How data analytics can boost operational efficiency

The amount of data stored by organizations keeps on growing. Estimates show that by 2025, the amount of data will have grown to 175 ZB. Besides raising questions about storage solutions, this also raises questions about data analyst workloads and how to use and manage data efficiently without breaking the bank. Fortunately, the fact that your data grows does not mean that your organization has to turn into a data jungle where it is difficult to find the right route.

In this article, we will guide you through some of the best solutions that boost operational efficiency using data. It is all about cutting down workloads, making data accessible, and optimizing data-driven decision making. Keep on reading to find out how you can use your organization’s data to boost operational efficiency.

Storing your ever-growing datasets is one thing; using them efficiently is another challenge. Cloud data warehouses such as BigQuery drive insights and actions faster which will help you to make smarter, better-informed decisions and to improve business operations. Together with the rest of Google Cloud’s smart analytics, it clears the way to ingest data from various sources in the most cost-efficient way. This is why our tools BigQuery and Looker form such a strong combination.

BigQuery manages your data, operationalizes workloads, and contains the predictive machine learning metrics you need. To get the most value out of this data while controlling costs, companies must leverage their data infrastructure to connect people with data in new, smarter ways. This is where Looker comes in. Looker allows you to democratize data access so that anyone in the company can understand and see what’s going on in real-time.

The combination of these tools enables employees to optimize the decisions they make because they have access to relevant data. At the same time, data analyst workload is lowered by 70% thanks to the centralized data model of these tools. Together, BigQuery and Looker form a power couple that allows you to enhance operational efficiency and data-use at the same time.

A clear example of a company that has successfully put its data to use to improve operational efficiency is Craveable. Craveable Brands worked with leading partners Sakura Sky to implement Google Cloud Platform (GCP) and StratosMedia to use GCP for a cloud-based content management solution for in-store digital menu boards.

Craveable Brands’ digital transformation seeks to improve employee access to data. Doing so will enrich customer relationship management (CRM) systems and loyalty programs that store the details of about 500,000 people. This will in turn enable Craveable Brands to deliver more targeted marketing campaigns and identify opportunities to provide better customer experiences. As part of the effort, the company is looking to modernize menu content displays and updates at Red Rooster restaurants.

“We were particularly impressed by technologies that could support our longer-term strategies,” says Russell. For instance, Russell cites machine learning-powered image content analysis through Cloud Vision API and machine learning-enabled text analysis through Cloud Natural Language as being of interest to the business.

Moving to GCP enables Craveable Brands to reduce its data costs by 75% while gaining access to Google’s experience and expertise in scaling and crunching data. In addition, the company has found it can complete some queries twice as fast as it could in its previous cloud environment. This allows Craveable Brands to make decision-making data available to its business and marketing teams quickly and extend access to that data to more team members across the business. If you’d like to learn more about the case of Craveable Brands, please continue reading here.

As the case of Craveable Brands shows, organizations that use data analytic tools such as Looker and BigQuery together will optimize costs, manage demand and drive better business outcomes. In this manner, they solve the questions surrounding data workloads and costs, boosting operational efficiency while simultaneously making data-based decisions. It truly allows you to get the best of both worlds.