June 2020 Summaries

Data scientists, once hailed as holding the "sexiest job of the 21st century," face dissatisfaction despite high salaries and perks from top companies like Microsoft and Google. A significant proportion are unhappy due to spending extensive time on tedious data cleaning and organizing tasks, being overwhelmed by ad-hoc reporting demands, and experiencing a disconnect between their work and organizational goals. This misalignment often stems from a "data language barrier" where business leaders fail to grasp the collaborative nature of data science, leading to ineffective use of data insights and a high failure rate in big data projects. To improve job satisfaction and harness data's potential, organizations are encouraged to enhance data literacy, empower data scientists to focus on impactful work, and adopt community-driven approaches to analytics. The future remains promising for data scientists, as their skills are increasingly essential for deriving actionable insights from ever-growing datasets.

A collaborative approach to analytics, which involves the integration of diverse skills and knowledge from various roles within an organization, enhances both organizational and individual team performance by multiplying opportunities for insights. Nearly four in ten organizations are already using collaborative methods for analytics, with more planning to adopt these strategies. Such collaboration helps in discovering and utilizing available data more effectively, as it breaks down data silos and ensures that more relevant data is used. By engaging domain experts in the analytics process, organizations can leverage their on-the-ground experience for deeper insights, which can prevent costly errors and speed up the delivery of actionable insights. The involvement of multiple perspectives enriches the "why" questions behind data, promoting faster and more accurate decision-making, critical in a competitive landscape where timing is vital. Furthermore, fostering a culture of curiosity and participation across the organization can drive innovation and the pursuit of new insights, ultimately providing a significant competitive edge.

Data visualization is crucial for effective data analysis as it enables the clear communication of insights that inform business decisions. Community-driven analytics is an emerging approach that empowers all team members, regardless of technical skill, to contribute to business intelligence efforts, fostering collaboration and faster data-driven decision-making. Sigma's Analytics and Business Intelligence (A&BI) platform supports this by providing real-time data access, tools for data exploration, and the ability to share and repurpose analyses across teams and applications. Recent enhancements to Sigma's platform facilitate the creation and management of dynamic dashboards, improve collaborative features, and introduce application embedding capabilities, allowing users to integrate visualizations directly into business workflows. These updates aim to democratize data access and empower organizations to derive competitive advantage by enabling iterative data exploration and real-time insights without relying solely on data experts.

Data Demystified is a monthly video podcast by Sigma focusing on enhancing data literacy and exploring transformative data insights. In a recent episode, host Daniel Codella converses with Joseph Bates, an Analytics Architect at Migo, a Series B FinTech company. Migo uses a cloud-based platform to extend credit and payment options to underbanked populations in developing regions like Western Africa and South America, providing vital financial lifelines through innovative technologies such as SMS messaging and data from phone bill records. Bates discusses Migo's evolution from a rudimentary data analytics setup to a sophisticated architecture centered around Snowflake, which integrates various data sources and supports self-service analytics. He highlights the importance of consistent data terminology and a unified data model to enhance collaboration and decision-making. Migo's goals include improving data accessibility, reducing engineering bottlenecks, and transforming customer engagement funnels into continuous loops using marketing automation. Bates also emphasizes the significance of enhancing their credit scoring models, which are crucial for Migo's operations, as their customers typically lack traditional credit scores.

Collaborative analytics is a community-driven approach to business intelligence that leverages the collective knowledge and perspectives of individuals across an organization to enhance data analysis and insights. This method emphasizes the importance of involving stakeholders from various departments, partners, and even industries to create a data-curious culture that fuels innovation and problem-solving. Despite its potential, many organizations face challenges in fully adopting collaborative analytics due to unsuitable legacy tools and cultural barriers, which hinder the involvement of non-technical users. To overcome these obstacles, companies must invest in modern analytics tools with robust collaboration features, data democratization, and a balanced governance framework. By fostering a culture of collaboration and curiosity, organizations can unlock the full potential of their data, leading to more accurate insights, faster problem-solving, and a significant competitive advantage.