Data Science – Make or Buy?
Companies are increasingly turning to data science and data analytics solutions to leverage the sea of data for their own business.
The data science project is defined. The proof of concept has been successfully implemented. And now? Unfortunately, this is often the end of the story. In fact, one of the greatest challenges lies in the operationalization of data science projects, i.e., in their successful transfer to productive business operations.
According to a study by Gartner, more than half of all data science projects are not fully operationalized. However, this step is the key to using data profitably in the long term and to achieving the actual added value of Data Science projects. To prevent you from falling into the same trap in the future, we would like to use this article to show you what operationalization is all about, highlight the differences between it and a Data Science project, and use the Data Science operationalization cycle to show you what to look out for and which questions need to be clarified in advance.
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Definition: The operationalization of Data Science projects describes the permanent integration of Data Science or analysis results into the IT infrastructure and operational business processes of a company. Operationalization thus refers to the continuous delivery of Data Science solutions to the end user, or as we say at pacemaker, the journey from data project to data product.
To get a better understanding of what data products can look like, here are a few examples from our own practice.
Forecasting: Development of a forecasting software for a logistics service provider for the online trade of a large fashion retailer to predict orders and returns and thus optimally plan e-commerce logistics.
P&C order and returns forecasting ➞
Product Insights: Development of an interactive dashboard for continuous evaluation of customer feedback and device data for a 360° view of product and service quality for a manufacturer and distributor of household electronics.
Dynamic Route Optimization: Development of dynamic route planning software for a pharmaceutical logistics company to save time and costs through optimal route planning and to ensure faster supply to hospitals and patients.
Data products can therefore be very different and create real added value in diverse application areas. It is important to note that it is not always a matter of transferring all use cases into a data product. Some data science projects, for example, only serve as a one-time basis for decision-making and do not require a permanently operated data product.
A Data Science project and an operationalization project are actually two different things with different requirements and goals. A fact that many end users are often not aware of. But what exactly are the differences between the two?
While the focus of a Data Science project is mostly on the feasibility testing of certain use cases, the so-called proof of concept (POC), and the development of analysis models is in the foreground, operationalization is about developing Data Science software solutions that permanently integrate the analysis results into everyday business. The operationalization process therefore picks up where the Data Science project leaves off. At this point, a successful Data Science project becomes a software development project. The goal is to develop a software solution that meets the requirements of everyday business. It is therefore necessary to check whether the assumptions made in the POC also apply in the productive environment and using constantly updated data. This is also referred to as proof of scale (POS).
The diagram below provides an overview of the main differences between the underlying questions that need to be examined in the context of the two projects.
Comparison of the questions in the two test phases (source: own representation based on Gartner, 2020, Follow 4 Data Science Best Practices to Achieve Project Success).
Similar to the CRISP-DM approach for Data Science projects, successful implementation of data products also requires a systematic approach. In fact, the lack of a systematic operationalization methodology is one of the main failure reasons for successful productization, according to Gartner.
The operationalization process is a continuous cycle. The graphic below provides an overview of the entire process. The individual steps are briefly explained in more detail below.
The operationalization cycle as a structured process for implementing data products (source: own representation based on Gartner, 2018, How to Operationalize Machine Learning and Data Science Projects).
If this systematic process is followed, a major hurdle of operationalization is overcome. Nevertheless, productification is and remains a complex process with very different challenges, both technical and organizational. In conclusion, we would therefore like to take a look at the main reasons responsible for failure.
Although the reasons may vary, the following crystallize as decisive:
To ensure that you don't fall into the same traps and that the implementation of your data product is successful, there are a few questions that should be addressed in advance. These include the following:
Successful implementation requires the interaction of an interdisciplinary team. In addition to the business departments, a representative of the IT department should be involved on the company side, because he or she knows the IT infrastructure into which the data product is to be integrated better than anyone else.
The key to using data profitably in the long term and achieving the actual added value of data science projects lies in operationalization. If this step is successful, productivity can be increased, costs can be reduced, revenues can be increased, and ultimately profits can be increased. Therefore, it is important to select use cases at the beginning of a Data Science project with regard to their producibility and then to establish a systematic procedure to transfer them into permanent use. In this way, you will not only profit from a higher ROI of your Data Science initiatives, but also increase the acceptance of these topics in your own company.
As data science software experts, we at pacemaker are your contact for the realization of your data project and the development of your individual data product. We accompany you from the idea to the seamless integration into your IT infrastructure and operational business processes.
Contact us! To the project inquiry.
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