“The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change.”
According to Donald Feinberg, vice president and distinguished analyst at Gartner, the very challenge created by digital disruption — too much data — has also created an unprecedented opportunity. Data centricity is becoming one of the most important trends for data analytics companies in the last quarter of 2019. Data analytics services are evolving from data-driven architectures to data-centric ones.
What’s the difference between data-driven and data centric architectures?
Data analytics companies, consulting and tools are part of a natural progression from data-driven architecture – when an organization creates the data analytics tools and culture to act based on data – to data-centric architecture – when data is part of the organization’s core model.
What does data centricity really mean? Let’s use marketing data analytics for an example. First, a data-driven project might involve integrating response data from marketing automation with customer data from CRM and accounting systems to help target a new segment. Data analytics tools like PowerBI or TIBCO Spotfire might be used to help manage and measure the results, particularly for stakeholders who do not have access to marketing automation reports.
Data centricity, on the other hand, would involve cataloging data assets, so that the data from marketing automation, CRM, and accounting tools would have already been anticipated and catalogued. New targets would require analytical work, perhaps performed internally or perhaps with data analytics consulting, but this work would build on the data centric foundation, with this metadata being shared with others, including stakeholders who have their dashboards to monitor marketing data analytics.
There are a couple examples of the trend towards data-centric architectures.
A data catalog is a common point of reference that enables data users to find, understand and collaborate on entreprise data. Data analytics and business users annotation this data in an incremental, consistent way to enrich data with context. In this way, data assets are organized incrementally but with central organization in mind. In some situations, a data catalog crawls databases and business intelligence systems to help automate organizing enterprise data.
The Blurring of Inbound and Outbound Marketing
Inbound marketing has been a trend in marketing data analytics since outbound techniques like email marketing to lists became ubiquitous and started to turn off prospects. Inbound techniques focus on content and attractive visitors to your content to engage in a dialog that, in the ideal situation, turns into a customer journey.
The need for agility drives data-centric architectures. Data-driven organizations are simply not fast enough to initiate their part in a dialog with the market when data needs to be pulled from different tools, organized, and used to deliver campaigns. Interactions that feel more natural to people are driven by marketing data analytics that are already setup and anticipate many uses. As new interactions or campaigns occur, the data is updated, and interactions can be inbound or outbound at any time based on triggers rather than campaign plans.
If you need help on the journey to data centricity, Syntelli Solutions provides data analytics consulting for any level of data maturity. Contact us to learn more.