Weighing up guided vs self-service data analytics

Weighing up guided vs self-service data analytics

Making data both available and useful remains imperative to gaining real, tangible value from business intelligence. Yet still, it remains a great challenge for many organisations. As such, adopting systems that enhance the integrity, efficiency, reliability and analytic capability of data pipelines can help business leaders make more insightful decisions. 

But how do you know which analytics approach is actually the best for your business – should you choose a guided data analytics solution, or self-service data analytics solution? To help you determine which approach is right for your business, we describe the approaches and take a look at the pros, cons and use cases for each of your options.

What is guided analytics?

A guided analytics solution is created by a developer, data scientist, or technology vendor and is based on a particular business requirement. It works best if these business requirements are very specific and well-defined. As the name suggests, guided analytics uses capabilities that guide the user’s trajectory as they explore structured dashboards, charts and reports. Deploying guided analytics means that you’re essentially predicting or steering the route the user will take, assuming the questions they might ask along the way and the requirements they need to meet.

The pros:

  • With guided analytics, you don’t have to build a data analytics solution from scratch. This saves time for business users, helping them get to the solution more quickly.  
  • This approach helps keep data flowing. The input and guidance from developers will help determine the value end-users gain from the analytics.
  • Business users can navigate the data securely through a set of predefined metrics and principles that stem from a company’s data governance framework. This gives users assurance of the data’s integrity and reliability. 
  • Can often contain ‘pre-packaged’ analytics functionality with pre-configured analytics dashboarding and reports.

The cons:

  • Guided analytics can perpetuate an unfederated data environment on account of it being configured for a specific problem. This means it can be an inflexible analytics solution that cannot be scaled, customised or used iteratively as requirements change. 
  • This approach doesn’t empower users to generate their own insights – and may even limit the interpretability of the data – because guided analytics is firmly linear and query-based. 
  • Being a more traditional approach, guided analytics is not always interoperable with the latest tech solutions that help to integrate diverse data systems. So, while guided analytics provides insights quickly when new, bespoke requirements inevitably surface in an organisation, guided analytics can be restrictive in building custom reports.

The use cases of guided data analytics

Guided analytics is useful for analysing trends and patterns without drilling down into more granular details or issues, and is functional for sales, finance, business operations and HR teams. It’s effective in spotting peaks and troughs in charts, which will lead users to find the answers to critical business questions as they work towards narrowing gaps, fostering more agile decision-making, driving down costs and boosting profitability.

What is self-service analytics?

Self-service analytics gives all data users the power to perform queries, ad hoc analytics, and create reports. Unlike guided analytics, this approach helps every user gain insight from the data themselves rather than from reports created by a data scientist. 

Self-service analytics accelerates teams and individuals to go straight to the ‘why’ behind the data, helping them to dig deeper and uncover perhaps previously concealed insights. This is a more ‘choose your own destiny’ approach, offering users choices based on preferences or evolving requirements and more flexibility in their data analysis. This means that with the right system, they have the autonomy to access, analyse and report on the data of their choosing and develop their own insights from there.

The pros:

  • Self-service analytics give all business users the flexibility to choose their own data analysis path without the need to understand data science and programming. 
  • The approach provides quick, seamless access to data to support agile decision-making without relying on data scientists or IT teams to generate reports. And insights can simply be shared among different departments that might find the business intelligence useful.  
  • Helps to integrate disparate data sources across organisations which breaks down silos to provide a single source of truth. 
  • Business users are also given the capability to drill down and uncover underlying root causes as they need to. This gives organisations more confidence in their data and the insights generated.

The cons:

  • Organisations need a robust data governance framework in place for self-service analytics to function optimally, including standardised data definitions and controls for permission, sharing and access. 
  • Giving users access to the data doesn’t always mean they have the skillset to work with it. If there is a problem that is particularly challenging, then the data concepts are likely to also be more complex, meaning the skillset must align with the capabilities of the self-service solution. 
  • Without basic data training, organisations run the risk of multiple users continuously generating reports that may present conflicting data and metrics, undermining data accuracy and consistency. 

The use cases of self-service data analytics

Self-service data analytics gives users the ability to rapidly access, analyse or process data. It’s a suitable solution for so many industries – from healthcare to marketing to manufacturing and logistics – as it can provide real-time data, improve visibility and generate a better understanding of consumer behaviours. It’s also particularly useful for the financial sector and internal audit functions because it helps to overcome the challenges created by disparate data systems and data silos. This is because it’s interoperable, can easily integrate with existing tech stacks, and is without the limitations associated with the data always being ‘provided’ through guided analytics.

Self-service analytics is preferable for many forward-thinking organisations as it aligns with business units that want more ownership and flexibility to create bespoke analytics. It’s also the approach of choice for companies operating in complex data landscapes with numerous siloed systems, where there are simply too many paths to consider for guided analytics. 

Business intelligence is indispensable to effective decision-making which is why users need to be able to access and analyse data through the right approach that meets their specific business needs. By providing greater propensity to action more meaningful, data-driven insights, the right analytics solution can quickly deliver value and generate ROI. To learn more about what analytics solution is best for your organisation, contact us today

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