Redefining Your Data Team’s Roles & Responsibilities for Data Prep Success
Before you can analyse and draw insights from your data you first need to collect, clean and consolidate it. With swathes of data at their fingertips, this can be a pressing challenge for many businesses but it doesn't have to be. Megan Kierstead, Principal UX Researcher at Trifacta gives tips for on how you can ensure data preparation success.
All data initiatives, whether machine learning, visualisation or reporting, rely on clean data. Which means that data preparation is essential to any data-driven organisation. Increasingly, businesses are adopting new solutions to increase the accessibility of data preparation and reduce the time involved in a governed, secure manner—no longer is this process considered a job for only 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.
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Given that data preparation is not only a relatively new technology, but also a new process for many organisations, successful adoption requires adjusting the roles and responsibilities of team members to reap the benefits. A sound data preparation strategy requires organisations 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.
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. But how can you make the most out of your data team? Here is an overview of the key strengths and responsibilities of each of these types of data experts and the role that they can play in your data preparation strategy.
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 realise understanding the context of your data gives you the power to answer crucial questions about your organisation.
Data analysts used to only be accountable for reporting against data, but increasingly they also are expected to prepare and cleanse data as well. With the rise of new data preparation solutions, data scientists and IT organisations 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 organisation’s analytic efforts, the number of analysts preparing data should continue to grow, as long as organisations simultaneously have the right people overseeing and operationalising this work so that others in the business can take leverage it.
Operationalising is where data engineers come in. They 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 Extract Transform and Load (ETL) processes; now, organisations have recognised that their somewhat unique combination of technical skills and data know-how allow them to 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 organisation’s overall DataOps practices. In addition to operationalising 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 prepare and analyse data - which are often time-consuming processes. When scaling data preparation, organisations 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 organisations. 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 successful data preparation. In particular, the data architect should be consulted during any technology evaluation process to make sure new 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 recognise how having these tools deployed across their organisation leads to more efficient data pipelines, improved KPIs, and potentially new insights from data. They value tools that will make their organisation 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 organisation 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 this information that helps to determine the business strategy, new ventures, and the future growth of the organisation. In today’s data-driven world, they are essential. The more insights that analytics leaders are able to draw on, the better armed they are to make the right decisions.