Building a data-driven culture with the Google Cloud
Building a data-driven culture is a journey that starts with understanding the importance of data, then continues down the road to folding data-driven decision making into all of the business’s teams and initiatives. Implementing a data culture can help organizations become more agile, innovative and responsive to customer needs.
Google Cloud works to provide digital insights and the capability to take action to both consumers and enterprises, both in advertising work with businesses and now, through the tools and services for data management and predictive insights offered by Google Cloud.
What brings the data-driven culture for your business?
Adding agility to your organization
Talking about the concept of agility, it’s about attitudes, as well as the technology that a company uses. Instead of spending time searching for the right data, or tinkering with hardware or software that you need to find or analyze that data, Googlers don’t worry about lack of skills or lack of agility in operating, scaling, or managing data. Google believes this kind of mindset can help users work more efficiently and reduce or eliminate silos that often exist.
Better data, smarter businesses
Following the collaborative data culture, though, advanced analytics tools and outputs have to be easily accessible to everyone. Once you’ve established which team needs which information, you can set up users with the right data to do their work.
Google Cloud builds its own AI and ML technology into systems like BigQuery for data warehousing, and in places like Contact Center AI and Document Understanding. Google built BigQuery ML with a familiar SQL interface, so you only have to write a query to be able to use machine learning. And the built-in advanced analytics features mean you can be prepared for the next generation of smart technology.
Getting the insights you need
Data governance is essential for the users to have the right access and to keep data protected through its use and lifecycle. Google Cloud’s data warehouse solution – BigQuery lets you process data in object storage, transactional databases, Sheets and more, so you never have to duplicate data. It includes public datasets, too, so you can get even more value from your internal data.
Building trust into your data and teams
So one big step in achieving a data-driven culture is making it easy for users to trust data – trust that it’s updated and accurate. Data comes from a huge variety of sources, but it all requires the same baseline level of protection that includes encryption, data exfiltration, and access controls. Beyond that, you’ll likely have your own threat models and areas of focus for protecting data. Depending on your industry, you might be working on complying with specific compliance and regulation rules.
At Google, BeyondCorp is a zero-trust security model that grew out of original zero-trust networking principles. The idea behind this is that access controls shift from the network perimeter to individual users and devices, so users can work securely from any location without a VPN. BeyondCorp includes single sign-on, access proxy, access control engine, user inventory, device inventory, security policies, and a trust repository.
Google Cloud services that drive the data-driven culture:
Cloud SQL – is a relational database service for MySQL, PostgreSQL, and SQL Server, fully managed by the Google Cloud. Cloud SQL automatically ensures your databases are reliable, secure, and scalable so that your business continues to run without disruption. Cloud SQL automates all your backups, replication, encryption patches, and capacity increases—while ensuring greater than 99.95% availability, anywhere in the world.
Cloud Spanner – is a fully managed relational database with unlimited scale, strong consistency, and up to 99.999% availability. Data is automatically and instantly copied across regions, which means that if one region goes offline, the organization’s data can still be retrieved from another region. It provides strong consistency and massive scalability, which means that, for organizations, this is no longer a trade-off.
Cloud Bigtable – is a fully managed, scalable NoSQL database service for large analytical and operational workloads with up to 99.999% availability. It is perfect for storing very large amounts of data in a key-value store and supports high read and write throughput at low latency for fast access to large amounts of data. Throughput scales linearly—you can increase QPS (queries per second) by adding Bigtable nodes.
BigQuery – is a serverless, highly scalable, and cost-effective multi cloud data warehouse designed for business agility, fully-managed by the Google Cloud. BigQuery enables data scientists and data analysts to build and operationalize ML models on planet-scale structured or semi-structured data. This service allows you to analyze petabytes of data using incredibly fast speeds and zero operational overhead.
Looker – Looker is a modern business intelligence and big data analytics platform that helps you explore, analyze and share real-time business analytics easily. It enables you to build data experiences that empower users and reduce reliance on your data teams by up to 99% as well as improve productivity, decision-making, and innovation by delivering more insights to more users.
Dataflow – managed service for executing a wide variety of data processing patterns. Dataflow is a service for large-scale processing of data, which enables you to develop real-time batch and stream data processing pipelines. Plus, new customers get $300 in free credits to spend on Dataflow or other Google Cloud products during the first 90 days.
Pub/Sub – is a service for real-time messaging and ingestion of data for event-driven systems and streaming analytics. Pub/Sub enables reliable, expressive, exactly-once processing and integration of event streams in Java, Python, and SQL. Also, with Pub/Sub you can create systems of event producers and consumers, called publishers and subscribers.
Dataprep – is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning. Dataprep is serverless and works at any scale, so there is no infrastructure to deploy or manage.
AutoML – enables developers with limited machine learning expertise to train high-quality models specific to their business needs and with minimal effort. You can build your own custom machine learning model in the matter of minutes.
AI Platform – is a unified, simply managed platform that makes machine learning easy to adopt by analysts and developers. AI Platform provides modern ML services and includes the Google Cloud AI Hub, a hosted repository of plug-and-play AI components.