c-suite

Getting your organization ready for AI:
managing data the right way

insight
July 17, 2024
9 min read

Play the podcast adaptation

Author

Eugen Eugen Rosenfeld
A CTO & a Solution Architect in Life Sciences at Nagarro. He has more than 20 years experience in different programming languages, technologies and business domains.

In today's digital landscape, companies are eager to tap into AI's potential. AI can automate tasks, increase the life span of devices, forecast financial results or customer behavior, and offer new ways to do business. One key factor that can make or break AI projects is the quality of data.

This article explores why data quality matters and how to improve data collection and processing to prepare your organization for AI adoption. Let's dive in to understand how good data practices make successful AI integration, by understanding data quality and its impact on AI.

Impact of data quality on AI implementation

High-quality data is important for AI implementation to work well. It's like the solid ground that AI stands on. With good data, AI can give us useful information, make trustworthy predictions, and positively help businesses and society. The volume and quality of data can affect the performance of any AI project. Some of the most important reasons are:

Accuracy

AI/ML models rely on accurate data to make relevant classifications, predictions, and decisions. Poor-quality data, such as incomplete or erroneous information, can lead to inaccurate outputs: garbage in – garbage out.

Reliability

High-quality data ensures the reliability and consistency of AI/ML outcomes over time. Consistent, reliable data enables the models to produce good results, getting trust among users and stakeholders.

Generalization

AI/ML models trained on high-quality data can generalize patterns and trends to new, unseen data with greater accuracy. Poor quality data can lead to overfitted or underfitted models.

Performance

High-quality data ensures that AI models perform optimally. Clean, well-organized data allows algorithms to learn more effectively, leading to better performance in tasks such as classification, prediction, and decision-making.

Bias mitigation

Data quality efforts can help mitigate biases inherent in datasets, which can affect AI outcomes and perpetuate discrimination. By ensuring representative and unbiased data, organizations can build fairer and more equitable AI systems.

Cost and efficiency

Poor-quality data can lead to inefficiencies and increased costs in AI development and deployment. Cleaning and preprocessing low-quality data consume resources and time, delaying project timelines and increasing expenses.

User experience

Quality data underpins user experience in AI-driven applications. Accurate and relevant recommendations, personalized content, and responsive interactions rely on high-quality data inputs, enhancing user satisfaction and engagement.

Data quality assessment for reliable AI

Having a good understanding of how data can impact the performance of AI models will also help us understand how data quality can be assessed. Some of the most important attributes are:

Accuracy

Accuracy measures the correctness of data values compared to the true or expected values. It involves verifying that data is free from errors, inconsistencies, or inaccuracies.

Completeness

Completeness evaluates whether all necessary data is present and available. It involves assessing if any data is missing or incomplete, which can impact the reliability and validity of analyses or predictions.

Consistency

Consistency examines the coherence and harmony of data across different sources, systems, or time periods. It ensures that data elements maintain uniformity and conformity to predefined standards.

Timeliness

Timeliness assesses the relevance and currency of data in relation to its intended use. It involves examining whether data is up-to-date and available in a timely manner to support decision-making processes.

Validity

Validity measures the extent to which data accurately represents the concepts or phenomena it purports to measure. It involves assessing whether data is relevant, appropriate, and aligned with the objectives of the analysis or application.

Integrity

Integrity evaluates the trustworthiness and reliability of data throughout its lifecycle. It involves ensuring that data remains secure, confidential, and free from unauthorized modifications or tampering.

Precision

Precision assesses the level of detail or granularity present in data values. It involves determining the degree of exactness or specificity with which data is represented, which can impact the accuracy and usefulness of analyses or interpretations.

Relevance

Relevance examines the significance and applicability of data to the objectives or requirements of a particular task or context. It involves assessing whether data is pertinent, meaningful, and valuable for informing decision-making processes or achieving desired outcomes.

