ITD: Mr. Krause, in the past, classic business intelligence software was very complex and in some cases could only be run by IT professionals. What are the advantages of self-service business intelligence solutions in companies?
Michael Krause: The pandemic has accelerated digital transformation. Even skeptical companies have moved to the cloud and sought to become data-driven. The pace of change and the number of questions business users want to answer with analytics is unprecedented. However, one obstacle is that the team of analysts is too small to respond to the requests of business users in a timely manner. Often the requested reports are already out of date when users receive them. The current skill shortage combined with the growing volume of data shows that there is only one solution to this situation: enabling business users to extract their own insights from data through self-service analytics. Equipped with technologies such as research, artificial intelligence or NLP, modern self-service analysis tools are easy to use even for non-technical users. The benefits of self-service analytics go beyond quick and accurate data insights. According to a 2020 Harvard Business Review survey, 36 percent of leaders report higher levels of engagement and satisfaction when employees are empowered to make data-driven decisions.
ITD: How do companies get the most out of their data? Which cloud data infrastructure is best suited here?
hem: The best infrastructure for getting the most out of cloud data is the modern data stack. It can be used to develop the best approach to each component of the infrastructure that is optimally designed for relevant business requirements. The modern dataset includes: Cloud-based data platforms such as Snowflake, Amazon Redshift, or Google BigQuery for data storage. Data ingestion tools like Fivetran or Stitch that automate the ingestion of data from various sources in a cloud data warehouse. Conversion tools like dbt or Supergrain that convert uploaded data. Cloud-based analysis tools that connect directly to the cloud data platform and allow analysis to be performed directly on the platform. As mentioned earlier, technologies such as artificial intelligence, machine learning or research help find the most important data and quickly identify changes. Otherwise, important insights will remain hidden in the mountains of data in the cloud data warehouse.
ITD: Why are open standards and tools so important here?
hem: In the world of traditional workplace software, it sometimes makes sense to be limited to a single provider due to difficult integration requirements. Fortunately, in the world of SaaS, this complexity fades away because it is all about openness and APIs, which makes it a lot easier to integrate the best technologies and get the best of both worlds: maximum enterprise value and easy integration. One of the most important characteristics of every component of the modern data stack is that they are interoperable and open. This enables companies to integrate and use technologies as required.
ITD: How can you empower your employees for self-service BI?
hem: Technologies such as artificial intelligence and research make it easy for non-technical users to gain insights into data themselves. But technology is only one piece of the puzzle. People, processes and company culture are other important components. Building a truly data-driven culture requires empowering employees not only with self-service tools, but also with the skills and authority to act on the insights they generate. This requires a change management approach and the adoption of new processes, including company-wide data literacy programmes. This should go hand in hand with self-service analytics so that business users can understand, interpret, think critically and act on data.
This is an article from our print edition 7-8 / 2022. Request a free trial.
IT: What role will the data team play in the future?
hem: Even with self-service analytics, data teams will play an important role in a data-driven business. But instead of creating dashboards and reports, their duties will focus on four areas. The first is training and upskilling, which is teaching business users how to use data effectively. This includes, for example, providing resources and services as well as promoting data literacy. Second, work with data strategically, collaborating with business users on how to use data to solve problems or model high-priority business cases. Third, there is still technical engineering work behind the scenes, which ensures, among other things, that cloud data can flow into production. And fourth, it’s about transformation, where data knowledge is expanded into new areas such as predictive analytics or machine learning.
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