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Redefining how companies work with data: The rise of the data expert

The world is undergoing a true transformation in the world of data, creating a new generation of tools to manage the data explosion seen since the dawn of the Covid pandemic.

However, many large, data-driven enterprises must decide how to distribute and manage access to data throughout the organization. While the cloud offers small businesses the opportunity to benefit from enterprise-grade data tools, systems, and platforms, many businesses find their data teams unable to quickly access the data they need. I quickly realized that it could be a bottleneck.

With this data growth, employers are factoring data management trends into their hiring decisions, leading to an increase in data-related jobs as well. Many companies are now hiring data experts to interpret advanced analytics and develop more robust insights. But relying solely on data experts can be problematic for business transformation. There are many obstacles that data professionals alone cannot overcome. Instead, teams should harness the power of big data into their data operations with the right data management solution. This allows companies to hybridize their operations without hiring more specialized staff.

Collaboration is key

However, decision makers can overlook other key team members when it comes to managing data operations. As data professionals become more business-oriented and business users learn to “self-serve” data, artificial divisions between data professionals and business users are likely to collapse. One aspect of this for her is the rise of roles such as “analytics engineer” that help bridge the gap between her IT and data consumers within the organization. Analytics engineers work with teams to analyze data so that the business can use the high-quality insights generated from their work. These engineers work with a broad team to help set up and activate a truly modern data stack.

The rise of data citizens

Rather than relying solely on hiring qualified data professionals, business leaders should aim to train existing employees in data skills. This helps keep costs and overhead down. Data literacy courses are already becoming commonplace in many companies, and large organizations such as Bloomberg and Adobe are going even further, offering in-house courses dedicated to training employees on how to use data. We have a digital academy.

Training existing employees is particularly effective. Because they combine newly acquired data skills with existing domain expertise to get the most value out of their data. These “data citizens” can derive value from data without waiting for another team of data experts or scientists to do it for them.

Unlock the business value of your data

Democratizing access to data and unlocking the business value of data within an organization requires the right technical tools. Reverse ETL transforms the normal work of a data warehouse, directing streams of valuable data directly to the teams that need it most. Contrary to the traditional process of loading data into a data warehouse, the data is first extracted from the data warehouse and then loaded into the operational system.

Reverse ETL is the key to breaking down the barriers between data and data consumers in your enterprise and unburdening overworked specialized data teams.

Role of data mesh

In addition to these technological changes and evolving data jobs, there are also new organizational approaches to how data works within the enterprise. data mesh. In short, Data Mesh provides a decentralized, “self-service” approach to delivering data across an organization. Rather than relying on a central data team to manage warehouses with highly specialized experts, it organizes data via shared protocols and makes it available to the business her users who need it most.

The importance of this is distributing data ownership across the organization so that teams have access to the correct data they need, when they need it. By applying product thinking to datasets, the data mesh approach ensures that datasets remain discoverable, secure, and discoverable. This equips your team to quickly extract the most important insights from your data.

Deliver data as a product

It is critical that businesses have access to the right people to make timely decisions. Empowering people across the business with access to the data they need through the right tools and technology empowers teams to act on data in real time and become data citizens. Empowering teams across the organization to autonomously manage data and analytics processes by deploying data citizens across the enterprise that can self-service data as a product. By having an in-house team of data experts across most functions, companies can gain full insight from their data and prevent unnecessary bottlenecks and inefficiencies.

About the author

Itamar Ben Hemo is the CEO and co-founder of Rivery. Whether you’re building your data stack or moving to the cloud, managing your data workflows and analyzing your business can be very challenging. Developing an in-house solution requires valuable resources and maintenance costs, but integrating multiple tools adds new complexity. Rivery’s SaaS platform offers fully managed his solutions for data ingestion, data transformation, data orchestration, reverse ETL, and more, with built-in support for the data operations development and deployment lifecycle. .

Featured Image: ©Golodenkov

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