There’s no one-size-fits-all solution for a modern data platform, and there likely never will be with the proliferation of multiple public and private cloud environments, entrenched on-premises data centers, and the exponential rise in edge computing – data sources are multiplying almost at the rate of data itself.
Today’s data platforms increasingly take a broad multi-platform approach that incorporates a wide range of data services (e.g. data warehouse, data lake, transactional database, IoT database and third-party data services), and integration services that support all major clouds and on-premise platforms and applications that run on and across these environments. Modern data platforms need a data fabric – technology that enables data that is distributed across different areas to be accessed in real-time in a unifying data layer, – to drive data flow orchestration, data enrichment, and automation To meet the varied requirements of users across an organization including data engineers, data scientists, business analysts and business users, the platform should also incorporate shared management and security services, as well as support a wide range of application development and analytical tools.
However, these needs create a singular challenge: who’s going to manage the creation and maintenance of such a platform? That’s where the role of the data platform leader comes in. Just as we’ve seen the creation of roles like Chief Data Officer and Chief Diversity Officer in response to critical needs, organizations require a highly skilled individual to manage the creation and maintenance of their platform(s). Enter the data platform leader – someone with a broad understanding of databases and streaming technologies, as well as a practical understanding of how to facilitate frictionless access to these data sources, how to formulate a new purpose, vision and mission for the platform and how to form close partnerships with analytics translators. We’ll get to those folks in a minute.
Developing a new purpose, vision and mission
Why must a data platform leader develop a new purpose, vision and mission? Consider this: data warehouse users have traditionally been data engineers, data scientists and business analysts who are interested in complex analytics. These users typically represent a relatively small percentage of an organization’s employees. The power and accessibility of a data platform capable of running not just in the data center, but also in the cloud or at the edge, will invariably bring in a broader base of business users who will use the platform to run simpler queries and analytics to make operational decisions.
However, accompanying these users will be new sets of business and operational requirements. To satisfy this ever-expanding user base and their different requirements, the data platform leader will need to formulate a new purpose for the platform (why it exists), a new vision for the platform (what it hopes to deliver) and a new mission (how will it achieve the vision).
Facilitating data service convergence
Knowledge of relational databases with analytics-optimized schemas and/or analytic databases has long been part of a data warehouse manager’s wheelhouse. However, the modern data platform extends access much further, enabling access to data lakes and transactional and IoT databases, and even streaming data. Increasing demand for real-time insights and non-relational data that can enable decision intelligence are bringing these formerly distinct worlds closer together. This requires the data platform leader to have a broad understanding of databases and streaming technologies as well as a practical understanding of how to facilitate frictionless access to these data sources.
Enabling frictionless data access
A data warehouse typically includes a semantic layer that represents data so end users can access that data using common business terms. A modern data platform, though, demands more. While a semantic layer is valuable, data platform leaders will need to enable more dynamic data integration than is typically sufficient to support a centralized data warehouse design. Enter the data fabric to provide a service layer that enables real-time access to data sourced from the full range of the data platform’s various services. The data fabric offers frictionless access to data from any source located on-premises and in the cloud to support the wide range of analytic and operational use cases that such a platform is intended to serve.
Working with analytics translators
I mentioned earlier that data platform leaders would need the ability to form close partnerships with analytics translators. Let’s start with what an analytics translator does and then we’ll get to why a close relationship is important.
According to McKinsey & Company, the analytics translator serves the following purpose:
“At the outset of an analytics initiative, translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved. These may be opportunities within a single line of business (e.g., improving product quality in manufacturing) or cross-organizational initiatives (e.g., reducing product delivery time).”
I expect the analytics translator and the data platform leader will become important partners. The analytics translator will be invaluable in establishing data platform priorities, and the data platform leader will provide the analytics translator with key performance indicators (KPIs) on mutually-agreed-upon usage goals.
In conclusion, the data platform leader has many soft and hard skillset requirements in common with a data warehouse manager, but there are a few fundamental and significant differences. The key difference includes developing a new purpose, vision and mission, having expertise in new data services and data fabrics, knowing how best to access those services, and possessing the ability to form close partnerships with analytics translators.
Teresa Wingfield, Director of Product Marketing, Actian
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