Embracing a data culture during business transformation
‘Data makes the world go round’ is a common refrain these days, and rightfully so, given that the amount of data generated globally has increased by nearly 5,000% in the last decade. And with the big data analytics market forecast to reach $103 billion by 2023, the importance and efficiency of data crunching has rapidly emerged as a critical component of a company’s success. Data is one of the richest assets possessed by any organization, and the benefits of automating processes to crunch numbers and predict business outcomes can provide a huge competitive advantage. When automated, data crunching is effortless, instantaneous, and scalable. However, as the quantity of data being generated continues to grow, poor data quality costs the US economy up to $3.1 trillion annually.
In other words, while data can make you rich, beware of fool’s gold!
To maximize the potential of available data, a business needs to adapt a strong data culture that focuses on the strengths and vulnerabilities of their data and its usage. Careful analysis should plot a company’s course towards implementation of a data-driven strategy, or a data-informed strategy. Applied in combination, they can generate considerable competitive advantages.
A data-driven strategy provides the capacity to make decisions based on data alone. It relies on fact-based analysis, free of human bias, and is best served when focused on a single business objective, using clearly identified relevant data, and where decision models can be built and tested. Data-driven strategies are frequently deployed in the insurance industry when assessing risk, discounts, pricing, and more, and in the logistic and transportation industry where operational components, such fuel consumption and speed regulation calculations, can be defined by data.
In other areas of a company’s operations, 100% reliance on data can also let you down. As stated by W. Edwards Deming, a statistician and business consultant recognized as a leading management thinker, ‘without data, all we have is an opinion’. However, the reverse is also true - without opinion, all we have is data. Being data rich, but information poor, is a recipe for disaster if using a purely data-driven strategy. The key to a successful data strategy lies in a clear understanding of the abundance and quality of data that a company possesses.
A strong data culture should define areas where an organization can benefit from a data-driven strategy, and when and where their data can be leveraged into a data-informed strategy that combines data analytics with the human instincts, expertise, and experience of strong leadership. To better understand the differences between the two strategies, consider the following examples:
The film Moneyball depicts the data-driven strategy deployed by the Oakland Athletics baseball team in respect to player selection. The organization revolutionized sports management by applying a purely data-driven strategy over human assessment in order to field a competitive team, despite the organization’s salary limitations. Using statistical indicators across the board, they focused on players who achieved specific areas of production. When combined, the data-driven strategy enabled them to field a fairly competitive team for years to come, with a lower salary base than other teams.
In terms of a successful data-informed strategy, the revamping of Airbnb’s website provides a perfect example. Based on customer feedback and industry trends, the company decided to alter its user interface (UI) to enhance the user experience. However, once implemented and launched, the company experienced a sharp drop in daily traffic. Based on data alone, a data-driven strategy may have enticed the company to return to its previously deployed UI. However, guided by a data-informed strategy, Airbnb recognized that the new interface was superior and dug deeper for an explanation of the data. They eventually found that the drop in traffic was attributable to an Internet Explorer bug that hampered user experiences. Once fixed, the new Airbnb site experienced record traffic.
The previous examples show how data-driven and data-informed strategies can produce optimal business outcomes when properly applied. However, consider the hypothetical implications of blindly committing to one strategy over the other. An app developer’s data might indicate that users who receive notifications also tend to be more active on the app. A purely data-driven strategy might lead the company to increase the notifications it generates. As a result, further data may indicate that users start to ignore notifications, and that even those who were deemed more responsive previously have now turned off their notifications. In this case, a data-informed strategy would have encouraged the company to dig deeper into the original data, where they would have found that the link between notifications and engagement is based on the quality of specific content.
On an operational level, a company can gain significant advantages by leveraging rich historical data into a data-driven strategy that automates its day-to-day processes. However, when it comes to strategic decisions, a data-informed strategy will typically provide more favorable results. For example, a company looking to expand into a new market that makes decisions based purely on data obtained from existing markets may see its plans compromised. So, how do you determine which data strategy to deploy? The definitive answer is…it depends.
To learn more about why embracing a data culture is crucial for your organization, contact us to discuss about your next project!