Database integration is notoriously tricky to manage. If you’re avoiding it and running queries on your transactional databases, your databases are likely overburdened by workloads. If you’re trying to build your pipeline internally, your data engineers are likely spending countless hours writing scripts and juggling outages at the expense of fresh, accurate and reliable data. Regardess, transactional databases hold a lot of data that data teams can use to analyze and drive insights across the entire organization. If you’re not centralizing and analyzing your database data, you’re leaving valuable insights on the table.
As you’ll see in the following examples, regardless of industry, location or company size, businesses with reliable access to database data can derive insights and make informed decisions around revenue, customer journey, customer support, marketing channels and more.
1. Analyzing operational data from multiple databases in the construction industry
Civil construction business Emery Sapp & Sons, generates a ton of data on a daily basis: the usual administrative, financial and labor workforce data, as well as data coming in from the field such as time spent on a job and a location, equipment runtime and utilization, and units of production – all stored within databases. When the business decided to migrate its data from PostgreSQL and SQL Server to a cloud data warehouse, the Director of Technology began by building scripts himself, but quickly learned that spending 20% of his time on pipeline management wasn’t sustainable or scalable.
With Fivetran, he offloaded the burden of data pipelines and built up-to-date dashboards for reports that used to take hours to create. Dashboards include fuel reports, revenue summary dashboards, accounts receivable dashboards, branch manager dashboards, and job summaries that show granular data points for individual jobs and enable the team to quickly identify large variances and react more quickly to potential problems. The construction industry operates on narrow margins, so using data to work faster and smarter to improve efficiency and gain a competitive edge has been key. Read the full case study.
2. Joining website data with transactional data to improve customer loyalty in retail
Australia’s largest pet ecommerce store, Pet Circle, wished to move away from using database services on top of Google Cloud, and chose Fivetran to centralize its transactional data from MariaDB into its Google BigQuery data warehouse. By choosing Fivetran over building internally, Pet Circle saved months of engineering effort and ongoing maintenance that would have required an additional engineer.
By joining views of website sessions with paid data in a single transformed table, the business can understand how often customers visit the site, login, modify their subscriptions and more. This data helps Pet Circle improve loyalty and retention by adding customer value rather than transactional value. Read the full case study.
3. Centralizing hundreds of databases for holistic insights across global business locations
Shared workspace provider, WeWork, employed Fivetran to make it easier to maintain data governance and compliance, ingesting data from hundreds of cloud-based and on-premise sources into Snowflake, including Postgres databases from each WeWork location.
Standardizing this information in a central location in the cloud gives stakeholders real-time insight into occupancy, turnover, outstanding renewals, member growth and profit margins per location, per region and holistically across the company.
This visibility allows WeWork community managers, the senior leadership team and even investors to see what’s working and what’s not, so they can draw specific conclusions about contributing factors and prioritize resources to replicate success across locations. Read the full case study.
4. Using customer order data to fuel customer campaigns in restaurant industry
Global restaurant chain Nando’s had an existing data infrastructure in place that was not meeting the demands of the business as it grew. Entire databases needed to be replicated daily – a poor use of the team’s time.
The business completely overhauled its infrastructure, using Fivetran to sync data from SQL Server to BigQuery. By using Fivetran for ELT, Hightouch for Reverse ETL and Looker dashboards, Nando’s tracks business is performance on a granular level, including the most profitable restaurants, customer order trends, meal deal performance and possible fraud incidents.
Integrating different data sources has enabled more sophisticated types of analysis. By joining in-restaurant transactions with platinum and gold customer purchases, the loyalty team can analyse the effectiveness of their programmes and tweak them accordingly. Warehouse data also enabled the business to target customers by postcode during COVID, for instance, to let them know when an outlet in their area was reopening. Read the full case study.
5. Joining transactional and marketing data to allocate marketing budget effectively
Pre-owned vehicle ecommerce marketplace Cars24 used custom pipelines to bring data from its MySQL stores into a larger MySQL database, which functioned as the warehouse. The analytics team had to run its queries on top of MySQL, which caused the ‘warehouse’ to go down frequently. After estimating that it would take a team of five or six more than four months to build the initial pipeline, with considerable maintenance afterwards, Cars24 decided to trial Fivetran. Within two days, the MySQL database connector was up and running.
With Fivetran seamlessly piping data into Snowflake, crashing the database is no longer a problem. By joining transactional data with marketing data, Cars24 can understand channel performance and allocate budget in the most effective way. Read the full case study.