The significance of high-quality data in the success of retail AI
Retail industry executives have recently concluded a period of increased production, preparation, and administration of heightened demand, which is indicative of the peak holiday season. During these periods, technology updates or implementation processes may have been neglected.
It is imperative to adopt and implement six critical data strategies to guarantee the success of the new technology, not only for the overall business but also to enhance the customer experience, in order to reestablish these projects in the new year, particularly if these technological enhancements involve AI.
Revenue potential of artificial intelligence
The value of AI to retailers is undeniable, as well-known consulting firms anticipate that it will reach billions or trillions of dollars within the next decade.
For instance, McKinsey anticipates that generative AI will generate an economic value of $240 billion to $390 billion. When combined with the value of nongenerative AI and analytics, billions could be transformed into trillions.
Retailers have the potential to capitalize on a portion of the anticipated revenue by establishing an effective AI model through the implementation of the following six AI strategies.
1. Recognize data anomalies
The success of AI is directly correlated with the data it is provided. Therefore, if your data is segregated, disparate, or obsolete, your AI outputs will be flawed.
Consequently, it is imperative that retail technology executives allocate sufficient time to identify and investigate the causes of data anomalies, which are the irregular or unanticipated patterns in a dataset that deviate from the anticipated norms and patterns.
This can be accomplished using a variety of techniques, such as statistical methods such as hypothesis testing and Z-score, machine learning methods such as autoencoders or isolation forests, or clustering-based methods like DBSCAN or local outlier factor.
2. Automate the process of data cleansing
Subsequently, guarantee that the data is free of errors. For instance, a retailer’s sales and marketing endeavors may be hindered by obsolete email addresses, misspelled names, and duplicate contacts. A reliable, automated data purification strategy guarantees that the data is reliable and accurate for the AI model to extract patterns from.
In accordance with the marketing example, these teams are able to automate advertisements and product recommendations by utilizing AI-powered tools that are based on previous sales and consumer data. Brand engagement and loyalty are enhanced when this is executed with precision.
3. Evaluate the integrity of data metrics.
Define data quality metrics such as completeness, accuracy, consistency, timeliness, and validity in advance of the adoption of AI, and perpetually monitor their quality.
Regular audits are conducted to guarantee the quality of data and to proactively identify any quality issues that may impede AI performance before they become a problem.
4. Execute data governance initiatives
Data governance is a critical component of a comprehensive data management strategy, as it establishes the rules and responsibilities for its use. Data inconsistencies in company-wide systems may remain unresolved in the absence of effective data governance, which could impede your data integration endeavors.
Data governance is the process of establishing data quality standards that your teams must adhere to in order to adhere to governance regulations. This may entail the development of a data dictionary, the establishment of data retention and deletion policies, or the specification of requirements for accuracy and completeness.
This is particularly critical when employing customer data. If AI is employed improperly, it may result in substantial repercussions, such as data exposure, litigation, and reduced consumer loyalty. All of which have a significant impact on the retailer’s financial performance.
5. Enhance the confidentiality of data
It is essential to secure the data used to train AI models, as AI systems process and store enormous quantities of data. Limiting access controls, encrypting data, employing firewalls to prevent unauthorized network access, routinely storing up data, and educating employees on security best practices are among the numerous methods available to achieve this.
Human error is responsible for 22% of all data intrusions, as per IBM’s Cost of a Data Breach Report. Data intrusions can lead to legal and financial penalties and ramifications, as well as a negative impact on reputations. Class action lawsuits comprise this category. Data intrusions can result in customer attrition and lost business, as consumers who have had their data compromised frequently elect to conduct their retail business elsewhere.
6. Standardize the data.
Lastly, the training of machine learning models is contingent upon the collection, formatting, storage, and exchange of consistent data from a variety of sources.
Data cleansing, data governance, data normalization, and transformation are among the most frequently employed data standardization methods. Normalization involves the organization of data into a structured format and the elimination of redundancies, whereas data transformation encompasses practices such as the consistent formatting of dates or the use of lowercase text for all text.
AI that is fueled by superior data
Retailers who are in the initial stages of their AI endeavors should be interested in implementing these six data strategies to enhance their business operations, consumer experiences, and outputs.
Although retailers may have neglected AI priorities in order to meet the demands of the holiday season. It is now imperative to reestablish AI programs with datasets that have been adequately prepared.