A HandBook Dictionary on DataOps and Its Importance

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A HandBook Dictionary on DataOps and Its
Importance
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• Big giants like Google and Amazon, release
  software quite often in a day! Reason? They
  started to implement DevOps, which helped them
  improve upon their quality of codes and reduced
  their product cycles.
• Optimizing and releasing codes swiftly was once a
  pipedream for most of the organizations. However,
  the end-to-end cycle time has greatly been
  reduced for the organizations that have already
  started implementing the practices and making
  value out of it.
• After observing the success of Big Giants,
  companies want to get into the process ending
  with -Ops treatment. They want to embrace the
  revolutionary change the DataOps practices are
  bringing into the process.
• DataOps, under its umbrella, covers Agile
  methodologies, DevOps, and Lean Manufacturing
  processes and collaboratively helps in focusing
  on communication improvement, integration, and
  automation of the data coming in the data
  pipeline.
• Nick Heudecker, an analyst at Gartner, confirmed
  that the implementation of DataOps in the Test
  Environment Management Tool automates the data
  flow between the managers and consumers.

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• Additionally, it mitigates the chances of any
  miscommunication between the makers and the
  buyers. He further added that it is a
  people-driven practice first and then a
  technology-oriented.
• The inconsistency, inflexibility, bottlenecks,
  long cycles, and a waste of time almost becomes
  negligible with the implementation.
• It is observed that nearly 75 of an employees
  day is unproductive because of unplanned and more
  work scenarios. It does happen with the
  organizations that resources are there.

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• However, they still feel the need to hire more to
  improve the overall productivity of the process.
  Such scenarios are a suitable example of poor
  business processes.
• It was quite a surprise for everyone to know when
  Amazon declared that their team releases
  50,000,000 codes every year, while for others, it
  requires a minimum of 6 months for the data team
  to deliver a 20-line SQL change.
• Imagine the wonders this implementation can bring
  to your company if followed well.
• DataOps helps in making procurement and storage
  of data efficient and fast. It also gives
  real-time insights into the large volume of data
  collected automatically by the tools.
• It parallelly works with different processes
  related to data handling, including the DevSecOps
  practices and quickens the software release time,
  thereby improving the quality of products.
• Having said that, there are challenges and
  troubles faced by the management in handling data
  for quality analysis. If not implemented in the
  right manner, the collected data loses its value,
  and the delivery time starts fluctuating.

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• And this enforces the data management team to
  remain on their toes and ensure that all the
  queries are resolved on time, and no process is
  delayed beyond expectations.
• Adding to this, the data in the pipeline is
  growing, and so is the requirement and
  expectations from data analytics, scientist, and
  data-hungry applications.
• Also, the data is received in different ways
  through different platforms that demand more
  control over the system in order to identify the
  loopholes.
• Some daunting challenges are bad quality and
  manual processes. Lets have a look into them
  briefly.
• Bad Data Quality
• The entire data loses its credibility if the
  collection of data is badly performed. The whole
  program and the team is left in jeopardy if the
  data formats are different and dont match with
  the requirement.
• Various data types and formats can lead to errors
  like duplication of entries, scheme change, and
  input failures.

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• When this goes out of hand, it becomes difficult
  for the team to know the root cause and trace the
  error. Additionally, constant and regular updates
  in the data pipeline mess up the situation more.
• Coping up with these changes is a tad difficult
  and time-consuming task for the organizations.

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• Manual Processes
• Manual integration of testing and analytics is a
  tedious and time-consuming process. It takes
  hours and effort to analyze the data and come out
  with meaningful data insights.
• The team involved with the analysis has to commit
  more and make watchful steps without a single
  compromisation.
• Hence to overcome these challenges, a tool
  wouldnt suffice instead, you need to bring
  change in the underlying processes involved with
  the data management.

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• DataOps, with its agile methodology, helps
  organizations overcome hurdles and data
  management complexities without any
  compromisation. It focuses mainly on data
  integration, cooperation, collaboration,
  communication, measurement, and automation. \
• This speed of process reduces the life cycle of
  product delivery and sets up a clear transparent
  platform for communication between engineers,
  data scientists, It and the Quality assurance
  team.
• DataOps Implementation

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• Just a few minor changes in the ongoing process
  helps in setting up DataOps effectively into the
  organization. It mitigates manual errors and
  efforts, thereby saving a lot of time.
• The implementation also notifies the company if
  any projection is done or any security alert is
  detected.
• It keeps the data intact in high quality and
  gives ultimate control over statistical
  processes.
• Implementation of these practices keeps the
  organizations working culture structured, boost
  reusability while supporting multi-developer
  environments.
• It also facilitates customized version control
  over tools and systems, which further will save a
  lot of development time and also speed up the
  analytics related to the process.
• DataOps provide the utmost flexibility in dealing
  with the data analytics pipeline. With minimal
  changes in the processes, DataOps enables getting
  desired results in the system.

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• Are you excited to streamlines the processes
  using class-apart tools and automating the
  workflow within the organization?
• The processes also impact the production
  environment and keep a check on the quality of
  data received in the pipeline.
• You get live insights into the data and report
  generation, which enables developers and
  stakeholders to speed up the process and thereby
  reduce the product delivery time.
• DataOps is a promising phenomenon that evaluates
  each and every step involved in the process.
• Not just a single step, but the whole process
  continuously participates in making an
  organization DataOps-compliant.

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Contact Us

• Company Name Enov8
• Contact Person Ashley Hosking
• Address Level 5, 14 Martin Place, Sydney, 2000,
  New South Wales, Australia.
• Phone(s) 61 2 8916 6391
• Fax [phone]
• Website - https//www.enov8.com

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