Your AI Initiatives Depend on Getting This Part Right

As organizations pursue the transformative power of AI, many of them may overlook or minimize the significance of a critical factor: the quality and organization of the data fueling these intelligent systems. High-quality data isn't just beneficial; it’s essential. AI initiatives falter without the proper data foundation, producing unreliable and misleading results. The better the information we use to train AI systems, the better the answers these algorithms give us.

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Define Your AI Strategy and Data Needs

Before diving into data preparation, you need a clear vision of what you want to achieve with AI. Think of your AI strategy as a roadmap guiding your journey through the complex terrain of data and algorithms. Just as a well-planned road trip requires a clear destination and planned stops, your AI strategy needs defined goals and milestones. These include target implementation dates, ROI and usage frequency of the tool, for example.

Your AI strategy should align with your business objectives and outline the specific problems you aim to solve, as well as any opportunities you want to seize. For example, if an organization seeks to improve call center agent efficiency, the target milestones may look like this:

• Implement AI-powered agent assist tool as a pilot within three months.
• Conduct initial agent training with bi-weekly check-ins for continuous support.
• Reduce agent talk time by 10 percent within the first six months.
• Improve CSAT scores by three points within the first year.
• Reduce agent attrition by 20 percent within the first year.
• Achieve 200 percent ROI within the first two years, with payback under eight months.

Once you’ve defined the milestones and strategy, you’ll need to assess your data needs. Take stock of your current data assets to identify gaps. Consider the following elements.

Size

How much data is readily available? What timeline does the data span, and does it provide a holistic view for the AI tool to begin learning from?

Diversity

Are there enough different data sources to give a complete picture? Have previously untapped data sources been included, such as customer data, partner data, etc.?

Velocity

How quickly is the provided data created and processed? Is the data still relevant by the time the AI tool processes it? How can data velocity be sped up, if needed?

Think of this process as conducting a comprehensive inventory audit for your AI initiative. Just as a business must understand its inventory to meet customer demand, your AI project requires a clear comprehension of the data resources needed to meet its objectives. You need more accurate or better-quality data to create effective AI models. Therefore:

• Clearly articulate your AI project objectives.
• Identify the types and volume of data required.
• Assess current data assets and identify gaps.

Establish a Robust Data Governance Framework

Think of data governance as the constitution for your data republic, laying down the fundamental principles that ensure consistency, security and compliance. Your governance framework sets the rules, defines roles and responsibilities, and ensures that everyone across your organization manages data consistently and securely. Without proper governance, you risk data chaos — inconsistent outputs, unclear ownership and potential compliance issues.

For example, in a call center, the agents may each have access to the data sets, but only those in managerial or executive roles may have the ability to manipulate or revise these sets. Governance strengthens data accuracy and consistency because fewer people can change data sets. Further, data governance helps teams avoid compliance issues, such as violations of the General Data Protection Regulation (GPDR).

A robust framework starts with comprehensive policies covering the entire data lifecycle, from collection and storage to processing and sharing. Designate data stewards for different data domains to ensure data quality and compliance for different domains. These stewards will be responsible for bridging technical and business teams and ensuring that data serves the needs of the entire organization. Their roles should include developing comprehensive data governance policies and creating a data catalog to document sources and usage.

Ensure Data Quality and Relevance

Data quality in AI implementation is akin to having accurate customer information in a CRM system. Imagine trying to run a marketing campaign with outdated or incomplete contact details. AI projects with poor data quality face similar hurdles, leading to unreliable and misleading insights. 

Start by defining clear data quality metrics that align with your AI objectives, much like how you’d establish key performance indicators (KPIs) for your CRM. If an AI tool helps contact center agents increase efficiency, you might track the completeness of customer interaction history, average call time, hold time and call demand. Without these metrics, the data sets being input into the AI tool are incomplete, and the tool cannot provide accurate, well-informed output.

To ensure high-quality data for your AI initiatives, establish a comprehensive data quality pipeline that includes:

1. Data Profiling 

Perform an in-depth examination of your data sets. Employ statistical techniques like mean, minimum, maximum and frequency to comprehend data patterns, spot outliers and uncover anomalies. This process provides a comprehensive view of your data’s current condition and highlights potential problems. The AI tool will understandably benefit from finding these patterns or problems early.

2. Data Cleansing

Correct common data issues manually or through machine learning-powered automation software to ensure the most accurate data sets. This should  incorporate standardizing formats, removing duplicates and handling missing values through appropriate imputation techniques.

3. Data Validation

To ensure data integrity, cross-reference different data sources, validate against predefined value ranges and make sure there’s relational integrity between various data elements. This helps to confirm accuracy and consistency across multiple data sources.

4. Manual Review

Schedule regular audits by domain experts to catch nuanced issues that automated systems might miss, such as AI lacking contextual understanding of the data. This is particularly important for complex data sets or those that deal with niche industry-specific information, like contact center data.

5. Continuous Monitoring

Implement real-time data quality checks within your data pipelines. Set up alerts for when data quality metrics fall below defined thresholds. This helps identify and resolve issues and maintain ongoing reliability of your AI systems.

