Self-Service Data Preparation: Research to Practice

It is widely accepted that the majority of time in any data analysis project is devoted to preparing the data . In 2012, noted data science leader DJ Patil put the fraction of time spent on data preparation at 80%, based on informal discussions in his team at LinkedIn [28]. Analysts we interviewed in an academic study around the same time put the percent time “munging” or “wrangling” data at “greater than half” . Judging from these user stories, the inefficiency of data preparation is the single biggest problem in data analytics. The database community does have a tradition of research on related topics. Most common is algorithmic work (covered in various surveys, e.g. [15, 4, 3, 26]) that focuses on automating certain aspects of data integration and cleaning, or that uses humans as passive computational elements via crowdsourcing. What computer science researchers often miss are the skills realities in real-world organizations. In practice, the primary bottleneck is not the quality of the inner-loop algorithms, but rather the lack of technology enabling domain experts to perform end-to-end data preparation without programming experience. Fully preparing a dataset requires an iterated cycle of input data quality assessment, transform specification, and output quality assessment—all in service of a larger “preparedness” goal that tends to shift fluidly as the work reveals additional properties of the data. Traditionally there has been a divide between the people who know the data and use case best, and the people who have the skills to prepare data using traditional programmatic approaches. This results in the data preparation cycle being split across parties and across time: domain experts try to express their desired outcomes for prepared data to developers or IT professionals, who in turn try to satisfy the needs. A single iteration of this cycle can take from hours to weeks in a typical organization, and rarely produces a satisfying outcome: typically either the end-user did not specify their desires well, or the developer did not achieve the desired outcome. Neither tends to enjoy the experience. In short, the primary problem in data preparation is self-service: we need to enable the people who know the data best to prepare it themselves. Research focused on these user-facing concerns is scattered across the fields of databases, HCI and programming languages (e.g., [7, 29, 13, 24, 17, 9, 16]). While under-investigated in the research community, the topic has become an important force in the industry. In 2015, industry analysts began publishing rankings in an emerging new market category dubbed “Self-Service” or “End-User” Data Preparation [5, 1]. Two years later, the established analyst firm Forrester did a first annual Forrester Wave report on Data Preparation [23]: a mark of arrival for this category. Another major analyst firm, Gartner, has weighed in with various reports on the Data Preparation market (e.g. [35, 8]). Meanwhile, in 2017 Google Cloud Platform was the first cloud provider to launch Self-Service Data Preparation as a native service in their cloud [27], while Azure and Amazon Web Services also announced partnerships with data preparation vendors in 2018. Market size estimates for Data Preparation start at $1 billion [35] and go well upwards depending on the analyst and projected time horizon. Not bad for a technology that was seeded from academic research, and did not even have a name four years ago.