7 Steps to Managing Data in the AI ​​Age

7 Steps to Managing Data in the AI ​​Age 2
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According to Gartner, AI will generate 10 percent of all new data in 2025. This statistic has important implications for business leaders in the digital age.

First, it points to another important development: Overall Along with advanced AI and machine learning (ML) tools, data generation will skyrocket. Statista predicts that humans will create, process and consume 180 zettabytes of data in 2025, a nearly 300 percent increase from 2020.

Additionally, large-scale AI data generation can negatively impact data quality — so even if business leaders understand their data, it's not necessarily accurate or usable. We've already seen these problems arise sporadically with creative AI “hallucinations,” which fabricate data points or statistics. Now, imagine the risks an organization could face if these deceptions were replicated on an exponential scale and across all systems.

In this complex data landscape, it is imperative that leaders perform an effective data health check. Doing so is the first step to organizing and managing business data sustainably.

What is a Data Health Check?

Simply put, a data health check assesses the accuracy, consistency, and reliability of an organization's data. It does this by answering questions such as:

  • Does master data contain duplicate entries?
  • Is the data format consistent across records (eg, date formats, numeric formats)?
  • Are relationships between data objects correctly represented (for example, parent-child relationships)?
  • Are there any broken links or references between data objects?
  • Is the data structured in a way that supports business processes?

Business-specific standards for data health will vary. For example, some industries require more timely data than others. Thus, these businesses' data health checks will prioritize the hyper-timeline as the default “expiration period” (eg, “data older than three months = expired”).

Data health testing is critical to AI strategy and financial health. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually. With so much money on the line, leaders should prioritize starting an audit as soon as possible.

How to audit your data starting today

We will break down the data audit into seven distinct steps.

1. Check the database configuration.

An organization's database includes all systems for managing and storing business data (for example, a master data management (MDM) solution). This repository should include all types of data, from transactional and reference data to metadata.

Start your data health check by verifying that your database is configured correctly. Ensure that the buffer pools or caches are large enough to handle the workload efficiently. Additionally, verify that the table and index layouts match the default schema.

2. Verify the schema.

Verify that all modifications conform to business requirements regarding data integrity and consistency. This process includes checking for addition, deletion or change of columns, data types, constraints and indexes. Ensure all changes are documented and reviewed to comply with your data governance policies.

3. Update table and index statistics.

Update database table and index statistics. Doing so will enable the database optimizer to choose the most efficient access plans for SQL queries, improving query performance.

4. Delete the old version.

Implement a policy to regularly delete older versions of data to prevent performance degradation. Accumulating obsolete data can lead to increased storage costs, slower query performance and higher maintenance overhead.

5. Ensure high cash hit ratios

Monitor and optimize cache hit ratios to ensure database queries are served from cache instead of disk (when possible). This step reduces latency and improves overall system performance.

6. Leverage AI

AI and ML are powerful tools for automating the data health check process. AI can help with data cleansing, validation and anomaly detection. It can identify patterns and trends that the human eye might miss.

7. Measure data health.

Once you've completed steps 1-6, your organization's database should be cleaner and healthier. Nevertheless, it is important to verify the success of your data health campaign.

Develop metrics to measure the health of your data, focusing on aspects such as completeness, consistency, accuracy, timeliness, and accuracy. Create dashboards and reports that provide visibility into these metrics, allowing all stakeholders to track data quality over time.

Does your organization's data pass the stress test?

During a data health check, you will identify several weaknesses in your organization's data strategy. That's normal, especially considering that 52 percent of all business data is “dark,” or unclassified and unusable. I suggest? Use what you've learned to fine-tune your data strategy instead of waiting for data proliferation to get worse in the coming years.

And, if reviewing data audit initiatives hasn't helped, it's time to hire a data leader or consult with an MDM provider about what your organization can do to improve data quality and effectiveness. Can take steps. Doing so today will set your organization up for success down the road.

Most importantly, AI is advancing and data is proliferating. Organizations will struggle to catch up. I hope that advancing data intelligence will enable leaders to keep pace with the digital age — but only if they create the right data management policies.

Image credit: monsite/depositphotos.com

Steven Lynn Semarchy has a Product Marketing Manager, responsible for executing the go-to-market strategy for an award-winning data company. Prior to joining Cimarche, Steven was a technology strategy consultant at Ernst & Young advising on large-scale data initiatives for global and Fortune 500 firms. He holds a BS in Marketing and Tech Management and a Masters in Information Systems from the Indiana University – Kelly School of Business.

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