Unlocking Success: Best Practices for Self-Serve Analytics to Empower Your Team and Drive Data Democratisation

First things first - Encourage a Data Driven Culture

Foster an environment where data is not just valued but actively used to make informed decisions at every level of your organisation. True data democratisation happens when users are empowered to make their own decisions, leading to exceptional customer experiences driven by data insights.

Pro Tip: Start by asking the crucial question: “Who is your data cheerleader in your organisation?.

Your products are only as good as the usage, so if you have built a brilliant dashboard with zero users there is something fundamentally wrong with the user adoption or change management process. So how do you tackle this?.

• Ensure the Right Tools are there: Select analytics tools that are intuitive and easy to use, with robust features that meet the needs of your organisation. Ensure business buy in and agreement to the adoption during the early phases.
• Change Management & User Training: Ensure that users are adequately trained to use the tools effectively. This can include formal training sessions, online tutorials, and ongoing support with user guides and FAQs. Often users come from an Excel background and might be new to data analytics or BI tools, so ensure they understand the differences clearly. This is much trickier with self-serve analytics as users are not looking at pre-built dashboards but in turn must pick and choose elements to build their own. Ensure they are well versed to produce various cuts of the end reports while ensuring how to use them needs to be well understood by user community.

Save those user guides and video demos for later reference especially as new stakeholders can benefit from this.

“Aim for DATA FLUENCY, not just DATA LITERACY.”

What is the difference?.

So data literacy is the starting state of any organisation . It is the foundational knowledge required to work with data efficiently and includes the ability to read, understand, and interpret data. It involves skills like reading charts, understanding dashboards, and interpreting data visualisations.

Data fluency goes a step further . It's about being able to use data to make decisions , solve problems and communicate insights effectively.This includes the ability to integrate data into daily tasks, create data-driven strategies, and explain data insights or use narratives with data.Data fluency is what makes your organisation stand out as you are leveraging the data to your competitive advantage.

In essence, while data literacy equips individuals with the ability to understand and interpret data, data fluency empowers them to leverage this understanding to make impactful decisions.

If you were to put it in terms of the Data-Information-Knowledge-Wisdom (DIKW) pyramid a data literate audience will touch upon "Data" and "Information" sections while a Data fluent audience will dwelve deep into the "Knowledge" and "Wisdom" sections of the pyramid.

Now that you have the basic ground work done , what does this mean technically for the data teams?. Few of the key considerations while developing your self serve analytical products are below.

• Data Governance: Implement governance policies to ensure data quality and security, while also monitoring usage to identify areas for improvement.
• Data Classification : Ensure data is classified and protected to avoid sensitive information from being shared across to unintended recipients. Al data should be classified accurately ensuring PII or commercially sensitive information is protected and encrypted. (Think GDPR, GSCOP, HIPAA etc.)
• Audits: Ensure the users with access to self-serve analytics tools are aware of these policies and that the usage is also audited from time to time. Auditing is also an excellent way to ensure your organisation is not spending millions in licensing costs for inactive users.
• Capture Usage Statistics: Track usage statistics from BI tools to understand ad hoc data usage, restrict Export privileges or inbuilt sharing features unless otherwise signed off.
• Data Access Controls: Remove unwanted access from time to time to ensure you are always compliant eliminating potential risk factors. If they have not used it for a good 90 days, its ideal to review the usage and recoup the license. Equally ensure users who have left the organisation no longer have access to sensitive data.
• Data Dictionary & Data Stewardship: Ensure a single source of truth with a proper Data Dictionary that is accessible to everyone across the organisation. All KPIs should be clearly defined in the data catalogue or a data dictionary which is easily accessible to everyone removing ambiguity. It’s always best to establish clear ownership of KPIs in the organisation to avoid ambiguity (e.g.: Profit definitions should be owned by Finance and not by HR department). Ensure ownership and assign Data stewards for data products with a support model that will provide dedicated support to users.
• Data Lineage: ?Ensure your organisation has a good data tracking mechanism from source to destination so that the tech teams and users can understand the data flow quite easily and clearly. This is especially useful for troubleshooting, as it allows you to view both upstream and downstream systems to uncover relevant data context, such as source changes and usage patterns. This capability enables you to trace issues back to their origin. Hopefully your Post Incident Reviews will be much simpler once this is implemented.
• Enable AI: This should perhaps have been the single most important point to mention , however a lot of tools who have AI embedded are not as effective today especially due to the underlying data quality issues. But if you have recent reliable data then enable insights and narratives via AI or your AutoBots to go that extra mile!.

Incorporating AI with high-quality data not only enhances the accuracy of insights but also streamlines processes, allowing for more strategic decision-making. Embrace AI and automation to transform your data into actionable intelligence and achieve greater efficiency and effectiveness in your endeavours.

Hope this helps in your journey towards Data Democratisation!