All data initiatives, whether machine learning, visualization or reporting, rely on clean data. Which means that data preparation is essential to any data-driven organization. Increasingly, organizations are adopting new solutions to increase the accessibility of data preparation (and reduce the time involved) in a governed, secure manner—no longer is data preparation considered a job for IT or highly-skilled technical teams, but rather one that spans a variety of different users, in particular the data analysts who know the data best.
Given that data preparation is not only a relatively new technology, but also a new process for many organizations, successful adoption of data preparation strategies requires adjusting the roles and responsibilities of team members to reap the benefits. A sound data preparation strategy requires organizations to consider how to appropriately leverage the different skills of their team. In order to increase efficiency, each role should be clearly defined and employed at the right time. In this post, we’ll outline how data analysts, engineers, architects, scientists, and analytics leaders all play a specific role in contributing to the success of data preparation —and analytics initiatives more broadly.
Analysts deliver value to businesses by having a deep relationship with their data. They are focused on efficiently and regularly delivering results based on knowing their data, and knowing it well. Perhaps better than anyone else, they understand that understanding the context of your data gives you the power to answer crucial questions about your organization.
Data analysts used to only be accountable for reporting against data, but increasingly they also are expected to undertake data preparation and data cleansing tasks as well. With the rise of new data preparation solutions, data scientists and IT organizations are no longer completing data preparation on behalf of analysts. Instead, these solutions have empowered analysts to own the entire analytics process end-to-end. As the frontlines of an organization’s analytic efforts, the amount of data preparation should continue to grow, as long as organizations simultaneously have the right people overseeing and operationalizing data preparation so that others in the organization can take leverage it.
Operationalizing is where data engineers come in. Data engineers play a growing and increasingly critical role in tying business and data preparation processes together. No longer are data engineers devoted to just architecting databases and developing ETL processes; now, organizations have recognized that their somewhat unique combination of technical skills and data know-how empower their more business-focused colleagues by helping them streamline and automate data-related processes.
Data engineers see the bigger picture of data preparation and how it fits into a business, making them invaluable resources for the success of an organization’s overall DataOps practices. In addition to operationalizing and building repeatable workflows typically developed by their analyst colleagues, data engineers often serve as a resource by providing training, scripts, and queries to help others on data preparation and analysis. They are particularly good at helping businesses scale through the operationalization of repeatable, time-consuming data preparation processes. When scaling data preparation, organizations should lean on data engineers to help lead this effort and ensure secure growth.
The data architect decides how data (and the tools that access it) will be configured, integrated, scaled, and governed across different organizations. Their broad interests mean they have a direct and important stake in any business project that uses data owned or touched by IT. Analytics initiatives need the buy-in of data architects to succeed, since they typically both govern and control the data that analysts and other stakeholders will use in these projects.
Because a data architect typically deals with a large number of disparate systems and datasets, for every initiative, they need to easily understand who will use the data, how it will be used, and flow through every system. A data architect manages the security and access controls to data sources that flow into any data preparation system. As a result, the data architect and data engineer will work closely to ensure the success of business users who are performing this data preparation. In particular, the data architect should be consulted during any technology evaluation process to ensure new data preparation solutions are scalable and integrate into the existing environment.
Analytics leaders understand the importance of data in delivering business value. And while they may not directly use data preparation tools themselves, they recognize how having data preparation tools deployed across their organizations leads to more efficient data pipelines, improved KPIs, and potentially new insights from data. They value tools that will make their organization smarter, faster, and more efficient, so automation and repeatable processes are crucial features of the technology across their portfolio. These leaders must quickly and regularly demonstrate quantifiable value—any data preparation platform that empowers their organization to own and control the end-to-end process is seen as a huge win.
At the end of the day, the efforts of the data analysts, data scientists, data engineer, and data architects are all meant to fuel insights for analytics leaders. It is these insights that help to determine business strategy, new ventures, and the future growth of the organization. In today’s data-driven world, they are essential. The more insights that analytics leaders have, the better armed they are to make the right decisions.
To learn more about how to structure your team to successfully adopt data preparation at your organization, we invite you to join this upcoming webinar with Forrester analyst Michele Goetz on How to Structure a Modern DataOps Team.