Finance and Accounting

The rise of self-service analytics: Empowering business users

Business intelligence has been the backbone of modern business operations to empower data-driven decisions, relying on data analytics capabilities to monitor performance, track market trends, and identify potential improvement opportunities. But to grow in today’s data economy, businesses have to go beyond simple enterprise reporting and focus on building a data-driven company culture – making high-value insights accessible to every team member.

This is where self-service analytics comes in, where self-service business intelligence has been empowering employees at every level to access and analyse data to support data-driven decision-making. As a result, organisations leveraging self-service analytics tools have been able to incorporate empowerment, agility, and collaboration in their company culture.

Understanding self-service analytics

Self-service analytics refers to the ability of an individual employee to analyse data and extract valuable insights without support from IT, business intelligence, or data science specialists. This allows the end-user to access data-driven insights without specialised technical skills. It can be a game-changer in today’s dynamic world, where data-driven real-time decisions are crucial to be agile. From operations and accounting to marketing and sales, self-service analytics tools are useful everywhere, and failing to embrace self-service business intelligence is often a surefire way to lose your competitive advantage.

Benefits of self-service analytics

Self-service analytics offer many advantages to modern businesses and end-users. They allow you to go beyond just enterprise reporting, free up your analytics team to focus on core business issues, and empower every individual employee and department with:

Improved efficiency

Individual end-users can quickly access the necessary data, get the desired answers, and generate reports without having to wait for a data analyst to complete the task.

Improved accuracy

Self-service analytics tools facilitate direct access to data, reducing the chances of errors when manually entering, downloading, or processing the data.

Greater customisation

With greater control over their data and the parameters they want to focus on, individual users can create custom reports and dashboards that meet the specific needs and goals of individual employees, teams, or departments.

Self-service analytics best practices

Self-service analytics tools have the potential to break down data barriers, eliminate delays and bottlenecks when accessing data analytics expertise, and empower real-time decision-making for every individual. But what can make it possible?

Here are some key best practices that can help you fully leverage the potential of self-service business intelligence:

Ensuring easy data access

The first step is to ensure all available data is easily accessible to relevant end-users. This includes having a centralised data repository where data is appropriately tagged and organised.

Ensuring a user-friendly interface

An easy, intuitive, and engaging user interface is crucial for self-service analytics. An easy-to-use and navigate interface can enable users to explore data effectively and customise their experience.

Encouraging collaboration

With many users gaining insights from the same data, encouraging collaboration is crucial to eliminate redundancies. This can include using tools like Google Sheets, Microsoft Teams, Slack, or SharePoint to share reports and circulate the insights widely.

Following best data governance practices

Quality data is the foundation of self-service analytics. So, focusing on data governance best practices for a well-managed, controlled, and centralised data repository is crucial.

Creating effective feedback loops

Continuous improvement is an integral element of self-service analytics tools. This means you must welcome user feedback, address issues, make continuous improvements, and track progress in real time.

Enabling the future of self-service analytics

Self-service analytics has come a long way from complex tools and limited analytics capabilities in the past to advanced analytics tools, cloud computing, big data technologies, and real-time analytics tools of the present. But to truly progress towards the future of self-service analytics, with AI data applications, large language models, machine learning predications, and natural language processing, we must:

  • Make trust among non-technical business users a priority for reliable analytics
  • Empower data governance and data discovery to access greater insights
  • Leverage data catalogues to consumerise data discovery and analytics

To achieve this, modern businesses must focus on capabilities – such as business modelling, collaboration, machine learning, trust, and consumability – that can help organisations adapt and leverage more self-service analytics tools and expand their analytical capabilities.

How can Infosys BPM help?

Leveraging the available data and making the right decisions in real time is crucial to stay agile and competitive in today's dynamic market. Infosys BPM enterprise reporting services bpo and self-service business intelligence services can help you centralise your data sources, reduce reporting and planning efforts, and offer timely qualitative business insights to relevant stakeholders. Experience a 30-45% improvement in efficiency, accelerate reporting timelines, and achieve a superior stakeholder experience with Infosys BPM next-gen ERAP.

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