DataOps Is Here to Stay. Here’s Why.
The methodology we use tames unruly pipelines in order to increase the value of your data — so you can adapt faster to business changes.
There are many reasons why some things become popular while others do not in the tech industry. What distinguishes one from the other? Is there a solitary, ideal predictor of whether something has staying power?
Looking at how relevant something is across data’s lifecycle can give clues as to whether it will be enduring.
By creating a centralized location for your data, data warehouses give you instant access to information so that you can make informed decisions. But they have trouble helping you see your past or your future. They're in style now but will become less and less relevant unless they find ways to be used throughout data's timeline.
Data lakes are capable of capturing changes in data over time, but the manual API integrations that act as "tributaries" to feed data lakes are often limited by their initial design purposes. The frequency with which data is captured and the breadth of data availability across an organization serve to limit the relevance of data over time.
The same goes for master data management (MDM). Ponder how static or frozen in time the concept of traditional MDM is. It queries: "What is my 360-degree view of this customer right now?" The answer to this question changes over time, as data sources come and go. MDM sits atop the ever-changing histories of many different data sources, all of which wax and wane in relevance over time.
DataOps helps organizations manage their data pipelines and get more value from their data. The business of data operations is to maintain our data so that it changes over time in a way that is beneficial to us.
How can mapping out who needs data and why be a good starting point for using DataOps? For example, system administrators might be capturing hourly snapshots of data for disaster recovery, while fulfillment teams might be ingesting subsets of the same data into their supply chain systems via the system’s APIs.
After you establish the initial data-consumption map for your organization, you'll need to map out how often each data consumer needs the information. Some may need it every five minutes, while others can wait for daily updates.
A common next step is to establish a data lake strategy, in which organizations replicate data from a target application or source into a nearby data lake. All data consumers are then instructed to use that replica as the primary source for data from a given target. By forming data lakes around common watering holes, organizations can develop a "data fabric" strategy that interconnects the lakes and unifies constantly evolving data sources, allowing for the analysis of patterns and opportunities over time.
DataOps is an ongoing process for organizations to become more responsive to signals from various data sources about the speed and health of the business. Those who engage in DataOps will have a competitive advantage.
