Self-service has become a buzzword in the world of analytics. But buzzwords don't appear by chance. If the promise of self-service is so popular today, it's because it could meet real needs that are increasingly being felt in the business world. To find out more, we've put together an ebook which you can download here.
Self-service in analytics can remove a bottleneck
Without self-service, only experts can manipulate data and use it to answer business questions. This is the role of data analysts: to meet business needs by conducting in-depth analyses, then getting relevant figures out into everyone's hands, ideally in the form of easy-to-understand, actionable visualizations. Data analysts are able to do this work thanks to the contribution of data engineers, who process the data, clean it up and set up the company's data architecture by building secure "pipelines" through which the data can circulate.
The problem is that the time and availability of these experts are not unlimited. They can't immediately respond to all the questions their colleagues ask them. And these questions are often urgent: business evolves rapidly. To be effective, answers must be recent, up-to-date and relevant. Companies try to solve this problem by providing all departments with dashboards that are updated in real time. But often, these dashboards are not perfectly adapted to the needs of business users. They then turn directly to the experts, which creates three problems:
- Experts are slow to respond, creating a bottleneck that slows down business operations.
- All this keeps the data experts busy, leaving them with no bandwidth to carry out more in-depth analyses and answer structural questions.
- Business needs are not always well understood by technical profiles: to compensate for this, specialized profiles can be called in to "translate" business needs for data experts, creating an additional bottleneck.
Self-service: a must for SaaS and B2B software customers
These are problems for all companies. But they are even more acute for software publishers. The market has set the bar very high in terms of data: SaaS software users all expect direct access to their usage data.This applies particularly to B2B software. When customers are companies, it's even more important to provide them with the data they need to manage their operations. B2B software publishers therefore need to integrate an analytics brick into their platform to satisfy their customers and their customers' customers. Sometimes, they even need to make data available to their suppliers or partners.
So it's in their interest to make them self-sufficient. The bottleneck problem is even more severe in these cases: without a self-service solution, their own product builders (the experts in charge of creating the product) have to take the time to respond to requests from external users. Let's take the example of personnel management software for the hospitality industry. The data visualizations and KPIs integrated into the platform by default may not be the most relevant for each use case. Product builders must therefore intervene on a case-by-case basis to deliver the necessary information.
IA is breathing new life into self-service
Why now? The rise of artificial intelligence is breathing new life into self-service. And it's showing in software vendors' marketing communications. In recent years, the general public has seen first-hand the effectiveness of generative AI models. If they can create images, text or code from scratch, what can they do in terms of data analytics? We've already explored the potential of AI in a previous ebook. In short: experts expect models to be a timesaver. But for non-technical profiles, it can be the means to directly access and manipulate data, even without training or specialization in the field. So, is AI finally making the dream of self-service a reality?
To discover the benefits and risks of self-service, we highly recommend our ebook, downloadable here.
