18 - Voice of Industry Experts - Leveraging Generative AI to Solve Recurring Data Ecosystem Problems

Leveraging Generative AI to Solve Recurring Data Ecosystem Problems

Leveraging Generative AI to Solve Recurring Data Ecosystem Problems: A Data Practitioner's Perspective
Artificial Intelligence (AI) has already started solving some process problems across industries, but it has barely scratched the surface of challenges within data ecosystems. As data practitioners, we see recurring issues throughout data lifecycle that persist regardless of the technology used. While some issues are systemic in nature, others are related to usage and interpretation of data.
In this blog, we explore these common problems in data ecosystems, how AI can be leveraged to address them and potential risks involved.

Data Ecosystems: 

In any organization, data ecosystems are built over time with core data processing applications expanding and enriching to both generate and consume data, process and transform data to ultimate use it for decision making, generating and deriving insights. Thus data is truly the oil that runs these ecosystems, be it simple or ultra complex. Common characteristics that makes data more difficult to handle are:
  • Context and subjectivity – business, technical or referential context helps to explain the data
  • Process and data are intertwined at all times. A process cannot be agnostic of data it processes thus making it difficult to separate them
  • Data has history as well as shelf life – Any element of data is valid in a certain time context

Recurring problems in Data ecosystems: 

No matter the scale or technology, data ecosystems face persistent challenges that often hinder effective data usage such as:
  • Working with data across data silos: Finding, investigating, tracking, reconciling data across different data silos is the single most recurring activity
  • Poor data quality: Quality controls on data for incomplete, inaccurate, inconsistent data is contextual, often falling into cracks between applications handling data
  • Lack of standardization and Governance: Varying data definitions, formats, multiple storage locations complicate usage, collaboration and data sharing
  • Talent shortage: Since the days of mainframes, the efficiency and effectiveness of data ecosystems are largely driven by quality and availability of talent supply; especially professionals who can combine data engineering skills, business acumen and adapting to technology changes

How can generative AI solve these recurring problems: 

Generative AI with its ability to create content(set of instructions, developer guides, technical documents etc.), code and insights from data inputs present new set of tools to tackle the recurring problems. To be sure, the difference between generative AI solutions and automation, RPA and chatbots has to be clearly understood and used to solve the right kind of problem.
Our clients have frequently asked about this and its quiet clear that generative AI has great potential to solve some of following use cases:
  • Data integration and breaking silos: Automatically map and generate data transformations to harmonize data from multiple sources
  • Risk free technology transformations: In case of technology refresh of data ecosystems (e.g. On-prem to cloud) code generation, reviews, test case generation, traceability can allow faster and cost effective transformation
  • Quality control: By learning data patterns, generative AI can take us towards self-healing quality controls and improve data reliability
  • Semantic data standardization: generative AI can generate glossaries, sematic tags, technical and business metadata description at each stage of data processing. This will then enable end to end data lineage
  • Automate business documentation: Often, legacy applications are black boxes without any insight into business rules or logic. Generative AI can efficiently extract necessary business logic and document BRD(Business requirements documents)
There are many more use cases that generative AI can now facilitate. Given that brownfield ecosystems have organically grown over time, the day to day data challenges can be eased.

As a set of tools, generative AI can help different roles in getting their jobs done efficiently. Common use cases that are either experimented or put in practice are:

  • Developers : code generation and conversion, code reviews, suggest and improve coding best practices and standards, bridge vulnerabilities such as code injection, understand legacy code (e.g. Cobol)
  • Support engineers: monitor and troubleshoot batch runs, analyse tickets, incidents, first level decision making on actions, root cause analysis and recommendations to permanently fix systemic problems
  • Application owners/ Subject matter experts: Track business and system impact of data anomalies, change management, data observability across application ecosystem
  • Business analysts and Product owners: Use generative AI assistant to translate business requirements to technical requirements, generate specifications, define & track product roadmap
  • Business users: talk to data to derive insights, govern data

Potential Risks of using generative AI in Data ecosystems: 

While generative AI offers significant benefits, data practitioners should be aware and mindful of need for human in the loop and human driven use of the technology. Risk areas:
  • Data privacy and compliance needs of organization to allow data access to generative AI. The risk of exposure of sensitive data warrants a multi layered approach to using generative AI
  • Bias and Incorrect outputs if trained on biased data or insufficient data. Esp in domain of data quality, rules recommendations need to be holistic and reviewed in business context
  • Security risks – AI tools become target of adversarial attacks aimed at corrupting data or model or both
  • Overreliance on AI in data may trivialize the nuances of data in business context leading to gross errors in expected outcome
  • Nevertheless, the recommendation is to proceed with caution on generative AI usage and benefit from productivity boost.

Finally, questions for data practitioners to consider:

  • At which data lifecycle stages do bottlenecks most frequently occur, how can generative AI resolve them
  • Which solutions are best done through non AI automation vs generative AI
  • Which roles could benefit most through AI augmentation
  • What safeguards are necessary to ensure AI generated output is accurate and compliant
  • How can human expertise today be blended with AI to maximize benefits and minimize risks
If you have experiences using generative AI for data problems or concerns on related risks, feel free to share in the comments!


Vrushali Kulkarni is a seasoned data practitioner with 23+ years of experience in enterprise data and analytics. She has led the design and delivery of bespoke platforms, turning complex challenges into impactful solutions. Her passion lies in leveraging data to drive innovation, efficiency, and business growth.

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