By Patrick Keuller; VP, Business Unit Operations & Strategy, Park Place Technologies
Data migrations present retailers with a huge opportunity for modernization, progression and strategic advances that can place them ahead in the customer-buying experience, gaining increased market share and adding brand value. But when data migrations go wrong, or when incumbent retail platforms fail to be optimized, they quickly become both under-performant and under threat from data breaches. Additional migratory pressures occur in retail settings because of the increasing volumes of data sets that need harvesting. Managing exponential data sets housed on systems that aren’t fit for today’s modern omni-channel needs, is fraught with data risks. Lagging systems invariably mean data loss. And when data loss occurs involving sensitive customer personal and financial information, retail brands pay heavily for reputational damage. Target, British Airways, Home Depot, Optus, and Under Armour are all examples of retailers that have publicly suffered data loss and subsequently compromised customer trust for not modernizing their data infrastructure.
So, effective data migrations remain an incredibly important strategic directive in this competitive sector, with new online retailers emerging daily, offering seamless, fast and personalized shopping experiences. To keep up, established retailers need to stay agile and adapt to changing consumer demands, using modernized data analytics driven platforms that give unified data management across departments to better organize, store and respond to customer buying behaviours. Using these insights in a unified format gives retailers an optimized data-driven understanding of their business that highlights flaws internally while enhancing shopping experiences externally, regardless of the retail channel. At the customer end, optimal usage of historical data can anticipate interactions and preferences, allowing personalised marketing and targeted promotions. Critical then that effective migration needs to deliver robust data quality management practices to ensure complete data integrity and reliability of data from disparate systems to avoid data clutter and disparity. Poor data quality leads to ineffective decision-making, customer dissatisfaction, and operational inefficiencies.
Data migrations also allow retailers to leverage emerging technologies, such as artificial intelligence (AI), machine learning (ML), or, in some settings, augmented reality (AR); providing options to create an immersive customer shopping experience backed by an optimized supply chain. Enhanced digital analytics will allow the delivery of pools of valuable data insights for trends, forecasting, and price optimization.
From an IT perspective, agility and scalability can also be achieved through data migrations, allowing retailers to expand rapidly to changing market conditions such as increasing data volumes for increased transactions with cloud-based flex. Additionally, the extra layers of security and compliance that these ecommerce platforms offer means that retailers can meet data protection and privacy regulations and minimise the risk of data breaches with regular platform updates. Data migration, when properly executed, also offers the opportunity for cleansing and validating existing customer data sets, decluttering and validating data before migration. This enables cleansed and regulatory GDPR compliant data only to be transferred and cuts down the size of the migration through obsolete record removal.
Before any data migration occurs, a plan that scopes all the data including the order of migration and likely risk factors should be agreed. This will involve a comprehensive data inventory that builds a complete understanding of the retailer’s data landscape to ensure that no critical data is overlooked during the migration process. It also helps in planning the migration strategy, assessing data quality, addressing security concerns, and maintaining compliance with relevant regulations. This data inventory will consider both the data sources (and these could come from anywhere within the organisation – e.g. CRMs, order management, payment gateways, inventory reports) alongside the data types that exist in the original (source) system. These can include customer data (personal information, purchase history), product data (descriptions, prices, SKUs), transaction data, shipping information, alongside any other data relevant to the retailer’s operations including metadata tagging. Once understood, the likely data volumes to be migrated then need to be anticipated.
For execution, the first step is to ensure that all data is backed up securely should unforeseen synchronization issues occur. This mitigation step is essential as data corruption, technical glitches and compatibility issues between systems and differing hardware can readily occur. Having such a backup and recovery plan is therefore imperative, as retailers rely on data for daily operations. The second pre-migration step involves the assessment of intended security measures in place for the new platform, noting security protocols, encryption capabilities, access controls and compliancy. For data in transit, only encrypted and secure protocol connections should be used. Finally, detailed sample testing of the migration process in a controlled sample environment should occur, proving data dependencies and verifying compatibility between systems. Only then should data begin migrating to the new retail platform.
Once in progress, continuous monitoring and validation of moving data at each stage of the migration plan is essential, so that project managers can check for inconsistencies. Once complete, further validation should occur, testing data residing on the new platform, comparing accuracy and completeness with the source system. This data integrity verification ensures data is accurate, complete, consistent and functioning as expected. If there are gaps, retailers have the chance to pinpoint and address issues. Once in place, usage of the new platform should provide a kick-start for retailers to maintain full data governance and maintenance, keeping data hygiene levels high to keep data integrity, security, and compliance in-check. When fully achieved, with data being the new gold, retailers can begin to monetise this asset to provide additional growth.