The pandemic has forced organizations to accelerate their digital transformation journey for competitive advantage and business growth. To achieve this goal, many companies have started modernizing their data and analytics in the cloud. According to the latest state of the cloud report, 54% of organizations have adopted cloud data warehouses to drive their digital business.
Businesses are using data ingestion from various sources (e.g., traditional databases, data warehouses, mainframe systems, streaming data, machine logs) to migrate data into a cloud data warehouse. However, most businesses have struggled to ingest their application data into a cloud data warehouse and data lake due to siloed infrastructure and hand-coding approaches. This has hindered them from becoming a data-driven organization. As a result, they cannot synchronize their application data to make data-driven business decisions.
This blog post kicks off a series that will cover the following topics:
On-premises enterprise data warehouses are not equipped to support the newer demands of cloud analytics and AI. Deployed on dedicated hardware and installed and managed by the IT team, they are expensive and time-consuming to set up, operate, and scale. They can also take months to upgrade and often require a fair amount of regular maintenance that only an experienced database administrator can provide. In addition, many data warehouses require rigid data models that are not flexible enough to handle new and different types of data, such as semi-structured or unstructured.
To remain efficient and competitive, organizations must harness the power of the vast amount of diverse data constantly being generated and conduct analytics on that data. Fortunately, with advancements in cloud data warehouse architecture and cloud data management tools, organizations can meet new challenges related to data volume, variety, and velocity.
Customer experience: Monitoring customer behavior in real time and combining it with their historical buying pattern data can help organizations provide the right product to the right customer at the right time.
For example, telecom companies are facing increasingly tough times as digitalization has severely impacted them. Customers are not ready to pay for voice and text service, and the Over-the-top (OTT) platforms are forcing telecom operators to adapt to new business models. Therefore, reducing customer churn and increasing retention is their top priority. Telcos can capture customer location data, network information from towers, and weather data in real time to predict service disruptions and proactively notify customers with the SLA via SMS or emails. This can help improve customer experience as they are already aware of the issue and the timeline to resolve it.
Quality assurance: Organizations can use streaming data (e.g., sensor data generated from IoT devices and machines in real time) to detect anomalies in the product and quickly act to fix them.
For example, manufacturers can set up a rule that if the temperature of a turbine goes beyond 100 degrees centigrade, it will send a real-time alert to the operations team to take remedial action. This will help the operations team rapidly repair the turbine before it stops working.
Operational efficiency: Organizations can reduce costs, boost margins, streamline processes, and rapidly respond to market needs by analyzing events in a cloud data warehouse.
For example, food industry companies can improve operational efficiency and deliver the best services to their customers by analyzing the customers' behavior using their shopping data by implementing predictive analytics to calculate the average checkout wait time.
Innovation: By using the new sources of digitized data and applying machine learning and AI techniques, organizations can spot and capitalize on trends to disrupt their industry before any competitors.
For example, OTT, ride-sharing apps, and ecommerce platforms have disrupted the entertainment, auto, and retail industries by capturing customer data in real time to provide the best service at a lesser price than traditional players.
In the on-premises world, organizations may have operated only five to ten enterprise applications that generated significant data. Now, in the cloud world, even midsize organizations have hundreds of applications. Each application can potentially create data silos – marketing in one system, finance in another, product information in another – with none fully integrated for analytics consumption. This data must be ingested, stored, and integrated into a cloud data warehouse to help organizations drive advanced analytics use cases. Let’s walk through some common use cases that can benefit by ingesting application data into a cloud data warehouse:
Real-time offer management – Combining customer's real-time transaction records from mobile apps and customer’s spend history can help retailers generate real-time offers and alerts to increase revenue via cross-sell and up-sell.
Customer fraud analytics – Banks can run a real-time fraud detection ML model on application Change Data Capture (CDC) data and proactively alert their customers of potential fraud.
Predictive maintenance – Manufacturers can identify stress signals coming from devices and act on them early to optimize costs and maximize availability.
Clinical research optimization – Healthcare companies can collect and process bedside monitor data for clinical researchers to understand and detect disease markers more effectively.
Informatica Cloud Mass Ingestion is a code-free, cloud-native data ingestion service available via the Intelligent Data Management Cloud. Cloud Mass Ingestion provides format-agnostic data movement and mass data ingestion, including file transfer, and exactly-once database replication. It also offers mass streaming ingestion from various sources, complete with real-time monitoring, alerting, and lifecycle management. In addition to all the above data ingestion capabilities, we will launch the application ingestion capability in CMI in October.
Benefits of Informatica's Cloud Mass Ingestion:
Speed: Build data ingestion jobs in minutes with a simple, easy-to-use four-step wizard-based experience.
Simplicity: Simplify data ingestion with a single cloud-native data ingestion solution with out-of-the-box connectivity to databases, files, streaming and application sources..
Scale: Ingest terabytes of any data, any pattern, at any latency at scale in real time and batch with no data limit.
Flexibility: Track, capture, and update changed data in real time with automatic schema drift support to accelerate database replication and synchronization use cases.
As mentioned earlier, we will be launching the application ingestion capability in Informatica Cloud Mass Ingestion, which will help you ingest data from SaaS and on-prem applications (e.g., Salesforce, SAP ECC, Dynamics 365) to support use cases like application synchronization, application modernization, and advanced analytics.
Now that we understand the benefits of code-free data ingestion and introduced application ingestion, we will deep-dive into the use cases and key capabilities of Cloud Mass Ingestion in the next post in our series.