Re-defining the way that businesses deal with data: the rise of data experts

The world is experiencing a period of real change in the world of data with the birth of a new generation of tools to handle the explosion of data seen since the start of the Covid pandemic.

However, many large, data-driven businesses are still faced with the decision of how to decentralize and manage data access within their organizations. While the cloud offers small businesses the opportunity to take advantage of enterprise-grade data tools, systems and platforms, many businesses have quickly learned that the data team can become stymied if analysts and engineers cannot access the data. Find out they need it right away.

With this growth in data also comes a growth in data job roles, with employers factoring trends for data management into their hiring decisions. Many businesses are now hiring data experts to interpret advanced analytics to generate more powerful insights. Yet relying solely on data professionals can be problematic in business transformation. There are many obstacles that cannot be overcome by data professionals alone. Instead, the power of big data should be harnessed by teams in their data operations with the right data management solution. This allows businesses to hybridize their operations without hiring more specialized staff.

Collaboration is key

However, decision-makers can overlook other complete team members when it comes to managing their data operations. As data professionals become more business-minded and business users learn to ‘self-serve’ with data, the artificial divide between data professionals and business users can be broken down. One aspect of this is the rise of roles such as analytics engineer, which help bridge the gap between IT and data consumers in an organization. Analytics engineers collaborate with the team to analyze the data, to ensure that the business can use the high-quality insights generated from their work. Together with broader teams, these engineers help organize and enable a truly modern data stack.

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The rise of the information citizen

Rather than relying solely on hiring qualified data professionals, business leaders should aim to train their existing workers with data skills: this can help keep costs and overheads down. Data literacy courses are already common in many companies, and large organizations such as Bloomberg and Adobe are going further, with in-house digital academies dedicated to training employees in how to use data.

Training existing employees is especially powerful as they combine newly acquired data skills with their existing domain skills to extract maximum value from data. These ‘data citizens’ will be able to extract value from data without waiting for a separate team of data experts or scientists to do so.

Unlocking the business value of data

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Democratizing access to data in your organization and unlocking the business value of data requires the right technological tools. Reverse ETL turns the typical function of data warehouses on their heads to deliver valuable data streams directly to the teams that need them. It bypasses the traditional process of loading data into a data warehouse, first extracting it from the data warehouse and then loading it into the operating systems.

Reverse ETL is the key to breaking down the barriers between data and data consumers within an enterprise and taking the burden off of overworked specialist data teams.

Data mesh role

With these technological changes and the evolution of job roles around data, there is also a new organizational approach to how data works within companies; Data mesh. In short, a data mesh provides a decentralized and ‘self-service’ approach to information delivery across an organization. Instead of relying on a centralized data team – where the warehouse is controlled by hyper-specialized experts – data is organized through shared protocols, to serve the business users who need it.

Its importance is that it helps teams to access the right data they need, exactly when they need it, by distributing data ownership across the organization. By applying product thinking to datasets, a data mesh approach will ensure that the discoverability, security, and exploration of datasets are maintained. Teams are then better equipped to quickly derive the most important insights from their data.

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Serving data as output

In order to make timely decisions, it is important that businesses provide access to the right people. By empowering people across the business to access the data they need through the right tools and technologies, teams can act on data in real time to become data citizens. Having data citizens across the enterprise with data self-service capabilities as a product enables teams across an organization to manage their data and analytics processes autonomously. With an internal team of data experts across multiple functions, businesses will be able to gain full insight from their data and avoid unnecessary bottlenecks and inefficiencies.

About the author

Itamar Ben Hammo is the CEO and co-founder of Reverie. Whether you’re building your own data stack or moving to the cloud, managing your data workflow to analyze your business can be a real challenge. Building an in-house solution requires valuable resources and maintenance, while integrating multiple devices adds new layers of complexity. Rivery’s SaaS platform provides a fully managed solution for data consumption, data transformation, data orchestration, reverse ETL and more, with built-in support for the development and implementation lifecycle of your data operations.

Featured Image: ©Gorodinkov


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