Data industrialisation entails harnessing the power of data to drive informed decision-making at an enterprise level, in a systematic and organised manner. By leveraging insights derived from data, relevant stakeholders are empowered to make strategic choices that can yield significant business value and enhance competitiveness. In this article, we’ll delve deeper into the intricacies of data industrialisation and explore how it can revolutionise the way organisations operate.
Join us as we uncover the key principles, benefits, and challenges associated with this dynamic approach, and discover how it can pave the way for a more data-driven future.
Through agile collaboration, small teams can rapidly develop promising Data Science pilots. However, scaling such minimal viable products (MVPs) to enterprise level is a complex process with many challenges along the way. But, with the right approach and collaboration model, these challenges can be overcome – resulting in industrialised solutions that lead to significant business value.
It has never been easier for organisations to leverage their data by developing tailored software solutions. Backend, frontend and data analytics frameworks are readily available, integrated data platforms have advanced and matured significantly and cloud environments provide readily-available capabilities for analytics, software development and
deployment. Developing tailored solutions to meet organisation-specific challenges can lead to competitive advantages and internal efficiencies.
While typically developed in small teams, Data Science solutions can be scaled up for large amounts of users. In contrast to other activities that provide business value, scaling Data Science solutions relies mostly on technology and not on manual labour, which represents a significant advantage. The ability to develop and scale Data Science solutions for relevant stakeholders across an organisation has become a critical capability for every medium to large-scale enterprise.
The process can be broken down into two major steps:
Step 1: The development and field-testing of a Data Science pilot or MVP.
Step 2: The industrialisation and scaling of successful MVPs to relevant users across an organisation.
Either of these steps can be achieved with the help of third parties, however, the initial creation of critical IP (Step 1), often takes place in-house.
While the industrialisation of a Data Science pilot depends on the situation (based on factors such as relevant data, user-types, available technology, business goals, etc), the process itself shows recurring patterns and phases across use-cases, organisations, and even industries – as illustrated in the chart.
Before industrialisation: start of industrialisation
After a Data Science pilot has been developed and tested, the organisation can then decide whether to scale-up, industrialise and roll out the solution to relevant stakeholders. This decision typically depends on a number of relevant factor such as the expected business impact in light of the pilot’s field-test results, budgetary and capacity constraints, the degree of complexity added to IT systems and the required level of change management. Ideally, these topics have already been considered during the pilot development. In any case, a tested MVP allows a more accurate assessment which can then lead to an informed decision for – or against – industrialisation.
Read about the phases of alignment, setup and execution over in Philipp’s full article here:
https://issuu.com/datasciencetalent/docs/the_data_scientist_mag_issue_2_digital_copy_for_is/s/19459894
