Problems With Structural Bias With Big Data Automation Models

Automation is not a fad. It is the future of business models in almost every industry. Unfortunately, automation has introduced new risks that brands must prepare for.

One of the biggest concerns that have been raised in recent months is the risk of institutional bias and unintentional discrimination. Brands may make decisions based on demographic information with limited sample sizes or flawed data sets.

This can lead to some problems. Brands must understand the challenges of structural bias in their data.

What problems can biased data create?

Brands are discovering a couple of different ways that biased data can affect their business models. Here are some of the issues they must be prepared to address.

Allegations of employment discrimination based on flawed data

A couple of months ago, James Damore, a Google employee, stirred up quite a controversy after publishing his Google manifesto. He cherry-picked some studies to present data that yielded an unfavourable view of female employees at Google. Wired’s Megan Molteni states that this shows the problems that poorly selected data can create in an increasingly diverse workforce:

It wasn’t a screed or a rant, but, judging by his document, Damore clearly feels that some basic truths are getting ignored ”silenced, even ”by Google’s bosses. So in response, the engineer adopted a methodology at the core of Google’s culture: He went to look at the data. Google’s Ideological Echo Chamber wants to be a discussion of ideas about diversity through solid, ineluctable science.

Brands that rely too heavily on big data may be unwittingly engaging in similar to us, even if their goal is the exact opposite. As Apple and other brands develop new diversity programs, they are trying to understand the differences between employees and different demographic groups better. Big data may help them.

However, their data may be skewed due to poor representation of different subgroups. Forbes wrote an article on this topic recently.

Poorly optimised marketing campaigns

Marketing automation is becoming more essential than ever. Most brands use big data to understand their customer base and optimise their outreach strategy. Marketing automation software companies, like GetResponse, integrate third-party business tools like Salesforce CRM and Shopify eCommerce platforms to provide a steady stream of real-time data about their customers at different stages of the buying cycle.

One of the best ways to boost ROI is tailoring their marketing automation strategy to individual customer groups.However, your marketing automation strategy will be ineffective if it is predicated on misleading or incomplete data. This can occur for the following reasons:

Brands use the same ads to engage with all customer groups at the beginning of their funnel. Their data may indicate that one demographic group is less interested in their offer when the problem is their messaging.

Some demographic groups may be overrepresented with their targeting strategy. They will make these observations while using email marketing platforms that can track their ROI. This is particularly common for brands that rely heavily on media buying. They may display ads on sites that appeal to certain demographics, so Other users may not be exposed to their message. As a result, they may not have a large enough sample of certain demographics to draw statistically significant conclusions.

When brands use the same marketing strategy to reach every customer group, Amy finds that some of their subgroups are not representative of their general population. For example, a brand may use the same ads and target options to attract both male and female users. They may find that only female users that don’t conform to traditional gender perspectives will convert. As a result, they don’t adequately represent the needs and preferences of female customers as a whole.

Use Common Sense with Your Big Data Strategy

Big data is helping brands better understand their employees and customers. However, it is easy to draw the wrong conclusions if your data is poorly structured or interpreted the wrong way. Try to preserve the integrity of your data as much as possible, especially when it comes to issues of diversity.