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Minimize operational and key-personnel risks

Many data platform projects are complex and risky, data automation is designed to minimize and mitigate these risks.

Why it matters

  • Without an answer to the many risks related to building platforms for data, analytics, AI and ML, organizations may very well spend too much money and time without getting the results they aim for
  • Key risks include
    1.  Not meeting users' expectations often by treating it as a technology project and not and dedicating enough time with stakeholders (Affects quality)
    2. Key personnel risks such as when people change role due to lack of documentation and standardization (Affects time, costs, quality)
    3. Lack of agility as the solution is getting too complex and difficult to change, you may reduce ROI and get more technical debt (Affects time, costs, quality)
    4. Lack of data quality and lack of built-in support to continuously monitor data quality levels hinder new projects (Affects time, costs, quality)
    5. Lack of data availability as data can be spread across multiple data silos, have highly complex structures, or be hard to access through available APIs (Affects time, costs, quality)
    6. Lack of ownership to data due to data being pushed to a 3rd party SaaS solutions (Affects quality)
    7. Change in dependent technologies such storage, processing and other services where your data team is unaware and can be at risk of a system crash (Affects time, costs)
    8. Data wrangling is much more time-consuming than expected and projects are deplayed (Affects time, costs)
    9. Not able to scale with cost/benefits in the cloud making the project either start too expensive or start cheap but cannot scale later (Affects costs, quality)

How it works

This is how data automation mitigates these risks:

  1. Meeting users' expectations: Automation lets your data team use less time on data integration and more on involving stakeholders and greating value from the data
  2. Key personnel are less critical due to standardization and best practices letting people change roles and projects without crippling production and operation
  3. Agile solutions due to built-in and automatically updated documentation, data lineage, change handling mechanisms such as impact analysis, source control, test automation and many productivity means
  4. Ready data quality mechanisms with test automation as part of DataOps to be proactive about data quality challenges and address challenges to the data owners
  5.  Easier to make data available with passthru API generation, automation of reading APIs and any database and flat file
  6. Full ownership of your data as the data platform runs within your tenant in Azure or AWS and not copied or sent out
  7. R&D team monitors dependent technologies and have dedicated resources for making updated and fixes, and even discuss directly with teams developing those technologies for early warnings
  8. Automation minimizes time needed for data wrangling by working on a higher level of abstraction
  9. Scale up/down (or add/remove storage technologies) at any time in the cloud without rewriting code due to the higher abstraction level in the automation tool, lets you leverage new future technolgies faster and lets you start small and scale (no big bang start needed regardless of your future ambitions)  

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