DataOps manages your data workflow and processes, plucking out various bottlenecks and roadblocks that prevent your data organisation from achieving efficient productivity and appropriate quality.

2. DataOps has become a trending buzzword in the IT industry. But what is DataOps? Is it worth incorporating DataOps in
your organisational data operations?
Let’s find out!
What Is DataOps?
DataOps compilation of technical processes, organisational workflows, cultural approaches, and data architectural styles
that enable:
● Innovations and research, delivering advanced insights to clients with rapidly
● Impeccable data quality and minimal error rates
● Successful collaboration and cooperation between intricate arrays of teams, technological resources, and IT
environments
● Efficient measurement, robust monitoring, and transparency in results.
In practical terms, DataOps integrates Agile methodology, lean manufacturing, and DevOps culture to the process of data
analytics.
Therefore for a precise understanding of DataOps, we’ll need to explore the terminologies: Agile process, lean
manufacturing, and DevOps.

3. The Three Pillars Of DataOps Management
Agile Methodology
For effective DataOps management, both collaboration and innovation are necessary. Therefore, DataOps implements
Agile Development into data analytics for enabling data teams and users to function collaboratively in a more productive
and effective way.
Using the Agile environment, the data team publishes new or modified analytics in short increments or slots called
“sprints.”
With rapid advancements, the data teams can consistently reassess and streamline their priorities and efficiently comply
with the evolving innovation requirements from the ongoing user feedback loop.
This level of responsiveness is impractical to achieve through a Waterfall project management style since it blocks a team
into a long development cycle without collaborating with the users until the one “star-studded” deliverable at the end.
In a DataOps culture, Agile approaches empower enterprises to respond quickly to evolving client/user demands and
accelerate time to value.

4. Lean Manufacturing
The process or method of “lean manufacturing” was initially conceptualised by the Japanese manufacturing industry
(Toyota) and embraced globally.
The lean manufacturing approach involves limiting waste within a system or manufacturing pipeline without adversely
affecting productivity.
Data analytics and management utilises a data pipeline. Data constantly enters from one end of the pipeline, progresses
through various extraction and purification phases/steps, and exits from the other end as reports, models, forecasts, and
views.
This data pipeline (also known as data factory) constitutes the “operations” part of data analytics.
DataOps includes orchestration, management, and monitoring of the data factory. One specifically powerful and
essential lean manufacturing tool is SPC or statistical process control.
SPC measures and verifies data and operational components of the data pipeline to ensure the statistical variance is
maintained within acceptable ranges.
SPC contributes to spectacular enhancements in data quality efficiency, workflow efficiency, and transparency in the data analytics
realm. If the SPC tool detects any anomaly, the data analytics team receives an alert through an automated process.

5. DevOps
The DevOps approach implements automation in the software development cycle that rapidly accelerates the build
lifecycle (also called release engineering).
DevOps supports consistent software application deployment by leveraging on-demand IT resources and introducing
automation in code integration, testing, and deployment phases.
This conjunction of software development (Dev) and IT operations (Ops) minimises deployment time, time to market,
defects and bugs, and the time required for troubleshooting and bug resolution.
Using the DevOps approach, industry leader reduced their software release cycle time from months to seconds. DataOps
implements DevOps and other methodologies which apply to the unique challenges of managing an enterprise-critical
data operations pipeline.

6. What Challenges Does DataOps Address?
DataOps manages your data workflow and processes, plucking out various bottlenecks and roadblocks that prevent your
data organisation from achieving efficient productivity and appropriate quality.
Lengthy timelines discourage and disappoint your clients and can interfere with creativity.
DataOps solves the following data analytics and data management issues.
● Poor and in-efficient teamwork
● Long waiting time for IT systems and access to data
● Lack of team collaboration and siloed culture
● Over-caution and intricate verification approaches
● Inflexible data architecture
● Bottleneck and obstacles in the data pipeline
● Poor data quality

7. Is DataOps Worth Your Investment?
As complex and overwhelming as some of these data management issues are, various IT portfolio management
companies and other enterprises have successfully attained rapid cycle time and immaculate data quality through
DataOps.
Delivery pipeline and data insights extraction got reduced from months to hours and even minutes.
Using the DataOps platform, transform raw data into insights that bring value to the business and your customers.