It’s essential for retailers to ensure data accessibility. This term referes to how easily staff and thirdparty partners like agencies and media platforms can access and use data to make decisions affecting business performance and growth.
Having accessible data is critical when making data-driven decisions. Retailers therefore need to ensure that their staff and third party partners have access to the right data and tools to analyze it effectively. Data analytics can only be successfully implemented if the organization supports all its employees with effective platforms.
Many retailers today, including large retailers, often struggle to utilize data that drives innovation, despite making significant investments. This is because the data does not reach the intended audience, or is difficult to access, and employees are left to unravel complex data without the right tools, leading to false conclusions based on limited data sources. Remember, data is not limited to numbers and text – data also include all the creative assets that retailers invest major sums of money to generate for their brands (logos, pictos, product shots, ambience/scenery, etc.) – all this needs to be accessible to rendering engines and platforms to complement the pure ‘code’ data that most marketers associate with automation.
Data security also comes into play when it comes to data accessibility. It can be difficult to keep data access secure when immense amounts of data are being stored and processed daily. To address this issue, data stakeholders such as data governance and security ops need to be educated on which data is sensitive and which is not. Security policies need to be created or reformed to apply to a situation where there higher data accessibility is required. For example, analysts may need to access customer data, but in an anonymized way, such as by applying dynamic data masking.
Complying with various privacy laws can pose a threat to data analytics as well. Processing sensitive data under GDPR, CCPA, PIPEDA, HIPAA, and other privacy and security regulations requires anonymization, setting security policies, and allocating precious data engineering time. It may also necessitate implementing specific views or spinning up new ETL/ELT processes. In order to keep up with modern data analytics systems, you need to hire competent technical staff with proven track records in data analysis, bridging the talent gap. All of these can be complex and costly endeavors to implement.
Addressing the challenges of data security and complying with various privacy laws requires an understanding of what data is sensitive, and which is less sensitive, and creating appropriate security policies.
ARISTID’s Omnipublish omnichannel communication platform centralizes and harmonizes all of the necessary information for your commercial publications, making data accessible and digestible and letting you leverage all your data to make the right business decisions.