Editor’s note: The post is part of a series highlighting our partners, and their solutions, that are Built with BigQuery. To fully leverage the data that’s critical for modern businesses, it must be accurate, complete, and up to date. Since 2007, ZoomInfo has provided B2B teams with the accurate firmographic, technographic, contact, and intent data they need to hit their marketing, sales, and revenue targets.
If someone asked you what makes data “healthy”, what would you say? What IS data health? Healthy data just means data that is quality, accessible, trusted, and secure, right? Wrong. Let's dissect. Data health really has nothing to do with the data itself, if you think about it.
It’s hard to believe enterprise BI platforms have been around for three decades. In that time, they have served the purpose of collecting and analyzing large amounts of data to help businesses make more informed decisions. But in today’s data-driven economy, analysts struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret data in real-time.
Machine learning (ML), more than any other workflow, has imposed the most stress on modern data architectures. Its success is often contingent on the collaboration of polyglot data teams stitching together SQL- and Python-based pipelines to execute the many steps that take place from data ingestion to ML model inference.
If data is currency in today’s digital environment, then organizations should waste no time in making sure every business user has fast access to data-driven insights.
At Cloudera we’re building the world’s only hybrid data platform that’s founded on open source and truly hybrid. What do we mean by truly hybrid? Well, not only does it seamlessly support on-premises and cloud-based deployments alike, but uniquely, it is cloud vendor agnostic, allowing multi-cloud strategies to thrive.
At least every few years, utility companies release squadrons of drones into the skies to digitally photograph hard-to-reach equipment and check for wear and tear. Around the world, there are 3 million miles of high-voltage transmission lines – enough to go from the moon and back half-a-dozen times – and as much as 64 million miles of local transmission lines; that represents a lot of images, and even more data.
Since the very first email was sent more than 50 years ago, the now-ubiquitous communication tool has evolved into more than just an electronic method of communication. Businesses have come to rely on it as a storage system for financial reports, legal documents, and personnel records. From daily operations to client and employee communications to the lifeblood of sales and marketing, email is still the gold standard for digital communications.
Customer advocacy is one of the Snowflake Support team’s most important roles. Working closely with customers around the world every day, we listen and learn to gain meaningful insights into Snowflake products, the ways our customers use those products, and the challenges they face. We feel a deep responsibility to take those insights and trends, analyze them, and drive positive change on behalf of Snowflake customers.
Relational databases are great at processing transactions, but they’re not designed to run analytics at scale. If you're a data engineer or a data analyst, you may want to continuously replicate your operational data into a data warehouse in real time, so you can make timely, data driven business decisions.
Deciding to adopt an AI-first strategy is the easy part. Figuring out how to implement it takes a little more effort. It requires a clear-eyed vision built around well-defined goals and a realistic execution plan. Being AI-first means setting up your organization for the future. By leveraging data, analytics, and automation, a company can gain a better understanding of where it is and where it needs to go.
At Talend, we hear every day from our customers that healthier data makes it easier to increase revenue, reduce costs, and mitigate risk. But the journey to healthier data isn’t always easy. To support healthy data, organizations must unify data activities across users with different skill sets and levels of technical expertise through intelligence and self-service capabilities.
Whether they know it or not, every company has become a data company. Data is no longer just a transactional byproduct, but a transformative enabler of business decision-making. In just a few years, modern data analytics has gone from being a science project to becoming the backbone of business operations to generate insights, fuel innovation, improve customer satisfaction, and drive revenue growth. But none of that can happen if data applications and pipelines aren’t running well.
BigQuery's Remote Functions (in preview) make it possible to apply custom cloud functions to your warehouse without moving data or managing compute. This flexibility unlocks many use cases including data enrichment. In this post we demonstrate a pattern for combining BigQuery with the Google Maps API to add drive times to datasets containing origin and destination locations. This enrichment pattern is easily adapted for address geocoding or adding Google Map's place descriptions to locations.
