast: it is impossible to effectively automate and optimize processes if your organization does not have the data and infrastructure to do so.

How do we solve this dilemma? How do we defeat the Chimera? Bellerophon analyzed the situation, his weapons, and the best way to move forward. When we talk about data, the answer is complex and obviously depends on each company. The important thing, as always, is to start at the beginning and lay a solid foundation. Understand where the business model is heading and what data is—and will be—important to walk that path. What maturity level does the organization have? What information and data are useful, and which ones should we start collecting? (5). Then, look for the best way to structure and organize them according to available resources and the most urgent needs. Analyze the data, adjust decisions, and finally, automate.

Shortcuts are tempting, as automating a specific process can sound like a major breakthrough. However, implementing automations outside of a general framework can bring complications. If the databases are not adequate, if their structure and organization are flawed, if the IT team is not prepared to maintain and monitor the processes, and if the organization lacks the maturity to support the process, it will hardly work. Not to mention the risks regarding cybersecurity and regulatory compliance. Charging forward without a plan may seem like a bold alternative, but many brave heroes and warriors were devoured by the Chimera.

Diving headfirst into AI can involve a considerable investment of time and money, and it would be wasting an opportunity to do something with less short-term impact but sustainable, scalable, and with a much greater medium and long-term impact.

Those who remember mythology might be wondering about Pegasus, without whom victory would have been impossible. In this case, WTW can be the one to help you defeat the Chimera. And if you want to fly too high and reach Mount Olympus to be a god, we are also going to have to bring you back down to earth.

1. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance
2. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/competing-through-data-three-experts-offer-their-game-plans
3. https://nucleusresearch.com/research/single/analytics-pays-back-13-01-for-every-dollar-spent/
4. Eurostat discusses the application of AI, noting that by 2025, 17% of small businesses, 30.36% of medium-sized businesses, and 55.03% of large businesses would have used AI. While this isn’t the same as simply exploiting data, it is indicative of the trend.://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
5. Christopher Penn “data-resistant a data-driven”