How to build Self-Service Analytics at Scale
The article was originally published on our blog at medium.com.
In today’s data-centric world, organizations strive to harness the power of data to make informed decisions. Self-service analytics has emerged as a game-changer, enabling users across an organization to access and analyze data independently, reducing dependency on data teams. This article explores the concept of self-service analytics at scale, highlighting its benefits, challenges, best practices, and technological considerations. By implementing a robust self-service analytics strategy, organizations can empower their users, foster a data-driven culture, and unlock valuable insights to drive success.
Data-Driven Company Framework
Data Wizards
Analytics developers, business analysts, consultants, admins — they design and build analytics solutions. They play a crucial role in in the field of analytics by enabling organizations to extract meaningful insights from their data. Here are some key tasks and responsibilities:
data extraction and transformation,
analytics solution design,
data modeling and analysis,
dashboard and report development.
Overall, Data Wizards bridge the gap between data science, data engineering, and business stakeholders, leveraging their technical expertise to create trusted and impactful analytics solutions that drive data-driven decision-making within organizations.
Business Users
Business users who utilize analytics are professionals from various functional areas within an organization who rely on data-driven insights to make informed decisions and drive business performance. Here are some common examples of business users who use analytics:
C-suite,
sales representatives,
operations managers,
finance professionals,
business executives,
customers,
interns.
That’s a wide group of stakeholders who leverage analytics to uncover patterns, trends, and correlations in data that help them make informed decisions, optimize processes, identify opportunities, and achieve their organizational goals. They expect the data to be easy to understand, trusted, close to their daily tasks.
Ambassadors
Data ambassadors are individuals within an organization who act as advocates for data-driven decision-making and promote the value and importance of data within their respective teams or departments. They play a critical role in fostering a data-driven culture and encouraging the adoption of analytics practices throughout the organization. Here are some key characteristics and responsibilities of data ambassadors:
data advocacy,
training and education,
collaboration and support,
communication and visualization,
quality assurance.
Data ambassadors can come from various backgrounds, including data analytics, business intelligence, data governance, or even business roles with a strong affinity for data. Their primary objective is to drive data literacy, encourage data-driven thinking, and facilitate the effective use of data to achieve organizational goals.
Pilot Project — Self-Service Analytics for Ambassadors
In our expereince, typical scaling scenario implies conducting a pilot project. Often it starts with defining several data use cases which are not covered with dashboards and reports due to their ad-hoc nature or too specific for one team/division. Based on that, the development team builds a dataset which is meant to cover these use cases and it’s shared with Ambassadors. Ambassadors then use that data to produce business insights or request of their respective teams.
It does not usually include developing nontechnical aspects. And while the pilot project gives good results, their adoption by the business is less successful due to well-known barriers, mainly:
business resistance,
lack of management adoption,
fear about losing decision-making power,
alignment of the pilot with exisiting business strategies.
