Empowering Citizen Data Scientists by Removing the Obstacles

Anyone who has ever hired (or attempted to hire) a data scientist is well aware of the challenges: not only are they extremely difficult to find, cut-throat competition, and sky-high salaries further make them inaccessible.

With harvesting data for insights becoming a core business requisite, many organisations are investing in automated machine learning tools, or better still in citizen data scientists who can drive as much value from an organisation’s data science efforts – if not more.

Removing Obstacles

There’s a world of opportunities citizens data scientists offer, including the ability to use big data tools and technology, create data models, and analyse and develop predictive analytics applications – without having to be data science or business intelligence experts.

Their primary job function lies outside statistics and analytics; it’s their deep knowledge of the business and analysis tools that makes them capable of tackling sophisticated analytical tasks that would typically have required a full-fledged data scientist.

While citizen data scientists are enabling organisations to take advantage of their ever-growing collections of data, there are several obstacles that come in the way of leveraging the skills and expertise:

1. Absence of a citizen data scientist strategy: Like any big data project, the results that citizen data scientists can showcase are far-reaching. But citizen data scientists can only provide their expertise when the organisation has a strategy in place that looks to identify such power users and leverage their capabilities to unlock value from data. Support from C-suite-level visionaries and a robust citizen data scientist strategy is needed to recognize how the organisation can take advantage of their citizen data scientists to help grow their company and its business. Leaders must constantly identify data evangelists across lines of business and department silos that can lend their support and guidance to the organisation’s analytics initiative.
2. Lack of specialised training: There are tons of data analysis tasks that citizen data scientists are known to exceed at. But, for many others, the lack of specialised data science training can restrict them from driving value. For instance, implementing a machine learning-based approach for a common business problem might be a cakewalk for citizen data scientists. But, carrying out a risk-benefits analysis for an insurance product or exploring new approaches for cancer treatment might need more than just knowledge of data science tools. For problems that are competitively differentiating or require deep domain expertise, providing citizen data scientists with the resources and training needed is critical to ensure the intended analytics outcomes.
3. A sea of corrupted, irrelevant, and inconsistent data: Voluminous amount of data are being generated by organisations each day that offer a treasure trove of insights. However, expecting citizen data scientists to glean this data without first cleansing or training the data is a recipe for disaster. Since most data science algorithms need to be fed with data that is relevant, updated, and clean, it is important to eliminate irrelevant and error-prone data and provide data sets that are consistent. Have appropriate data governance strategies in place so you can always ensure a single source of truth – without citizen data scientists having to spend an enormous amount of time cleansing data themselves.
4. Improper access to analytics tools: Many citizen data scientists bring with them a range of capabilities, which, when identified and applied correctly, can completely transform business outcomes. However, citizen data scientists can only offer value when they have easy access to the right analytics tools. Expecting citizen data scientists to adjust to the complexity of tools or providing systems that require complex coding will only come in the way of their jobs. Make sure you provide citizen data scientists with tools and systems that are friendly and that automatically perform a lot of the modelling. These systems should offer drag-and-drop features and emerging voice interfaces – so they can focus on unearthing insights that matter and not get bogged down by having to develop or manage complex algorithms.
5. Poor collaboration with data scientists: Many organisations invest in data scientists who can build complex analytics algorithms and discover insights from huge data sets. They also have a large number of citizen data scientists who can provide their data analytics expertise to further enhance outcomes. However, if the two cannot co-exist peacefully, the resulting analytics might not be as effective as it can be. Therefore, it is every organisation’s responsibility to ensure citizen data scientists work in a collaborative fashion with data scientists. Together, they can combine their business and technical acumen to carry out advanced data modeling, bridge departmental gaps, and find relevant patterns.

Empower them

Citizen data scientists, through their knowledge and expertise of data analysis tools, have the power to automatically detect, visualize, and narrate relevant findings from growing data – without building models or writing algorithms themselves.

Because they are capable of performing tasks that ideally require specialized data science skills, they can enable organisations to unearth value from data – quickly and more cost-effectively. However, when you hand over the keys of data analysis to power users who can identify important challenges and opportunities, you need to make sure you steer clear all obstacles that come in their way. By empowering your citizen data scientists, you stand the chance of realizing the full potential of your data and unlocking a new competitive advantage.