How Data Thinking will improve your data collaboration
Aktualisiert: 19. Okt. 2021
Data has changed the world. Even if there are some "the good old days" types out there, for most of us, data offers great opportunities. Let it be to solve the same problems more effectively or address new problems long believed invincible. Many companies yet fail miserably in their data initiatives. Some projects fade out in silence after yielding little success. Other project teams embark on a long rocky road to get supposedly simple things done. The demise rarely comes from a lack of technological infrastructure or financial commitment. Rather, it is about how people embrace technology and data and embed it into their communication processes during a data initiative.
McAfee et al. (2012) already argued long ago that data challenges are related to people and how they interact with technology . In enterprise settings, counterproductive interactions come into the picture when Business and IT collaborate on data-driven projects. In many organizations, one gets the impression that both IT and the various functional departments represent independently evolving microorganisms. Different, often incompatible working methods, language, and skills have evolved over time and coin long-established ways of working that suffice each team internally but provoke a great deal of tension when Business and IT departments leave their comfort zones to work together. However, it is precisely this cross-departmental collaboration that makes data projects successful and leads to the competitive advantage that everyone dreams of. In fact, 66% of the respondents to a study confirm that cooperation between IT and the business unit promotes value-creating IT solutions .
At detective, we bring together people from business and IT departments and ensure a good basis for communication without undermining individual working methods, skills and believes when it comes to enterprise data. We are convinced that the right method can improve existing communication rituals or just be a great way to help you get started. Exaggerated expectations that are formed unilaterally by business departments without the involvement of IT or vice versa often lead to much tension in advance, which is why it is important to seek strong collaboration from the outset and not only when the project plan and milestones are already in place. Many people don't relate to data or machine learning methods on a daily basis. They often lack the imagination of what works and what doesn't. This is where Data Thinking can help.
What is Data Thinking?
Data Thinking is a workshop method that involves the workshop team picking a use case, discussing all the necessary conditions and building a prototype that can be tested at the end of the workshop. The methodology is strongly inspired by a Design Sprint, which was invented by Google Venture's Jake Knapp and team. Data Thinking applies a similar concept to the world of data. Data projects often come with idiosyncratic challenges, so they need their own methodology to quickly identify use cases and build and test prototypes within a week before money and time are burned.
Duration: 1 Week
Goal: Identify a Use Case and test it, without wasting money or time
How Data Thinking improves Data Collaboration
Data thinking is about focus. It's about getting all the necessary stakeholders from IT and business together and thinking through a use case from both perspectives before moving into complex implementation and whirls of details. The first step is to build up a rough understanding of what constitutes good data, what methodologies are available and what pitfalls and traps lure around the corner when putting your data to work. In the second step, use cases are collected and prioritized according to their usefulness and added value. At this point, there are friction points between business and IT. Perspectives on what is expected and what is actually feasible often rub up against each other, revealing the pitfalls of speaking different languages and having distinct relationships with data. Yet, conflicting views are what it takes to build something great and Debating things out is a gift. It costs time and effort but once achieved we are much better off. The task: Building up a mutual understanding and bringing existing views into harmony. Subsequently, the selected use case is examined in detail. This includes internal and external data sources, stakeholders and touchpoints for the end user. For this purpose, the Data Thinking Canvas is used which you can download here. Finally, in the spirit of the Design Sprint, a prototype is built to test whether the idea is at all feasible and purposeful. The communication between business and IT is strengthened over the entire period, as they work together towards a single goal and remain permanently on the ball by focusing on the topic. Sticking together for such a week, builds trust and team spirit and sharing success at the end of the week is something awesome too.
Why detective users should try Data Thinking
At detective, we want to revolutionize the way IT and business collaborate on enterprise data. Our co-working, canvas-powered platform provides a sheltered environment for Business and IT employees to engage in meaningful conversations. No home field advantage. A spectrum of Design Thinking-inspired tools and easy-to-use data wrangling functionalities help users overcome the barriers of language and knowledge. Come as you are. No code big data access empowers users to set up and conduct data thinking workshops, from the convenience of their browsers. Having made your way onto the platform, a series of data thinking templates provides users an easy entry in the world of data thinking.
Data Thinking is a great methodology to get collaborating on enterprise data and make data initiatives more successful. A prototype certainly is only a part of the battle and it will take a lot more work to move ahead to a fully functioning product. Yet, it is a necessary milestone along the way. With the detective platform, we want to help enterprise users accomplish the collaboration- and communication-intensive phases of data-driven project in time, in quality and with ease. Want to learn more about the detective platform? Sign up to our newsletter and schedule a demo with us today.
 McAfee, A., and E. Brynjolfsson, “Big Data: The Management Revolution”, Harvard Business Review, 2012. https://hbr.org/2012/10/big-data-the-managementrevolution  Haberkorn et. al “Schnittstelle zwischen IT und Fachbereich“, Cassini Consulting 2021 https://www.cassini.de/inspire/cxo-studie