Advance Your Data-Driven Programs with Self-Service Analytics

Data-driven decision-making is crucial for organizational success in the current business climate of heightened competition and constant change. Giving business users access to insights at the right time is a top business priority, but it is not easy to do this cost-effectively and on a large scale.

Self-service analytics has been promising in this regard. Many businesses have focused on this in recent years, with mixed results. The main reason why self-service initiatives have failed in the past is because of the lack of a unified strategy and incorrect implementation. Having a clear plan and approach can increase adoption rates and advance your data-driven programs.

InfoCepts’ various self-service powered solutions and industry innovations have helped clients realize the full potential of their data for faster insights and smarter decisions. . In this blog, we share some self-service implementation tips for success.

1. Clearly define what self-service analytics means to your organization. 

The term self-service analytics can be interpreted in various ways. And it becomes difficult to set goals and expectations without a clear definition. This is why it is important to think about what, why, and context of your initiative before you start.

• What is the purpose of self-service? Self-service analytics aims to make it easier for users to do what they need — obtaining performance data reports, exploring trends, or data mashing — to facilitate their data-to-decisions journey. 

• Why is self-service needed? Ultimately, it is about making your analytics ecosystem more productive — shortening decision times, optimizing IT resources, and providing company users direct access to data, so they can generate their own insights.

• Context setting in important - The context of your self-service initiative is important and oftentimes overlooked. Is it consumer insights or for developers? Will it be used by just one person or teams of people? Aside from thinking about users, it is essential to think about making business units within your organization self-sufficient. After all, by 20241, 80 percent of digitally advanced businesses will replace their IT-centric approach with self-service, according to CIO.

2. Assess your current self-service abilities and progress to determine your next steps. 

InfoCepts’ Self-Service Continuum model below enables you to quickly understand your organization's current state regarding readiness and maturity, as well as envision its future path.

The transformation is a graduated process. The level of sophistication and agility increases as organizations move from the left to the right. Expectations are initially more IT-driven, but the later stages are typically more business-led and collaborative, giving users access to new analytics tools like machine learning and cognitive interactions.

It is critical to evaluate your current position on the continuum and where you want to be in the future. In our experience, organizations that take a step-by-step approach to improve their data sophistication and agility tend to get more value from data than those that do not. As they begin seeing success, these organizations put even more effort into progressing to the right of the self-service continuum. 

3. Understand your audience and their needs.

As you determine the direction of your self-service analytics journey, it is important to understand whom you are serving. Here is a straightforward way to classify different types of business users:

• Information consumers consume and engage with pre-defined reports to find answers to common questions.

• Business analysts are advanced users who generate and keep records of personalized views to answer recurring questions. They also combine local and commercial data sets.

• Citizen data scientists use self-service tools to build reusable objects. They manipulate reporting semantics for developing new applications.

• Data scientists analyze and extract insights from enterprise data assets to design, test, and deploy sophisticated use cases. They build and validate models based on a hypothesis and data.

For a self-serve solution to be useful, it must provide a wide range of capabilities that appeal to various users. Before taking your self-service capabilities further, make sure that you understand your end-user community’s profile, data maturity, key goals, and user personas. Use this information to shape your self-service strategies moving forward.

4. Formulate an approach for scaling up your self-service capabilities. 

There is more to self-service analytics than just interactive data visualization tools. Enterprise scale necessitates alignment, awareness, acceleration, and agility. It also requires considerably more cooperation between IT, users, and analysts. More importantly, it requires a fundamental change in perspective: from a one-size-fits-all approach to purpose-built solutions, from technology orientation to business orientation, from fragmented to collaborative governance, and from unsustainable to sustainable operations.

• Employing a single tool to build a general self-service platform does not work anymore. Consider your user personas and use cases when developing your analytic abilities. 

• Having a business-oriented mindset is key when it comes to self-service analytics. Seeing it as either a technological problem or a business opportunity only hinders development.

• Self-service analytics is a collaborative effort between IT and business. All stakeholders should be equally involved. 

• Finally, be sure to think about long-term sustainability when planning your self-service strategy. This means having a plan for roles and support structures on both business and IT teams. Once implemented, the program will require constant modification to ensure that it is effective and sustainable.

5. Implement a holistic rollout plan.

Our E-2-E Self-Service Analytics Implementation Model can assist you in defining and implementing an effective self-service analytics plan. This process-centric framework breaks down silos and allows stakeholders to operate as a unit.

• The Experience component focuses on identifying and prioritizing use cases by personalities, as well as applying design thinking and lean startup techniques to evaluate key analytics.

• The Education component stresses the need for leadership alignment, a dynamic (changeable) learning model depending on user capabilities, and a blended learning and onboarding method for users.

• The Engagement component prioritizes shared responsibility to maintain data quality and user trust. 

• The Enablers component shortens the transformation from raw data to actionable insights. To make this possible, it is essential to thoroughly comprehend upcoming obstacles and be aware of how to take care of possible setbacks. 

These five tips can give you a good start on your self-service journey. Do keep in mind that self-service analytics is an ongoing journey. Take time to reflect on your priorities and adjust your strategy as necessary. For more details, grab a copy of our practical guide to implementing self-service analytics in any organization.