Expanding your audience is a key part of running a thriving business – however, much of the engagement involved in this process today is automated. Maybe you choose the sites you advertise on based on external surveys and analytics or your strategy is determined partially by new machine learning systems. These may seem like powerful, convenient ways to set your business trajectory, but if you’re starting with the wrong data, these systems can fall short – particularly when it comes to multicultural engagement (MCE) programs.
Across the board, businesses are missing necessary data about the interests of minority groups, and without that information, digital strategy efforts won’t reach this important audience. So how do we bridge the gap in this digital age?
Look Behind The Data
To begin reshaping our data culture, we need to acknowledge that human fallibility lies behind all data. For example, if there are classist or racist elements in a data set – something that’s almost unavoidable in our current culture due to research biases – then those factors will be part of computer learning systems and strategy formation. Data scientists need to take responsibility for these situations when they occur and remedy when they reflect problems in human knowledge and measurement, not a factual reality.
How do racial or class-elements play out in our business strategies? For a company that’s seeking to diversify its client base, data might indicate that a racial group isn’t interested in its product, but the problem is, in fact, one of exposure and access, not race. This can result in a flawed marketing strategy that concludes it isn’t worth marketing to a particular racial group, rather than one that concludes that this community needs to be introduced to the product in a relevant, accessible way. The algorithm can be flawed because our research suffers from particular cultural misunderstandings.
Identify Your Problems
Another issue that arises when trying to solve MCE-related problems using data is that many businesses fail to identify the specific issue at hand when they hire data scientists. This is a barrier to implementing data-driven cultures because it relies on the expectation that data scientists can reach some incisive conclusion just by looking at numbers, but without understanding the culture of the business.
For example, consider a pet-related business that wants to fill an unmet need – but they don’t know quite what that need is. Based on data sets, consultants might conclude that they need to focus more on exotic pets or natural pet products, but is that actually the answer? Is there something missing in the data? Maybe the problem is actually a lack of data on Hispanic pet owners. You can’t solve a problem you can’t identify.
The Necessary And The Ethical
Ultimately, MCE programs are vital to business success today, not an optional growth consideration like they were in years past, and that means companies have an obligation to undertake these programs within an ethical framework.
That means that companies have to be responsible with data collection and management, should not participate in unauthorized data sharing or sales, and always interrogate the data – data use should always be in the spirit of the 'do-right rule.' If your customers wouldn’t be happy with how you’re using the data, where it came from, or the underlying assumptions involved in its collection, you’re crossing a line.
Companies should be making their best effort to engage all potential customers in respectful, culturally competent ways, and we can’t always do that with data. It’s time we take a step back from the data-let strategies and ask some critical questions before moving forward with future MCE programs targeted on business growth.