For schools in the early phases of their AI journey, it’s important that IT leaders prioritize creating a strong foundation for the school’s data maintenance before diving headfirst into this new frontier. This extra step of ensuring data is ready for AI directly impacts the accuracy and reliability of any insights derived from it. AI-ready data allows district and school staff to make informed decisions about the technology and avoid costly implementation errors.
The following six steps are critical to ensuring a school’s data infrastructure is ready for AI:
1. Detect data anomalies
Identifying anomalies in a data set can help improve the quality of AI-generated outputs, which will directly affect the success of the involved AI tools and impact faculty and students.
There are three main strategies for anomaly detection, depending on the types of data being leveraged, the resources and how they are distributed. These strategies include:
- Statistical: Z-score evaluation, interquartile range calculation or testing hypotheses.
- Machine learning: One-class support vector machines, isolation forests or neural network autoencoders.
- Clustering: DBSCAN, K-means clustering or local outlier factor.
2. Automate your data cleansing
Removing inaccurate or duplicate data is also critical to ensuring peak AI performance. Automating the process streamlines the ongoing management and analysis and confirms that data is trustworthy and usable without hours of manual cleaning.
If the data is flawed or incomplete, the AI model may recognize incorrect patterns, decreasing accuracy and causing greater error rates. Clean data also minimizes the computational resources required for training an AI tool, allowing it to operate without filtering out noise or irrelevant details.
There are several methods for automating data cleansing:
- Apply data validation rules like regular expressions, range and consistency checks.
- Utilize data profiling methods like statistical and frequency analysis, along with data quality evaluations.
- Use data quality assessment tools, like specialized commercial software or open source libraries.
3. Continuously monitor data quality metrics
Beyond general data monitoring, it’s crucial to watch for any fluctuation in data quality metrics. Issues to check include data drift, anomalies and bias. Any of these factors can risk lowering the accuracy of an AI output.
First, IT leadership and staff should define the data quality metrics that the organization should monitor, such as accuracy, consistency, timeliness or validity. Then, they should profile the data to identify and resolve any inconsistencies. Finally, they should conduct consistent data audits and continuously check on their monitoring processes.
4. Practice data governance
Data governance — the process of managing the availability, usability and security of the campus’ data — aims to maintain high-quality data sets that are secure and readily available for IT staff. It also works to combat misuse and ensure compliance with privacy regulations like GDPR, CCPA and HIPAA.
To establish and enforce data governance, IT teams must create rules and definitions for data quality standards within the district or campus. Then, through the processes already discussed, these teams can monitor data flow and usage to govern data expectations.
5. Secure your data
As with any online activity, maintaining the confidentiality, integrity and availability of all data is imperative to protect student and staff information from unauthorized access or theft. Plus, compromised data could result in biased or inaccurate results from AI systems.
To avoid breaches as well as the severe financial and legal repercussions that commonly stem from cyber attacks, IT staff should implement proper data security measures, such as:
- Access restrictions: Regulate who can view or interact with the institution’s information according to defined roles and privileges.
- Encryption: Transform information into an unreadable format that can only be accessed by those with permission.
- Firewalls: Block unauthorized individuals from entering the institution’s networks.
- Frequent backups: Generate duplicate versions of data to guarantee restoration in the event of loss or corruption.
- Training: Provide staff with knowledge about optimal data protection practices as a preventative measure.
6. Data standardization
Finally, IT staff should establish rules for collecting, managing and organizing data so that it can be shared securely and seamlessly between systems. This could be defining rules for formatting, naming conventions, representing data uniformly or using standardization codes.
This can take many forms, such as:
- Data cleansing: Defining and fixing data problems, missing values and other data inconsistencies.
- Data normalization: Eliminating redundant data and organizing it into a structured format.
- Data transformation: Converting all data into a uniform format, such as “Male” or “M” for gender.
GETTING AI READY
AI continues to evolve by the minute, so it’s never too soon for school IT leaders to begin planning their next upgrades. With these six key data strategies, your campus data can be accurate, accessible and AI-ready.
Duane Barnes is president of RapidScale, a managed cloud services company.
