The world is experiencing a truly transformational period in the world of data with the birth of a new generation of tools to manage the explosion of data seen since the dawn of the Covid pandemic.
However, many large, data-driven businesses are still facing the decision of how to decentralise and manage access to data across their organisations. Whilst the cloud offers smaller businesses the opportunity to benefit from enterprise-grade data tools, systems and platforms, many businesses have quickly realised that a data team can become a bottleneck if analysts and engineers are unable to access the data that they need straight away.
With this growth in data also comes a growth in data job roles, with employers factoring the trends for data management into their hiring decisions. Many businesses are now employing data experts to interpret advanced analytics to develop more robust insights. Yet relying on data experts alone can be problematic in the transformation of a business. There are many obstacles that cannot be faced by data experts alone. Instead, the power of big data should be leveraged by teams into their data operations with the right data management solution. This allows businesses to hybridise their operations without having to hire more expert staff.
Collaboration is key
Decision-makers can however, overlook other integral team members when it comes to managing their data operations. As data experts become more business-minded and business users learn to ‘self-serve’ with data, the artificial divisions between data experts and business users can break down. One aspect of this is the rise of roles such as ‘analytics engineer’, which help to bridge the gap between IT and the data consumers within an organisation. Analytics engineers collaborate with the team to analyse the data, to ensure that the business can use the high-quality insights generated from their work. Together with wider teams, these engineers help to set up and activate a truly modern data stack.
The rise of data citizens
Rather than relying solely on hiring qualified data experts, business leaders should aim to train their existing workers with data skills: this can help to keep costs and overheads down. Data literacy courses are already becoming common in many companies, and large organisations such as Bloomberg and Adobe are going further, with in-house digital academies dedicated to training workers in how to use data.
Training existing employees is particularly powerful because they combine newly acquired data skills with their existing domain expertise to extract maximum value from the 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 it for them.
Unlocking the business value of data
Democratising access to data within your organisation and unlocking the business value of data requires the right technological tools. Reverse ETL turns the normal job of data warehouses on their head to direct a stream of valuable data directly to the teams which need it most. It reverses the traditional process by which data is loaded into a data warehouse, by first extracting it from a data warehouse and then loading it into operational systems.
Reverse ETL is key to breaking down the barriers between data and the data consumers within a company and removing a burden from overworked specialist data teams.
The role of data mesh
Along with these technological changes and job role evolutions around data, there is also a new organisational approach to how data works within companies; a data mesh. In short, data mesh offers a decentralised and ‘self-serve’ approach to delivering data throughout an organisation. Rather than relying on a centralised data team – where the warehouse is controlled by hyper-specialised experts – data is organised via shared protocols, in order to serve the business users who need it most.
The significance of this is that it helps empower teams to access the correct data they need, right when they require it, via the distribution of data ownership across the organisation. By applying product thinking to datasets, a data mesh approach will ensure that the discoverability, security and explorability of datasets are retained. Teams are then better prepared to swiftly derive the most important insights from their data.
Serving data as a product
In order to make timely decisions, it is critical that businesses can provide access to the right people. By empowering people across the business with access to the data that they need through the appropriate tools and technologies, teams can act on data in real time to become data citizens. Having data citizens throughout the company that are able to self-serve data as a product enables teams across an organisation to autonomously manage their data and analytics processes. With an internal team of data experts in place across a majority of functions, businesses will be able to gain full insights from their data and needless bottlenecks and inefficiencies can be prevented.
About the Author
Itamar Ben Hemo is the CEO and co-founder of Rivery. Whether you’re building out your data stack or transitioning to the cloud, managing your data workflows to analyze your business can be a real challenge. Developing an in-house solution requires valuable resources and upkeep, while integrating several tools adds new layers of complexity. Rivery’s SaaS platform provides a fully-managed solution for data ingestion, data transformation, data orchestration, reverse ETL and more, with built-in support for your data operations development and deployment lifecycles.
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