Clearly data analytics can provide significant advantages for businesses and being able to provide access to relevant information when it’s needed to any worker, presents a lot of value. This is what Self Service Analytics is designed to do. According to a recent survey by The Data Warehouse Institute, improving trust in the data and empowering users with Self Service Analytics are the two most important steps in increasing an organisation’s success with analytics. Microsoft’s recent unveiling of Microsoft Fabric and Copilot in Microsoft Power BI is testament to this. Just arming frontline workers with business intelligence tools though isn’t enough. Here we’ll explore 6 key pillars for successful Self Service Analytics based on our experiences, highlighting best practices and tips to overcome key challenges associated with it.
Why Self Service Analytics Matters
The most sophisticated analysis is not always the best. Decision support is the most common use case for data and analytics, however the burden for adoption usually falls on the data and analytics team. Despite all the reported benefits for businesses associated with data analytics and business intelligence solutions, analytics hasn’t penetrated enterprises as much or as well as it could. Ventana research this year shows that for 75% of organisations, half or less of the workforce are using analytics, while Gartner goes on to say in their recent Analytics Consumerisation Democratisation Survey that this low analytics adoption rate is mainly due to complicated user experiences. If we believe that data and analytics can improve operations than this is concerning.
However, this is where Self Service Analytics can help. Self Service Analytics is designed to provide less reliance on IT for report requests delivering users insights without the wait. This allows users the ability to explore business data in a way that best suits them to uncover the insights they’re after. This type of approach also fosters higher data literacy across the organisation. In fact, Harvard Business Review research indicates that Self Service Analytics delivers higher levels of customer and employee engagement, top-line business growth and productivity gains.
Gartner defines Self Service Analytics as end users designing and deploying their own reports and analyses within an approved and supported architecture and tools portfolio. There are essentially three types of approaches to Self Service Analytics that organisations choose to deploy, these are either, a business-led approach, an enterprise approach or a managed approach. The characteristics of each are highlighted below:
6 Key Pillars For Enabling Self Service Analytics
For a Self Service Analytics approach to be successful we believe it needs to be grounded in the following six key pillars, each as important as each other.
1. Business Commitment
As with any data and analytics initiative, having strong commitment from the leadership team is essential. Do senior leaders ‘walk the talk’ when it comes to analytics and do they lead by example? One way to foster greater business and leadership commitment is to break up the Self Service Analytics project into smaller more manageable parts to prove value end-to-end in a specific, high priority business area. Understand the leader’s pain points and show how that problem can be addressed through Self Service Analytics by performing a Proof-Of-Concept, while also continually educating on the benefits of analytics and the downside of not taking action.
2. Single Version Of The Truth
IT teams need to design and architect a Self Service Analytics platform for the business that is performant and delivers a single version of the truth for corporate data. This ensures everyone has access to the same, consistent and reliable information for decision making. This should involve creating a central data repository for all data sources, this may be a datamart or a data warehouse depending on your organisation’s size, which eliminates data silos. It’s also important to ensure that the data housed in this central repository is a replica of the source data via constant validation and reconciliation checks. Working in conjunction with this should be robust data governance practices. Which brings us to the next pillar, data governance.
3. Data Governance
Data governance defines how data is accessed and treated within a broader data management strategy and relates to the way data is managed and protected as an asset across the organisation. Underpinning the entire Self Service Analytics architecture should be a strong data governance layer. This is the part many businesses struggle with or need assistance from a partner, like BoomData. This includes governance processes, security and compliance, data quality controls, metadata management and data usage audits. This ensures that the right people have access to the right data and data-driven insights come from a single source of truth. Establish clear roles and responsibilities, for example, data stewards are responsible to manage the data assets and act as a point of assistance for users.
Data governance dashboards should be used to provide a birds-eye view of all your Self Service users and reports. For those using the Microsoft suite, this includes tools like Power BI Purview or for more detail, Power BI Sentinel, to manage enterprise data assets. Power BI Sentinel goes beyond Microsoft’s Purview providing governance, auditing and disaster recovery enhancing your Power BI estate with backups, documentation, change tracking and data lineage, essentially bridging the gap between compliance and Self Service reporting. If you’d like to know more about either contact us.
4. Collaboration & Sharing
Self Service Analytics inherently fosters teamwork and knowledge sharing, promoting data-driven discussions. In fact, Gartner in its Top Trends in Data & Analytics 2023, highlights data sharing as a key priority for increasing efficiency and generating value in organisations as sharing data accelerates problem solving and facilitates data quality improvement.
One way to improve collaboration and sharing across the business is to consider adopting a ‘data fabric’ to enable a single architecture for sharing across heterogeneous internal and external data sources, as this will allow sharing at scale. Microsoft have just announced Microsoft Fabric which is in preview. This is an end-to-end analytics product that brings together all an organisation’s data and analytics in the one place. It combines Microsoft’s Power BI (for dashboards, reporting and business intelligence), Azure Synapse (data engineering, data warehouse, data science and real-time analytics) and Azure Data Factory (for data integration) into one unified SaaS platform.
It’s also important to identify data reusability opportunities in Self Service Analytics such as templates, workflows, visualisations and code. Ensure trust in the data via good data governance practices that make it easy to trace the source of data and help users find what they’re looking for, like data catalogues and active metadata insights.
5. Training & Support
Business and technical skills need to be assessed to determine a businesses readiness for Self Service Analytics and its data literacy. Plan and implement training and change management as part of this. Providing users with tutorials, documentation and online communities can help users develop data analysis skills and make the most of the available tools.
6. Self Service Analytics Ready Tools
The final pillar is about the Self Service Analytics platform that is chosen. A Self Service Analytics platform lets business users freely explore and analyse their data with easy-to-use, interactive visualisations, charts and tables. The best Self Service technologies also let users create and share their own analytics apps, dashboards and reports while offering a library of pre-built analytics assets. Power BI is recognised as a Leader by Gartner known for its effectiveness in Self Service Analytics for several reasons. Its combination of drag-and-drop ease of use, extensive connectivity options, advanced analytics capabilities, interactive pre-built visualisations, collaboration features publishing to Power BI Service, SharePoint or Teams, and seamless integration with the Microsoft ecosystem now even easier with Microsoft Fabric. Power BI’s recent announcement introducing Copilot for Power BI, which is now in private preview, provides automated insight generation, conversational experiences, and dynamic data stories using natural language AI.
Whatever your level of sophistication with analytics, the traditional approach of generating reports and pushing them out to users has evolved with many businesses looking to Self Service Analytics to provide business advantages. If your business is looking to make the move into Self Service Analytics BoomData can help, alternatively if you’re currently using Power BI but not sure if it’s optimised for Self Service Analytics, BoomData can provide a Power BI Audit where we review your Power BI instances and assess their effectiveness within your organisation against our proven framework.
