Tired of wrangling data into Excel spreadsheets? Still waiting on that report from IT? Well, by the time reports are ready, it’s often too late to take any meaningful action. Especially not in real-time. To accelerate business innovation and get value from data faster, instantaneous analysis and decision-making made possible with real-time data warehousing is the answer. Real-time data warehousing, like Google BigQuery, can help improve business agility, enable personalized customer experiences, and unlock new business models.
1. Improve business agility through optimization
The traditional approach to data analysis involves bringing data together on a fixed schedule to create reports. Data is gathered, sanitized, standardized, then rolled up into a report. When data is siloed on different systems, the reporting process can sometimes require a request to IT staff, who then have to pull data from various sources. This is a tedious process where by the time the data reaches the decision makers, it is no longer relevant. Bringing data together automatically in real-time can cut down on wait times and make real-time decision-making possible.
Real-time data warehousing allows businesses to combine the data from various sources into a single repository accessible to different business units. This allows important data to be shared across departments while retaining user roles and access to specific tools. For example, the marketing team using Mailchimp, the sales team using Salesforce, and the customer support using Zendesk can consolidate their data and garner insights that can fuel agile decision-making.
2. Personalize customer experiences with AI
Modern consumers expect a personalized customer experience. They have become accustomed to being connected at all hours and staying in the loop. The customer experience is not just about being quicker but giving them an experience that imitates the attentiveness they might expect one-on-one in a store. To achieve this personalized experience at scale online, organizations must have rapid access to real-time data to make prompt decisions.
Real-time data warehousing enables machine learning and advanced analytics to tailor every phase of the customer buying cycle to the individual buyer. For example, after completing an order, a customer is much more likely to make another purchase, so providing useful related products not only helps them, but could turn a single smaller purchase into a bigger one. Even if they don’t buy, AI powered systems can provide offers in the days or weeks following the purchase with further offers or helpful information to enhance the initial sale.
