数据现代化：解锁业务增长的关键

As a retailer, you collect massive amounts of data every day, but how much value is it providing?Point-of-sale transactions, inventory levels, shipping and logistics, customer purchases and returns,social media activity, and website traffic all provide valuable information that — when managedproperly — is pivotal to your company’s growth and success. However, siloed data and outdatedlegacy systems hamper advanced analytics and prevent you from harnessing the full power of yourdata. Improving practices and strategies for data governance begins with assessing and improvingdata management facilities. This paves the way for improved business models and facilitates: Automationto standardize data anddirect it to a single repository Accessibilitythrough the cloud Scalabilityas you collect more data Efficiencyin data processing andanalytics Data-driven strategyto increaseoperational efficiency and customerunderstanding Cost optimizationthroughstreamlining processes To maintain a competitive advantage, you must embrace modernization and rethink the way yourcompany collects, organizes, stores, and secures your data. Key aspects of modernization includeintegrating and standardizing data from various sources and in various formats, creating a holisticview of your data, and updating infrastructure (including upgrading hardware, software, networks,and databases). All this leads to solutions that help your bottom line and attract customers, such as: Customer 360 –Analyze data fromevery touchpoint on the customerjourney to develop a complete picture ofyour customers for targeted marketingcampaigns and to improve engagement. Demand forecasting –Use statisticalmodels and ML algorithms to analyzesales data and customer behavior topredict future demand and optimizeinventory. Personalization –Leveragecustomer data to tailor messages,product recommendations, andshopping experiences to each user. Supply chain risk management –Combine predictive analytics withreal-time analytics to gain insightinto inventory levels, productionschedules, shipping status, and supplierperformance. In this white paper, you will learn the failings of antiquated data management practices, as well asthe challenges to modernizing data and how to overcome them. You will also discover solutions anduse cases that allow you to maximize the potential of your data. LIMITATIONS OF TRADITIONAL DATA MANAGEMENT METHODS As an established industry, retail has spent the past five decades building data managementsolutions. This is great, but it means that some of your technologies — such as mainframes,proprietary data warehouses, and on-premises Hadoop-based data lakes — are holding backdata-driven innovations in multiple ways, including: •An inability to accommodate modern use casessuch as real-time analytics. Data-driveninitiatives are roadblocked when you are unable to support them. •Licensing costdiminishes the cost-effectiveness of new data strategies. •Scale-up cost and timingare prohibitive. Scaling up an on-premises data warehouse or datalake often becomes a project by itself, adding months to the timeline of any project, as well asmillions to its budget. •Limited agility.The monolithic architecture of legacy databases makes it a challenge to isolateand provision resources for specific tasks. This leads to performance issues when conflicts ariseover shared resources. •Lack of technical expertise available for support.As legacy technologies get older, it is harderto find IT professionals who know how to work on them. Additionally, it is difficult to achievethe same level of IT support and solutions on premises as are available on the cloud. This slowsdown new developments and improvements. DATA MODERNIZATION STRATEGIES Data modernization refers to projects that move historical data and, more importantly, dataprocessing pipelines from a legacy technical stack to a modern one. Most modern data technologiesare based in the cloud, so data modernization typically involves a cloud migration project. However,integration also plays an important role. Cloud migration Moving to the cloud allows your data infrastructure to benefit from the game-changingcharacteristics of cloud computing, including endless scalability, a pay-as-you-go financial model,and rapid solution deployment.The two most common examples of data modernization projectsare: •Data warehouse migration from on-premisesTeradata, Netezza, DB2, Oracle, or MS SQLServer system to one of the leading cloud solutions, such as Google Big Query, Amazon Redshift,Azure Synapse, or Snowflake. •Data lake migration from on-premises Hadoopsuch as Cloudera or Hortonworks to amanaged solution in a cloud, like Amazon EMR, Databricks, or Google Dataproc. Many organizations are moving their data to cloud-based systems, such as AWS, GCP, or MicrosoftAzure. Cloud migration allows your company to: •Scale– Cloud environments allow you to increase or decrease computing resources as needed.•Cut costs– Save