In recent years data science has accelerated rapidly as a discipline and found its place within organisations as data has become more integral to business strategy and operations. At the same time, business intelligence departments have grown and adapted as parts of the business seek deeper and more timely insights to inform their decision making.
However, data science and business intelligence teams typically operate independently with different approaches, tools and working methods. This has led to a degree of separation between teams that ultimately seek to address the same challenges and would benefit from shared data repositories and greater collaboration and alignment. However, we are starting to see the two data professions converge as organisations integrate their teams and the tools they use to become more effective.
Getting ahead in the field of Data Science
The very nature of data science and machine learning means that it’s a field that is constantly evolving and innovating, so it’s essential to stay up-to-date with the latest technologies and developments
Data scientists and BI teams learn to get on
One opportunity to accelerate the process of converging business intelligence and data science is to implement a central data repository so data scientists can leave their laptops behind and apply statistical models to the same ‘single point of truth’ as BI teams.
The hurdle has lied in the language they speak – BI is predominantly SQL, and data science employs libraries in languages such as Python, R, or Java, to perform more complex operations and employ artificial intelligence. It’s now possible for the data science team to run scripts directly on the data simultaneously, enabling all teams to work with the same live data and historical archives.
There are more immediate benefits beyond the promise of improved collaboration and access to data within the organisation: With a data-science compatible repository, C-level managers and frontline staff can access visual BI tools that exploit complex data science algorithms under the hood.
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Reports on tap
Gartner predicts that by 2019 more data analyses will be performed by self-service than by data scientists. I don’t think we are there yet, but times are changing quickly. The next evolution will be for BI reporting to become self-service, empowering all employees with the latest up-to-date metrics that are relevant to their job.
This is made possible by ever-more powerful and intuitive business intelligence tools sitting upon high-performance analytic databases – databases that can even cope with the Monday-morning workload without a hint of slowdown.
Digital banking start-up Revolut is a case in point. The forward-thinking business aims to make every decision data-driven, but having surpassed 3 million customers, and with global expansion planned, it was struggling to make the data available to its employees.
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Revolut employed a high-performance analytic database in the cloud to act as a central repository for all corporate data, then provided every employee with a BI tool and self-service access to the corporate data they need to carry out their work. Now anyone in the business is able to generate a report with actionable insights in a matter of a second – whenever they need it. By democratising its data in this way, teams are able to improve decision making and understand their performance against their key performance indicators (KPIs) at any given time.
Organisations will be looking to follow Revolut’s blueprint for self-service as they realise the benefits of democratising their data and helping their employees to become data native. Furthermore, those businesses that are able to bring their data science and BI teams closer together over the course of 2019 will have upper hand in their market as they dig deeper to understand complex relationships and reap the benefits of automation.
Written by Mathias Golombek, CTO at Exasol