Today I give you an introduction to self-service analytics. Debunk common risks and doubts. Follow the 6 pillars of a successful self-service analytics strategy. Make your digital journey more effective.
What is self service analytics?
Let’s have a clear definition: self-service analytics is a space for data exploration and ad hoc reporting by business users.
The level of complexity correlates with the level of data literacy in the organization (technical skills + IT support). Each of business groups need other type of BI/big data platforms. Data scientists, reporting analysts, or senior managers ask questions on different granularity level.
Self-service analytics relies on teamwork of IT and business. They must choose the right data and right access for the right decision-making.
Self-service analytics pros
- versatile what-if scenarios run across an organization
- fostering data culture
- empowering knowledge workers
- better informed decisions for senior managers
Self-service analytics cons
- a risk of misunderstanding the data behind
- risk of missing control and data security
- implementing data culture processes
- required training for power users
Why is self-service analytics important?
Data analytics skills put into business acumen build a competitive advantage for decision making.
How to enable self-service analytics and business intelligence?
1. Data governance
Before enabling self-service analytics, design top-notch data governance. IT team supports the frictionless process of data analytics. They must ensure the proper level of data security and quality roofed level of details.
2. Design of the validation process
Data culture is the first thing. Include well-structured processes that validate ad hoc reports accuracy.
Put data governance in the centre of all your processes. High-quality data make data culture stronger. People are up to involve in self-service analytics when they trust the data.
Set up a BI team that will check up your power users’ ad hoc reports.
3. Tailoring data sources
Data quality and level of details should be sliced and diced for end-users. Don’t give raw data that brings tons of questions about the aggregation and definition.
Datasets must be ready to reuse.
Provide a knowledge base that keeps all the information about the dataset:
- data dictionary
- fields definition (KPIs,
- connection type (live vs import)
- dataset owner/data steward
- join clauses definition
- data flow/data model architecture
- brief description of the purpose of the data source
- how to get the access to the dataset
4. Right choice of the self-service analytics tool
Choose the right tool that corresponds to the level of needs and technical expertise of your employees. Data exploration multiplied by a number of analysts gives you thousands of meaningful business questions. That’s the beauty of self-service analytics!
5. Listening to your self-service power users
Ask them constantly for feedback on how they like using your self-service analytics platform.
Design a process to collect feedback and enhance areas of improvement.
Apart from asking how they like the current tool, ask open questions:
- What do you expect from the self-service analytics platform?
- What answers do you look for in your data?
- What do you want to check by using self-service analytics?
It can turn out they need kinds of tools for
- data mining
- data visualization
- data storytelling
- reporting and collaboration
Thanks to the constant feedback, you quickly react to your business needs. As a result, you empower your employees and grow your business.
6. Supporting the learning process of the self-service analytics
Data literacy among senior managers fosters strategic decision making. Provide clear and practical training for them. Go through primary use cases. The essence is the ability to answer business ad hoc queries.
Set up 1 hour per week with developers “open hour” with experts. They will teach people individually and resolve their struggles.
Do the users ask the same questions about self-service analytics? Create a FAQ list, ideally, with video tutorials.
More info in a book: “Self-Service Analytics” by Sandra Swanson.
Self-service analytics is an introduction to data storytelling
After ticking all of the above bullet points, move to the next level: data storytelling in your organization. A mature data literacy evolves to storytelling. Stories evoke emotions. Numbers build trust. Data storytelling is a way to communicate in a world of information.
Join me on Clubhouse to discuss successful self-service analytics examples. We talk about all things data including data visualization, data storytelling, BI and AI.
Talk to you soon!
