4 things CIOs should know about event-centric data strategy

4 things CIOs should know about event-centric data strategy

A focus on data in flight, rather than at rest, is one hallmark of an event-centric data strategy

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January 18, 2019
Crossing the gap to big data

As digital transformation dominates most CIO priority lists, it is helpful to understand and adopt an event-driven data strategy as part of the cultural and technical foundation of an organization.  Gartner defines event-driven data architecture as one that “supports an organization’s ability to quickly respond to events and capitalize on business moments.”

The shift to digital business is therefore also a shift from hierarchical, enterprise-centric transaction processing to more agile, elastic, and open ecosystem event processing.

Virtually all business-relevant data is produced as a continuous stream of events – including website clicks, mobile application interactions, sensor measurements, database modifications, applications, machine logs, stock trades, and financial transactions, for example. So, adopting a stream-first mindset would seem to be a no-brainer. But it’s not quite that simple.

The preparation behind this transition to real-time data processing entails multiple considerations, ranging from purely technical to organizational requirements.

In particular, developers need to be prepared to support and build upon a faster, more distributed architecture designed to deliver continuous value to its users. A solid data strategy, clear vision, and adequate training are also required.

[ CIOs are taking a hard look at digital transformation investments in 2019. Read our related story, Digital transformation reality check: 10 trends. ]

Below are four differences between a traditional and an event-centric data strategy, and what CIOs and IT leaders should keep in mind while going through such a transition.

1. You’re dealing with data “in flight”

In monolithic architectures, data is at rest. But in event stream processing, data is “in flight” as it moves continuously through your infrastructure, producing valuable outcomes when data is most valuable: as soon as it is generated.

You need to reimagine your systems and infrastructure to handle large volumes of continuous streams of data and make appropriate data transformations in real time.

2. Reacting to data becomes the top priority

Your data infrastructure opens a different perspective, moving from a “preserving-my-data” to a “reacting-to-my-data” state of mind.

Stream processing enables your digital business to act upon events immediately as data is generated, providing an intuitive means of deriving real-time business intelligence insights, analytics, and product or service customizations that will help differentiate your company from its competition.

Therefore, your system needs to focus on endorsing this continuous flow while minimizing the tradeoffs required to process it.

3. Your source-of-truth is adjusted

Your data strategy will ultimately impact the outlook of data authority as well as the level of chaos within your organization stemming from increased data creation.

From the single-point data store in a monolithic data architecture, your focus will change to a stream processor, making data- and event-driven decisions as you react to events in real time or using sensor data to find the cause of a system failure that might impact the operation of your business.

4. IT takes on new roles in your organization

Shifting to event stream processing changes how business perceives IT and data systems. Your IT department is regarded differently and takes on additional responsibilities. Your infrastructure will enable multiple tiers of the organization to access and interpret both freshly baked and historical data independently of heavy, centralized processes.

Making the most of this approach requires stricter control over how data is processed and applied to avoid people getting stranded with piles of meaningless information.

Cultural change ahead

Adopting an event-driven architecture requires careful planning and groundwork in order to drive a successful transition. This transition is as much cultural as technical: It expands way beyond the data infrastructure teams and requires the early involvement of multiple departments within the organization.

CIOs need to align with their IT and data leaders on a shared vision to advocate this new data approach as the enterprise evolves from a passive request/response way of gathering data insights to an active, real-time data-driven way of operating.

[ Successful businesses are rethinking IT's role. Get lessons learned from your peers in our report: The New Rules of CIO Leadership. ]

Stephan Ewen is a committer and Project Management Committee member of the Apache Flink project. He is one of the original creators of Apache Flink and a co-founder and the CTO of data Artisans, a Berlin-based company that is bringing real-time data applications to the enterprise. He has a PhD in Computer Science from TU Berlin and has worked with IBM and Microsoft.

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