To get tech projects back on track in the new year, especially if they're incorporating AI, it's critical to adopt and implement six key data strategies to ensure the new tech is successful.
Peak holiday season means retail industry leaders have just finalized a period of ramped-up production, preparation and management of heightened demand. Technology updates or implementation processes may have fallen to the wayside during these times.
To get these projects back on track in the new year, especially if these tech upgrades are incorporating AI, it's critical to adopt and implement six key data strategies to ensure the new tech is successful, not only for the overall business but to improve customer experience.
AI's benefit to retailers is undeniable, as well-known consulting firms predict its value will reach billions or trillions of dollars within the next decade.
For example, McKinsey suggests generative AI is expected to unlock between $240 billion and $390 billion in economic value. When merged with the value of nongenerative AI and analytics, billions could turn into trillions.
By adopting and implementing the following six AI strategies, retailers can build an effective AI model and potentially tap into a portion of this projected revenue.
AI success is directly dependent on the data that the tool is fed, so if your data is siloed, disparate or outdated, your AI outputs will be flawed.
Therefore, retail tech leaders must take the time to detect and explore the reasons behind data anomalies -- the irregular or unexpected patterns in a dataset that don't conform to expected norms and patterns.
This can be done with a few different techniques, including statistical methods like Z-score and hypothesis testing, machine learning methods like isolation forests or autoencoders, or clustering-based methods like DBSCAN or local outlier factor.
Next, ensure that data is error-free. For example, misspelled names, outdated email addresses and duplicate contacts can impede a retailer's sales and marketing efforts. A solid, automated data cleansing strategy ensures data is accurate and trustworthy for the AI model to derive patterns from.
Following along the marketing example, accurate data empowers these teams to leverage AI-powered tools to automate ads and product recommendations based on previous sales and customer data. When this is done accurately, brand engagement and loyalty increase.
Define data quality metrics like completeness, accuracy, consistency, timeliness and validity ahead of AI adoption, and monitor that quality continuously.
Conducting regular audits ensures data quality as well as proactively identifies any quality issues that may hinder AI performance before they become a problem.
Data governance - the rules and roles of the how it's used - is key to an overall data management strategy. Without effective data governance, data inconsistencies in company-wide systems might go unresolved, hampering your data integration efforts.
Practicing data governance involves developing data quality standards so your teams have governance rules to follow. This might involve creating a data dictionary, specifying requirements for accuracy and completeness or establishing data retention and deletion policies.
This is especially important when leveraging customer data. If used inappropriately by AI, significant consequences may occur including lawsuits, data exposure, diminished customer loyalty. All of which have severe impacts on the retailer's bottom line.
AI systems process and store vast amounts of data, so it's imperative you secure the data used to train AI models. There are many methods to do this, but consider limiting access controls, encrypting data, using firewalls to prevent unauthorized network access, backing up data regularly and educating employees on security best practices.
According to IBM's Cost of a Data Breach Report, 22% of all breaches are due to human error. Data breaches adversely affect reputations and can result in legal and financial penalties and repercussions. This includes class action lawsuits. Data breaches can lead to lost business and customer churn because consumers who've had their data compromised often choose to take their retail business elsewhere.
Finally, consistent data is essential for training machine learning models and involves rules to collect, format, store and exchange data across diverse sources.
Some of the common data standardization techniques include data cleaning and data governance, along with data normalization and transformation. Normalization refers to organizing data into a structured format and eliminating redundancies, while data transformation refers to practices like having dates consistently formatted or all text being lowercase.
Retailers in the beginning phases of their AI journeys should be keen to adopt these six data strategies, both for improved outputs, business operations and customer experiences.
While retailers may have sidestepped AI priorities for peak-season holiday demands. Now is the time to get AI programs back on track with adequately prepared datasets.