We can measure the importance of data quality parameters in respect to impact on AI performance using a matrix (H = high, M = medium, L = low). However, this matrix can differ from one AI model to another. I would highly recommend to create your own matrix based on models KPIs:

How to measure data quality

Understanding data processing for AI adoption

An important aspect that impacts the performance of an AI model is the process of data preparation. Usually, the process of designing, implementing, and deploying an AI model is encompassing few important phases:

How to build an AI model

Data engineering phase and activities

Data collection

Data cleaning

Transformation & normalization

Feature selection & extraction

It has an important role as this is the start of the process. This activity is a long-running one, many times spanning over multiple years. Data collection can happen coincidentally or proactively.

When coincidentally, the quality of it might not be good enough for a very specific use-case. In this case, we recommend not to ignore the accumulated data but to evaluate what other use-cases can enable.

Proactivity happens when we have a specific use-case in mind and then we start to collect the data to implement it. In this case, we should be fine with the quality but still, poor analysis or low domain knowledge will have a big impact on the final performance. Also, no, or bad implemented organizational data processes can bring us to failure as delays and poor integrations might get our project outdated or too expensive to be still relevant.

It is an important activity when both coincidental and proactive approaches to data collection are happening. Data cleaning ensures that data is structured according to the use-case’s needs. Also, it ensures data consistency.

It is an activity where data is prepared to be more suitable for analysis and modeling. During this activity data gets standardized and prepared for features selection & extraction.

It is very important for model training. During this activity, features (parts of data with the highest impact on the problem definition) are identified, extracted, and stored for model training. To cover these activities in an efficient way resulting in good quality data, the organization must have in place a good maturity level regarding data collection and management.

To cover these activities in an efficient way resulting in good quality data, the organization must have in place a good maturity level regarding data collection and management.

Maturity level in data collection and data quality

To assess the maturity level of an organization regarding data collection and data quality, you can consider various parameters across different dimensions. Here are some key parameters indicating maturity in data collection and data quality:

Data governance:

Presence of established data governance policies, frameworks, and procedures.
Clarity of roles and responsibilities for data management and oversight.
Implementation of data accountability mechanisms.

Data management processes:

Adoption of standardized processes for data collection, storage, and management.
Integration of data from diverse sources into centralized repositories or data lakes.
Implementation of data lifecycle management practices, including data retention and archiving.

Data quality standards & metrics:

Establishment of data quality standards and metrics to assess the accuracy, completeness, consistency, and timeliness of data.
Regular monitoring and measurement of data quality using defined metrics and key performance indicators (KPIs).
Implementation of data quality assurance processes and corrective actions to address issues and discrepancies.

Data acquisition strategies:

Adoption of systematic approaches for data acquisition, including data sourcing, extraction, transformation, and loading (ETL).
Utilization of automated tools and technologies for data ingestion and integration.
Engagement with external data providers or partners to enrich and augment internal datasets.

Data integration & interoperability:

Implementation of data integration solutions and middleware to facilitate seamless data exchange and interoperability.
Alignment of data schemas, formats, and standards to enable interoperability between disparate systems and data sources.
Adoption of APIs and data connectors to integrate data from various applications and platforms.

Data quality control and assurance:

Implementation of data quality control measures, such as data profiling, cleansing, and validation.
Deployment of data quality tools and technologies to detect and correct errors, anomalies, and inconsistencies.
Establishment of data quality monitoring processes to track the performance and reliability of data over time.

Organizational culture & awareness:

Cultivation of a data-driven culture that emphasizes the importance of data quality and integrity.
Awareness and training programs to educate employees about data collection best practices, quality standards, and governance policies.
Integration of data quality considerations into decision-making processes and business workflows.

Understanding and evaluating these parameters, we would like to propose the following metrics that can depict the organization maturity level:

Metrics to measure data maturity level for AI

As we have seen, data is the fuel that drives the potential of AI. But it's also a responsibility. As we navigate this exciting technological landscape, we shouldn't only focus on the "how" of AI implementation, but also the "why". How can we use AI in an ethical and responsible way? What kind of future do we want to create with this powerful tool? The discussion about data management in the context of AI has only just begun. Let's continue this important dialogue.

Listen to the podcast version of the blog.

This podcast is AI-generated using Google Notebook LM, transforming the article into an audio experience.

Get your organizational data ready for AI

Digital Engineering

Intelligent Enterprise

Experience and Design