As a growing number of companies are looking to build out and leverage artificial intelligence solutions across their organization, they’re often delayed due to poor data quality that exist across their business operations. This quality deficiency prevents them from proceeding with their intended AI rollout. Once AI is fully implemented, it can improve data quality throughout a company.
Being faced with data quality issues forces a company to shift priorities and resources from implementing AI to fixing these quality shortcomings before they can proceed. This means extensive time delays, allocation of resources, and a slow draining of the AI budget.
The magnitude of this problem is multiplied by the number of data sources a company possesses, and even more so when you consider the ever-growing volume of new data pouring in. By some measures, the amount of new data available to a company doubles every two years. Having an abundance of data is a strategic advantage and should be looked upon in this manner, and a data infrastructure should be in place to support all of it. One sure way to take advantage of all this data is with the application of AI.
To help address this challenge of having data quality in place that is suitable for AI, organizations should look to appropriately design or redesign its underlying data architecture. Following are a few key steps that can be taken.
1. Consolidate your data
Consolidating and integrating all corporate data into a centralized data hub provides a single platform for all data. This helps ensure one version of the truth and a consistent data home for all users throughout an organization regardless of department.
2. Connect your data
Having connectivity and a data exchange enables the retrieval of data in its raw form before cleansing. This allows for single connectivity to all data sources now and into the future.
3. Use a modern data warehouse
By using a modern data warehouse (MDW), users can modify and enrich their data so that data issues can be resolved once. Data that resides in various systems can be rationalized and after rationalization, golden records can be created. And with an MDW, historical data can be preserved.
4. Consider a semantic layer
Within a data mart or what some call a semantic layer, data can be governed and prepared for any visualization tool. In addition, with a shared data model, all users can see the same data regardless of which data visualization tool is being used.
5. Deploy all-in-one data management software
Too many organizations have complicated systems in place which can obscure data management and can lead to data quality issues. Having numerous tools requires extensive management and coordination to ensure synchronization among all the various tools. One method recommended to fix this is to use an all-encompassing data management platform that eliminates the usage of multiple discrete tools.
6. Automate data management procedures
With an automated data management platform, time-consuming, hand-written code is eliminated in favor of automation. This eliminates the time needed for manual coding – which improves quality. It also frees up time to work on other issues regarding data quality and ultimately the bigger AI program.
AI, once fully in place after data quality is secured, can also improve the quality of future data. With an established AI program, the inherent intelligence in AI can be used to automate the gathering and collecting of needed data and automatically enter the data – removing the need for manual data entry. And when you remove manual tasks you generally improve data quality. AI can also identify data errors and anomalies, remove duplicate or outdated records, and identify third-party data sources that can provide value related to the data model.
With all this in mind, desiring to have AI within an enterprise is certainly a worthy cause, but it starts with having solid data quality across the enterprise. And with AI in place, not only can organizations enjoy all the benefits of AI such as predicting trends, identifying new opportunities, and answering tough business questions, but they can also be assured that improvement of the level of quality of their future data will transpire as well.
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