Today we are announcing the Preview of BigQuery Remote Functions. Remote Functions are user-defined functions (UDF) that let you extend BigQuery SQL with your own custom code, written and hosted in Cloud Functions, Google Cloud’s scalable pay-as-you-go functions as a service. A remote UDF accepts columns from BigQuery as input, performs actions on that input using a Cloud Function, and returns the result of those actions as a value in the query result.
Tuning Hive on Tez queries can never be done in a one-size-fits-all approach. The performance on queries depends on the size of the data, file types, query design, and query patterns. During performance testing, evaluate and validate configuration parameters and any SQL modifications.
Hitachi Vantara recently commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study to examine the value that customers could achieve using cloud and application modernization services from Hitachi Vantara. To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers at companies with experience using cloud and app modernization services from Hitachi Vantara.
The idea of running compute and storing data in the cloud is no longer a novel concept. With the evolution of 5G and Internet of Things (IoT), this brings along the next evolution of edge storage demands. Today, around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, Gartner predicts this figure will reach 75%.
We like to keep things light at Unravel. In a recent event, we hosted a group of industry experts for a night of laughs and drinks as we discussed cloud migration and heard from our friends at Don’t Tell Comedy. Unravel VP of Solutions Engineering Chris Santiago and AWS Sr. Worldwide Business Development Manager for Analytics Kiran Guduguntla moderated a discussion with data professionals from Black Knight, TJX Companies, AT&T Systems, Georgia Pacific, and IBM, among others.
The term DataOps (like its older cousin, DevOps) means different things to different people. But one thing that everyone agrees on is its objective: accelerate the delivery of data-driven insights to the business quickly and reliably.
Business intelligence empowers businesses to get the most out of their data by providing tools to analyze information, streamline operations, track performance, and inform decision-making. In the Microsoft Dynamics ecosystem, Power BI generates easy-to-read visualizations that help stakeholders perform key analysis. But finance professionals can encounter roadblocks when seeking deeper analysis than their technical knowledge of Power BI permits.
Customers who work with data warehouses, running BI on large datasets used to have to pick low latency but trading off freshness of data. With BigQuery BI Engine, they can accelerate their dashboards and reports that connect to BigQuery without having to sacrifice freshness of the data. Using the latest insights helps them make better decisions for the business.
Fraud, waste, and abuse (FWA) in government is a constant, multi-billion dollar issue that challenges agency leaders at all levels and across all sectors, from healthcare to education to taxation to Social Security. The scope and scale of public spending—federal outlays alone were approximately $6.6 trillion in fiscal year 2020 according to the Congressional Budget Office—make FWA an inherently difficult problem to solve.
Marketing based on the next best action requires effective data handling for success. For Canadian insurer, Beneva, however, silos were standing squarely in the way. With over three million customers and CA$13 billion in assets, Beneva is one of Canada’s largest financial institutions. They had over 75 years of product and customer data, but that data was isolated in various systems, databases, and customer portals.
I recently sat down with CFODive to discuss the importance of modern financial analytics in transforming the way financial leaders and their organizations operate – a topic that is only becoming increasingly prominent. Business strategies have had to rapidly adjust to address market volatility, consumer trends, and unpredictable world events. These dynamics have forced finance teams to rethink how they are using data and analytics and take a more modern approach.
Artificial intelligence (AI) has been a focus for research for decades, but has only recently become truly viable. The availability and maturity of automated data collection and analysis systems is making it possible for businesses to implement AI across their entire operations to boost efficiency and agility. AI has the potential to transform operations by improving three fundamental business requirements: process automation, decision-making based on data insights, and customer interaction.
Talend has a lot in common with our customers. We collect a whole lot of data. We take pride in how we use that data. And we’re always looking for new ways to drive better business outcomes with our data. That is why Talend recently launched the Digital Touchpoint Model (DTM) initiative to create an exceptional, data-driven, “One Talend” support experience for customers, using our own products and innovative best practices.
As digital transformation accelerates, and digital commerce increasingly becomes the dominant form of all commerce, regulators and governments around the world are recognizing the increased need for consumer protections and data protection measures.
