Why DataOps is a Major Quality Trend for 2020 - InformationWeek

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Data Management // Big Data Analytics
08:00 AM
Pierre DeBois
Pierre DeBois

Why DataOps is a Major Quality Trend for 2020

Take the DevOps concept a step further and see how DataOps can improve data quality and customer experiences.

Image: ra2 Studio - stock.adobe.com
Image: ra2 Studio - stock.adobe.com

If you have to select the biggest trends going forward into the next decade, one is something that you won’t see on a screen or a device. It is an essential backend activity for making customer experience from a screen or device great.

Incorporating DataOps -- developer-based protocols for data access, blending, and transformation -- is an emerging trend influencing strategic enterprise decisions. It will continue because of its impact on managing marketing campaign quality and ROI.

For a time analysts mainly focused on website analytics to better understand and manage customer-related activity. The data generating the metrics was accepted at face value because it came from a trusted web analytics solution. Analysts interpreted results from analytics solutions and then transformed metrics into graphs to explain outcomes and influence decisions.

Today’s analytic solutions incorporate conditional statistics. This new capability introduces an elevated complexity, with analysts comparing more dimensions and metrics from data transformed from its source. Moreover, many conditional theories associated with machine learning require specific judgments to identify when applied systematic decisions lead to bad marketing campaigns, poorly conceived machine learning models, and technological hiccups that weaken a customer experience. The end result is for analysts and managers to seek technological and operational management of data models that rely on those statistical frameworks.

DataOps provides the opportunity to apply developer-proven disciplines as a quality check for data and associated programming. For example, test-driven development, a testing discipline associated with software development, ensures that data quality and programmatic quality are met prior to parameters being applied to apps, chatbots, and device software meant to support customer-facing operations.

DataOps has been aimed primarily at advanced data models, but as devices and subsequently operations become more data-driven and managed by those models, DataOps concepts have become essential. The end result is an opportunity to apply DataOps best practices to maintaining quality of a customer usage of a device or customer interaction with an operation.

Business analysts can leverage DataOps disciplines to determine which operational practices and solutions intersect with activities to support a quality standard. Managers can ask some basic questions to frame what is needed, such as:

  • What kinds of alerts that identify outliers and trend changes within a given dataset work best? Do they trigger by a percentage or at a threshold value?
  • What data types will be populated under these variables? How are the statistical assumptions for the data being vetted and tracked against the variables used in predictive analytic models for quality initiatives?
  • How consistent are the data sources in avoiding the data not being available for observations in the datasets? How are those being handled? Are they adjusted at the model or are there consistent technicalities that require IT intervention to fix? 

DataOps can enhance quality by surfacing key processes that impact the organization. The data preparation may involve time-consuming tasks such as gathering requirements, modeling data, creating a report, and arranging for distribution of reports. But understanding the value of these tasks encourages the removal of siloed processes. A broad cross-section of professionals can review results and elevate the right actions, keeping data and model quality in step with a quality standard an organization seeks to uphold. The effort can be helpful for IT professionals to prioritize technology needs as well.

That need to work across teams will not dissipate in the years to come. According to Gartner over half of major new business systems by 2022 will incorporate continuous intelligence that uses real-time context data to improve decisions. That trend should hold for quality initiatives being maintained for operations and systems that closely impact customers and customer experiences. That trend also implies that management should be preparing all its stakeholders with a data strategy, not just the ones who are closest to the data. A DataOps strategy tends to require an organization to get the right stakeholders involved at the right time -- a critical component for programmatic engagement with customers and clients.

Imagine data as a deck of cards, and you can likely envision companies applying DataOps, as card players, focused on the hand they hold. DataOps practices can be the best way of ensuring a quality hand, aligning with overall corporate quality goals and supporting how well those goals are being reached. All of the different stakeholders in quality can be better coordinated, from operations to finance. That alignment will ultimately benefit a brand’s image when it comes to offering exceptional quality.

Pierre DeBois is the founder of Zimana, a small business analytics consultancy that reviews data from Web analytics and social media dashboard solutions, then provides recommendations and Web development action that improves marketing strategy and business profitability. He ... View Full Bio
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