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When to Allow Employees to Access Data and Analytics

As business leaders look to democratize data and analytics within their organizations, the question they should really be asking is “when” makes the most sense. We provide the following criteria to help you determine when to empower your data citizens: consider the skill level of citizens, measure the importance of the problem, determine the complexity of the problem, empower those with domain expertise, and challenge experts to look for bias.

As business leaders strive to get the most out of their analytics investments, democratizing data science often seems to provide the perfect solution plan. Using analytics software with no-code and low-code tools can empower almost anyone with data science skills. At best, this leads to better decision-making and greater self-reliance and self-service in data analytics—especially as data scientists are in short supply. Coupled with reduced talent costs (fewer high-cost data scientists) and more scalable customization to tailor analytics to specific business needs and circumstances. However, in all the discussions around whether and how to democratize data science and analytics, a key point has been overlooked. The conversation needs to define when to democratize data and analytics, and even redefine what democratization means. Fully democratizing data science and analytics comes with many risks. As Reid Blackman and Tamara Sipes wrote in a recent article, data science is hard, and untrained “experts” can’t necessarily solve hard problems, even with good software. The ease of clicking a button that produces results doesn’t guarantee a good answer — in fact, it can be deeply flawed, only a trained data scientist will know about it.

It’s only a matter of time

However, even with these reservations, the democratization of data science is here to stay, software And the proliferation of analytics tools is proof. Thomas Redman and Thomas Davenport are among the advocates of cultivating “citizen data scientists,” going so far as to screen each position for basic data science skills and competencies. However, the democratization of data science should not be taken to extremes. Analytics doesn’t have to be at your fingertips for organizations to thrive. How many talented people are not hired simply because they lack “basic data science skills”? This is impractical and too restrictive. As business leaders look to democratize data and analytics within their organizations, the question they should really be asking is “when” makes the most sense. Start by acknowledging that not every “citizen” in an organization has the same skills to be a citizen data scientist. As Nick Elprin, CEO and co-founder of Domino Data Labs, which provides organizations with data science and machine learning tools, told me in a recent talk, “Once you start modeling, more complex statistical problems often lurk in the Surface.”

The Challenge of Data Democratization Consider a grocery chain that recently used advanced predictive methods to fine-tune its Demand planning, which tries to avoid having too much stock (leading to damage) or too little stock (leading to lost sales). Losses due to spoilage and stock-outs are modest, but the problem of reducing them is tricky — considering all the variables of demand, seasonality, and consumer behavior. The complexity of the problem meant that the grocery chain couldn’t leave it to citizen data scientists, but tapped a real, well-trained team of data scientists. As Elprin and I discussed, data citizenship requires “representative democracy”. Just as American citizens elect politicians to represent them in Congress (presumably to act in their best interests in legislative matters), organizations need the right representation of data scientists and analysts to weigh in on issues that others simply don’t have the expertise to speak on. In short, it knows when and how much to democratize data. I suggest the following five criteria: Consider skill levels for “citizens”:

citizen data scientist, In some form, it’s here to stay. As mentioned earlier, there simply aren’t enough data scientists to go around, and using this scarce talent to solve every data problem is unsustainable. More importantly, the democratization of data is key to instilling analytical thinking throughout the organization. A well-publicized example is Coca-Cola, which launched a digital academy to train managers and team leaders, producing graduates of the program who have worked on some 20 digital, automation and analytics initiatives across multiple locations in the company’s manufacturing operations. However, when it comes to predictive modeling and advanced data analytics that can fundamentally change a company’s operations, it is critical to consider the skill level of the ‘citizen’. Sophisticated tools in the hands of a data scientist are additive and valuable; just the same tool in the hands of someone who “plays with the data” can lead to errors, incorrect assumptions, questionable results, and misinterpretation of results and conclusions. Measuring the importance of the problem: The more important the problem is to the company, the more necessary it is to have a process data Analysis experts. For example, generating a simple graph of historical buying trends could probably be done by someone with a dashboard that displays data in a visually appealing form. But strategic decisions that have a significant impact on a company’s operations require expertise and proven accuracy. For example, how much an insurance company should charge for a policy is so central to the business model itself that it would be unwise to delegate this task to a non-expert. Determining the complexity of the problem: Solving complex problems is beyond the capabilities of the typical citizen data scientist. Consider comparing customer satisfaction scores across customer segments (simple, well-defined metrics and lower risk) versus using deep learning to detect cancer in patients (complex and high risk). This complexity cannot be left to non-experts to make arbitrary decisions—and potentially wrong ones. Democratizing data makes sense when complexity and risk are low. An example is a Fortune 500 company I work for that relies on data throughout its operations. A few years ago, I ran a training program in which more than 4,500 managers were divided into small groups, and each group was asked to articulate an important business problem that could be solved analytically. Teams are empowered to solve simple problems using the software tools available, but most problems surface precisely because they are difficult to solve. Importantly, these managers are not responsible for actually solving these hard problems, but instead work with the data science team. Remarkably, these 1,000 teams identified no fewer than 1,000 business opportunities and 1,000 ways analytics could help the organization. Empowering people with domain expertise: If a company is looking for some “directed” insights – — customer X is more likely to buy a product than customer Y — then data democratization and some lower level citizen data science might be enough. In fact, dealing with these types of lower-level analytics can be a great way to make some simplified data tools available to those with domain expertise (i.e. closest to the customer). Greater precision, such as dealing with high-stakes and complex problems, requires specialized knowledge. The most compelling case for accuracy is when making high-stakes decisions based on a certain threshold. For example, if an aggressive cancer treatment plan with significant side effects is to be implemented and the probability of cancer is greater than 30%, it will be important to distinguish the difference between 29.9% and 30.1%. Precision matters – especially in medicine, clinical operations, technical operations, and financial institutions navigating markets and risk, often for very small profits at scale. Challenging experts to look for bias: Advanced analytics and artificial intelligence can easily lead to biased” decisions. This is challenging in part because the purpose of the analysis is to differentiate—that is, to make choices and decisions based on certain variables. (Send this offer to the older man, but not the younger woman, because we think they’ll behave differently in response.) So the big question is when this discrimination actually is acceptable, even good – and when it is inherently problematic, unfair and dangerous to the company’s reputation. Consider the example of Goldman Sachs, which has been accused of discrimination because the Apple Card offers lower credit limits to women than to men. In response, Goldman Sachs said it did not use gender in its model, only factors such as credit history and income. However, one could argue that credit history and income are related to gender and that using these variables penalizes women who have lower average incomes and have historically had fewer opportunities to build credit. When using discriminatory outputs, both policymakers and data professionals need to understand how data is generated and how it is interconnected, and how to measure factors such as differential treatment. A company should never jeopardize its reputation by letting citizen data scientists alone determine whether a model is biased. Data democratization has its benefits, but it also comes with challenges. Handing everyone the keys doesn’t make them experts, and gleaning the wrong insights can be disastrous. New software tools can make data available to everyone, but don’t mistake this broad access for true expertise.



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