Empowering the Citizen Data Scientist to Boost Business Value

In today's business landscape, every individual is potentially a data specialist, regardless of their official job title. From accountants to engineers, professionals across various domains are leveraging data to glean critical insights. This evolving role, known as a citizen data scientist, blends both art and science in data interaction. Let's explore the nuances of this role and how it is reshaping the business analytics landscape.

Who Is A Citizen Data Scientist? A Gartner Insight

According to Gartner, "A citizen data scientist is someone who creates or generates models using advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics."

But what empowers a citizen data scientist to leverage data and analytics for insightful decision-making effectively? The answer lies in three fundamental elements:

• Access to quality business data
• A robust self-service analytics (SSA) platform
• Stringent data and analytics governance processes

These elements help to tackle complex data study needs, letting business folks quickly and easily find solutions to important questions.

Citizen Data Scientist vs Data Scientist

A data scientist is a professional with expertise in various domains, including statistics, mathematics, computer science, and domain-specific knowledge. They often use programming languages such as Python or R, advanced statistical and machine learning techniques, and deeply understand data manipulation, data visualization, and data engineering.

The main differences between data scientists and citizen data scientists are their level of expertise, responsibilities, and the tools they use. Data scientists are highly skilled professionals who work with complex data problems, while citizen data scientists are non-experts who use simplified tools to perform basic data analysis tasks within their specific domain.

Citizen Data Scientist vs Citizen Data Analyst

The terms 'citizen data scientist' and 'citizen data analyst' are often used interchangeably to describe individuals in organizations who use data to inform decision-making despite not having formal training. The primary distinction between the two lies in the level of technical expertise and the complexity of tasks they undertake.

Enable Citizen Data Scientist with Quality Business Data

What are the solution patterns to support citizen data scientists working efficiently in organizations? Firstly, the right data is required for good insights and not necessarily more data. The right data in a domain-related analytics context has three main characteristics.

Right Dimensions

Fundamentally, analytics is using data to answer the right questions about the future state. Invariably, insights derived are dependent on the response (effect) and explanatory (cause) variables, and these variables are known as features or dimensions. Dimensions provide context on the measures such as price, quantity, and cycle time associated with the business process.

Right Structure 

Up to 80% of the data captured in business enterprises is unstructured data. Examples include documents, video, audio, images, and so on. Unstructured data is of little value for the analytics algorithms as the unstructured data do not have a predefined data model, which is required for analysis and data processing.

Less Variability

Business processes inherently have some degree of variation, and this variation is reflected in the data captured. Variation in data makes it difficult for the analytics algorithms to make timely and accurate predictions.

How to Empower Citizen Data Scientists?

The second component in enabling Citizen Data Scientists is the self-service analytics platform. The self-service analytics platform like Kyligence Zen enables business professionals to perform queries and generate insights with minimal IT support. A robust self-service analytics platform in the analytics and citizen data scientists context should support these key features.

Data Acquisition

An analytics platform is only as valuable as the data that is available to it.

The self-service analytics platform needs to have a few important features. First, it should connect easily to existing data sources. These sources could be the main database, such as a data warehouse, or record systems like ERP or CRM.

Next, it doesn't matter where these data sources are located. They could be on-premise, in the cloud, or on a hybrid-cloud setup.

Lastly, the platform should allow for simple management of various tasks. These tasks include organizing data indexes for quick searches, handling data loads, and updating data regularly.

Data Quality and Freshness

Getting useful and accurate insights depends on the quality and freshness of the data. Both of these aspects are at risk if data is stored in isolated and inaccessible compartments, known as silos.

• The first issue is that the truthfulness of the insights can be compromised without good data quality.
• The second issue is that if the data is not updated regularly, our assumptions about current affairs might be based on outdated information.

Performance, Scale, and Concurrency

A self-service analytics platform that is unusable because of slow response times and frozen dashboards is not viable. A true citizen data scientist wants to use data to follow and prove or disprove their insights and intuitions about the world they are analyzing. Data analysts can do quick data exploration and retrieve the piece of data they want.

Security

A self-service analytics platform doesn’t mean less or no security; governance is a key prerequisite for a successful self-service analytics platform and citizen data scientist. Self-service analytics platform platforms should support authentication of the citizen data scientist with identity management solutions and Role Based Access Control to ensure that access to sensitive data such as Payment Card Industry Data Security Standard (PCI DSS) and PII (Personally Identifiable Information) is controlled and governed.

Semantic Model

Analytics solutions depend on acquiring data from diverse systems. Given that the definitions of these data elements vary, often, there is a pressing need to offer a semantic or meaningful representation of data. Semantic models depict the relationships among specific data values. Hence, the SSA should help Citizen Data Scientists leverage a centralized semantic model so as to establish a single source of truth (SoT) for generating accurate and timely insights.

Libraries

The self-service analytics platform should have an extensive library of time-tested analytics algorithms, including access to open-source libraries such as TensorFlow, Keras, scikit-learn, and more. This will make it easy for citizen data scientists to reuse existing analytics algorithms instead of building their own solutions from scratch.

Governance

Lastly, citizen data scientists will not be empowered without the right governance processes. While there is no denying that citizen data scientists are powerful, it is also just as important to recognize that the Citizen Data Scientists enablement needs to be managed with a robust governance framework. The governance framework should identify data ownership, role evaluation, training on data literacy, optimizing queries, pre-computing results, flagging unused reports and dashboards, monitoring system performance, and other regulatory and data management activities.

So, what is the solution that brings together all three components i.e., quality business data, a robust SSA platform, and a strong data and analytics governance process to enable successful Citizen Data Scientists? Kyligence provides a holistic analytics platform by securely integrating data from various data sources to create a clean (right data), integrated, and metrics store for the citizen data scientists to derive powerful insights in near-real time. Kyligence also accelerates the productivity of citizen data scientists by automating data discovery, and data integration, and offering low-code/no-code analytics libraries for seamless and secure insight generation.

Conclusion

In today's digital and data-centric economy, analytics is a key enabler that transforms data into a business asset by providing the insights for sound decision-making. Sadly, most analytics projects have focused on centralized data teams to offer business insights, and the result is over 80% of analytics programs have failed to provide business benefits. This approach has not only delayed the consumption of insights, it has also increased the cost of transforming insights into appropriate business actions. The future of deriving value from data and analytics is to empower citizen data scientists as it will reduce cycle time, save costs, and improve customer service for organizations. However, the citizen data scientists has to be positioned for success and such positioning requires enablement of quality data, a strong governance process, and an easy-to-use self-service analytics platform like Kyligence.

Are you excited about being a citizen data scientist and gaining insights from data? Try out Kyligence Copilot to start your journey as a citizen data scientist!

Reference

1. Luisi, James. (2014). "Pragmatic Enterprise Architecture". Morgan Kaufmann.
2. Miranda, Gloria Macías-Lizaso. (2018). "Building an effective analytics organization". McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/financial-services/our-insights/building-an-effective-analytics-organization
3. Southekal, Prashanth. (2020). "Analytics Best Practices". Technics Publications.
4. Gartner. "How to Use Citizen Data Scientists to Maximize Your Data Strategy". Retrieved from https://www.gartner.com/smarterwithgartner/how-to-use-citizen-data-scientists-to-maximize-your-da-strategy