Maintaining data quality is an ongoing process. Reassess your data quality metrics regularly and adjust processes as your AI initiatives evolve. As your AI models interact with new data, be prepared to refine your data quality processes to address emerging patterns or challenges. 

• Establishing clear, AI-objective-aligned data quality metrics.
• Implementing a multi-step data quality pipeline with automated and manual checks.
• Monitoring your data pipeline and reassessing data quality processes regularly.

Address Missing Data

Missing data can lead to biased or inaccurate AI models. For example, three days’ worth of call volume data is lost or accidentally deleted, AI could deliver skewed results on ways to increase call center agent efficiency. Therefore, developing a strategy to handle missing values is essential for achieving successful AI outcomes. 

Analyze the extent and pattern of missing data in your data sets. Does it appear to be random, or is there a systemic reason for gaps? The answer to this question will guide your approach. You might proceed by excluding rows with missing values. Alternatively, you could try using statistical methods, like replacing the data with the mean or median of the set, to estimate (impute) missing values. Whatever method you choose, document your approach within an internal team document or regularly communicated channel for transparency so other teammates can reproduce this same method if needed again. 

• Analyze the extent and pattern of missing data.
• Choose appropriate methods for handling missing values.
• Document your approach to missing data handling.

Implement Strong Data Privacy and Security Measures

In today’s landscape of frequent data breaches and strict privacy regulations, safeguarding sensitive information is crucial for maintaining trust and compliance. In the realm of call centers, this data is often even more of interest to bad actors as it may contain personal information like customer names, addresses, payment information and more.  Think of data privacy and security as the vault and alarm system for your most valuable asset: data.

Create a thorough data privacy policy that specifies the collection, usage and protection of data. Enforce strong security measures, including encryption for at-rest and in-transit data. Use anonymization techniques like:

• K-anonymity: Generalizing or masking data or replacing personally identifiable information with a pseudonym.
• Differential privacy: Anonymizing a full data set to ensure that individual-level information cannot be leaked.

Both methods protect individual identities in large datasets. Update security protocols regularly to counter emerging threats and perform periodic privacy-impact assessments to ensure compliance with regulations like GDPR or the California Consumer Privacy Act (CCPA). Also:

• Develop a comprehensive data privacy policy.
• Implement encryption and anonymization techniques.
• Conduct regular privacy impact assessments.

Mitigate Bias in Your Data

Bias in AI systems is like a virus — if you don’t cure it, it can get out of control and lead to disaster. Imagine if an AI-powered training tool to help call center agents learn how to handle various customer situations had biases about the various types of people who agents may encounter. 

Addressing bias at the data level is crucial for developing fair and ethical AI. Biased data can lead to discriminatory outcomes, eroding trust in your AI systems and potentially causing reputational damage.

Take a close look at your data sets. Look for underrepresentation or overrepresentation of certain groups, or any data that may stand out as unusual compared to other categories. Implement bias mitigation strategies. One is re-sampling, which involves taking new or additional samples to cross-check against existing samples to identify and eliminate bias. Consider also re-weighting, which balances the contributions of under- and over-represented subgroups in your data sets. Remember, bias mitigation is an ongoing process — regularly update and diversify your datasets to reflect changing realities.

• Use bias detection tools to identify issues in datasets.
• Implement bias mitigation strategies.
• Ensure diversity in data sources.

Foster a Data-Quality Culture

When an entire organization shares responsibility for data quality, it significantly enhances the effectiveness and reliability of AI systems. To round out our call center agent efficiency example, when that call center has the AI-tool in place and remains data quality-conscious, it’s proactively ensuring the most accurate and reliable data for its system. In turn, this leads to the most valuable outputs. 

Cultivate a winning culture by providing comprehensive, ongoing training on data quality best practices for all employees. This should cover the basics of data management, the importance of data quality in AI and specific techniques for maintaining high-quality data in their respective roles.

Establish clear KPIs related to data quality (data accuracy rates, completeness of critical fields, timeliness of data updates) and integrate them into performance evaluations. This will reinforce the importance of data quality across the entire organization. 

Implement a system to encourage and reward employees for identifying and addressing data quality issues. This could involve creating a program that encourages employees to submit suggestions or report issues. Acknowledge and reward employees who make notable contributions to data quality improvement.

Establish a regular cadence of communication on the impact of data quality on AI initiatives. Share success stories where high-quality data leads to valuable insights or improved AI performance. Conversely, use examples of how poor data quality hinders AI effectiveness to illustrate the importance of maintaining data integrity. 

• Providing comprehensive training on data quality best practices.
• Establishing data quality KPIs and integrate them into performance evaluations.
• Implementing a reward system for contributions to data quality improvement.
• Regularly communicating the impact of data quality on AI success.

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Laying a Solid Foundation for Success

Every successful AI initiative is a journey that begins with clean, organized and usable data. By focusing on these critical areas — from strategy and governance to quality, privacy and culture — you will set your organization up for long-term success in the more intensively data-driven future that lies ahead. The effort you invest in data quality in the critical initial phase will pay dividends down the road in the form of more accurate insights, increased efficiency, and greater trust in your AI systems.