In today’s highly competitive digital world, enterprise companies must be data-driven to win. Data has become the fuel for companies to deliver accurate business decisions at lightning speed. Data-driven businesses are not only able to provide a better, more targeted customer experience but can also understand and act upon new opportunities or threats ahead of the competition. It is no surprise, then, that many enterprise CEOs have signed-off large, expensive digital transformation projects in a bid to turn their traditional companies into a data-driven wonder.
Yet, becoming data-driven requires more than a willingness to adopt and integrate new analytics technologies like machine learning (ML). A report by Gartner noted that “despite massive investments in data and analytics initiatives”, almost half of all organisations surveyed expressed “difficulties in bringing them into production”. The fact is, to truly be data-driven, data must sit at the very centre of the business. This not only requires data-centric processes and culture, but a real understanding of the teams responsible for making the most of this data within the business.
Data engineer vs. data scientist
A common misconception among enterprise business leaders is that their data-driven ambitions will be realised by hiring data scientists. Data scientists are, of course, a crucial part of a data-driven business. Their ability to unearth interesting and unusual data patterns, and develop predictive and analytical models, helps to discover new solutions that can lead to positive outcomes such as cost-saving. However, data scientists are not purely driven by business goals. Instead, they are motivated by experimentation. If not managed appropriately, this can hinder data projects as data scientists search for solutions that the business may not want to implement.
Data engineers, on the other hand, are responsible for translating data insights into technical and data requirements to directly meet business objectives. Unlike data scientists, data engineers are firmly focused on driving a business’s overall data strategy forward. This can include assisting with the performance of analytics projects, authorising data for different audiences, and ensuring data governance for regulation compliance.
Ultimately, data engineers are responsible for ensuring that the ‘right’ data is delivered for the right task with the level of quality and at the speed expected within a data-driven organisation. Enterprise companies must learn how to use both data scientists and data engineers on the same data project simultaneously. Only then will they achieve success. However, to make the most of this duo, it is important that enterprise companies acknowledge the common barriers that data engineers face within business environment.
Barriers facing data engineers
Data engineers face a unique challenge every day – an undefined job role. Part developer, part data scientist and part analyst, data engineers play a crucial role in enabling organisations to get value out of the data faster and at scale. Yet, this mishmash of capabilities can confuse business leaders and lead to the assignment of tasks better suited to a data scientist.
What’s more, in today’s digital world, new technologies, software and application methods are continuously being developed by communities outside of the business world. Keeping up to date with what’s new and understanding how it might be used to meet business objectives is a crucial part of a data engineer’s job. However, given the traditional confidents of enterprise companies, data engineers can often find themselves locked into a solution that limits agility – and thus, success.
A lack of data integrity can also be a key barrier for data engineers. Data-driven success is only possible when trusted data, and thus insights, can be delivered at speed to the business. However, according to the Harvard Business Review, 47 per cent of data records are created with flaws and errors that impact work.
In the UK, a survey commissioned by Talend highlighted a serious chasm between operational data workers and IT senior managers regarding data integrity. While almost half (45 per cent) of senior IT management were confident that their organisation’s data assets were accurate, complete and up to date; only 19 per cent of specialists working with the data day-to-day felt that this was the case.
In a data-driven business, insights must be leveraged by departments and teams across the enterprise. To ensure success, these data insights must be delivered in a format that can be consumed without inaccuracies, inconsistencies, or human flaws. To deliver trusted insights, data must also be trustworthy. Fortunately, data engineers own the end-to-end data strategy and are well placed to help data scientists and analysts capture data lineage, operationalise data models and data sets, and deliver trusted data throughout the enterprise. However, data engineers must be primed with the right resources and tools to successfully complete this crucial day-to-day requirement.
Priming data engineers to save the day
Luckily, new tools have emerged in recent years to alleviate pressure on data engineers. These solutions can help with the collection, management and integration of data so that more time can be spent analysing data and guiding data strategy. For example, to deliver trusted data at the speed enterprise companies need, data engineers are leveraging modern data integration and integrity solutions. This helps automate data pipeline creation, reduces integration complexity, ensures compliance with security and privacy requirements, and allows the business to easily adapt to technical and business changes.
Data engineers have become an extremely valuable resource to enterprise companies. They play a key strategic role in helping business leaders harness valuable data insights to meet critical business objectives. The capability to understand and navigate data is rare within the enterprise and can help to propel a traditional company into a thriving data-drive business. Yet, to do this, the role of a data engineer and the daily challenges plaguing them must be widely understood by business leaders. They must also have access to the right tools for success. If this is recognised, enterprise companies see themselves transforming into data-driven businesses – ultimately, giving the C-suite one less thing to worry about during this rocky climate.
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