Can Poor Data Management Pose a Threat to AI Success?

by August 14, 2020

Data Management

Enterprises are modernising their Data Infrastructures and Data Governance policies gain the most from AI implementation.

The increasing amounts of big data that modern enterprises possess make the case of data management even more vital than ever before. Efficient data management collaborating with Data Pipelines and Data Catalogues lets enterprises gain the maximum from the disruptive technologies, artificial intelligence in particular. Data catalogues are increasingly gaining prominence for all the data stakeholders, especially analyst, developer, and data scientists who are looking for available data assets to garner the maximum business intelligence.

  • A successful data catalogue is built on two fundamentals that encapsulate automation and collaboration.
  • Automation ensures that the technical metadata is enriched with business metadata for business users to gain the maximum from this valuable asset.
  • Since not everything can be elevated with automation, the case for collaboration gains strength. The hefty word is all about arranging for user-friendly interface for data specialists to enrich some metadata manually, discuss it, and share the knowledge amongst the citizen data scientists.

 

Addressing the Data Practice Threats

As several companies haven’t attained the ultimate nirvana, the high level of sophistication with crucial data-related aspects, posing a threat to the current data practices.

Is cleaning data really that difficult?

Research suggests, yes!! Cleaning crude and inaccurate data before feeding it into an AI model is a cumbersome process.

The Holistic Data Preparation Process-

  • As more organizations shift their AI workloads to a cloud environment, data integration challenges are intensifying. Some of the most common barriers to access third-party data sources include dealing with disparate data that exists on different systems and merging data from diverse sources.
  • Data preparation demands persistence and disciplined execution. Data specialists spend a large part of their workweek preparing big data for analytics and AI/machine learning (AI/ML) initiatives.
  • For all these efforts, the right talent and expertise can be critical. Often, AI/ML initiatives fail primarily due to lack of expertise, besides other major factors that include unavailability of production-ready data and integrated development environment.

To add to data management woes, Data Governance is fast gaining prominence as a tough problem spot.

As a result, it is easy to fall prey to pitfalls such as inadvertently using or revealing sensitive information hidden among anonymized data. For example, while a patient’s name might be encrypted from one section of a medical record that is used by an AI system, it could be present in the doctor’s notes section of the record. Such data governance and data responsibility conditions must be addressed since it is critical for the C-suite to be aware of as they work to stay in line with privacy rules, such as the California Consumer Privacy Act (CCPA), or the European Union’s General Data Protection Regulation (GDPR) and otherwise manage reputation risk.

 

Fracturing AI initiatives with Data Mismanagement

If these various data management and governance issues are not addressed early on, deeper issues could emerge later to fracture AI initiatives. AI technology providers can play a role in supporting businesses to navigate shortcomings related to data practices by:

  • Aligning data strategy with business outcomes, working with adopter organizations to clearly understand the business needs, the AI business case, and data management needs
  • Bringing a multidisciplinary team comprising AI/technical, business, regulatory, and domain specialists to establish data practices and strategy that align with the customer organization’s business goals and outcomes
  • Building a scalable data-based AI solution, considering all potential end users (employees, customers, business partners) of AI systems, and the overall IT infrastructure (on-premise, cloud, proprietary IT, open-source)

These steps can enable the adopter organizations to develop a holistic data-based AI strategy that scales with and adapts to their changing needs and demands.