Data science is an important skill, but the hard truth is many organizations aren’t seeing the ROI showing that data science work is making a business impact. Yet today, many organizations are still struggling to adopt a holistic approach centered around creating business value. Instead, they are focused on theoretical work. Here at Iguazio, we recently held a webinar with Noah Gift, founder of Pragmatic A.I. Labs, professor, author and MLOps consultant.
In a new ebook and video, we show you how to rapidly launch infrastructure that can scale with your startup as it grows into an enterprise.
Database schemas help with data integration and database optimization to drive better analysis and faster results.
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.
Synapse services are powerful tools for bringing data together for analytics, machine learning, reporting needs, and more. Synapse services serve the purpose of merging data integration, warehousing, and big data analysis together with the goal of gaining a unified experience to ingest, prepare, manage, and serve data for business intelligence needs.
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.
When we think of the various people and teams making use of ML and DBMS, we can place them on a spectrum based on the composition of their work.
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.
As the Director of Demand Gen at ThoughtSpot, I’m responsible for optimizing our campaigns and every customer touchpoint. No biggie, right? I’m part of a lean marketing team at a high-growth company. We’re not in the business of generating leads – we’re about creating real demand for our products.
With the launch of our High Volume Agent connectors for Oracle databases, you'll see faster setup times and easier access to all your data sources.
More and more companies are migrating their enterprise resource planning (ERP) to the cloud. It relieves them of the burdens typically associated with installing and maintaining complex software systems, and it’s arguably more secure because it’s monitored 24/7 by dedicated experts. There is no question that cloud-based ERPs like NetSuite, Epicor, Microsoft Dynamics, and Oracle Cloud are gaining steam. Rolling out a new ERP system can be highly disruptive to your organization, though.
Before the data era, data engineers and data scientists had few resources, few technologies, and few data to build something from. But they also had little pressure from the business to create new values, and above all, it was easier to find some time to write, check and implement their applications. It had the advantage of better control of quality.
A modern data stack, built on Fivetran and Google BigQuery, is fundamental to gaining the data visibility retailers need to deliver seamless personalized omnichannel experiences.
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.
Businesses around the globe are struggling to do more with less as budgets tighten, uncertainty looms, and talented workers can be scarce. At the same time, the finance function is emerging as a strategic pillar in many organizations. Companies are generating more data than ever before, and it’s falling on the finance team to make sense of the meaning behind all those numbers.
Last week ThoughtSpot took big steps toward further focusing our pricing on a single metric: customer value. In the course of my career, I’ve worked at many companies, and seen countless products, packages, and editions launched. Often, these initiatives are guided by what’s in it for the company: how do we get better margins, cross or upsell products together, or maximize revenue?
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.
The beauty of enterprise resource planning (ERP) software is its ability to provide a central view of your organization’s financial, operational, and business data. It automates repeatable tasks, streamlines your ability to create reports and analyze data, and sheds clarity on sales, marketing, human resources, supply chain management, and even manufacturing.
This is part 3 of our 3-part Hyperparameter Optimization series, if you haven’t read the previous 2 parts where we explain ClearML’s approach towards HPO, you can find them here and here. In this blog post, we will focus on applying everything we learned to a “real world” use case.
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.
Whether you work in a hospital, long-term care facility, clinical lab, or a HealthTech company, all workers in the healthcare sector today are focused on value-based care and cost efficiencies. For organizations intent on streamlining processes, an analytics tool is the logical next step. Undoubtedly, embedded analytics solutions deliver the best outcomes.
One element that rings true in the world of analytics is that it is everchanging. Chief Data Officers and business leaders must stay abreast of key trends so their organizations don’t miss out on its benefits. A relatively new buzzword in the embedded analytics arena was coined by thought leader Howard Dresner, who serves as Chief Research Officer of Dresner Advisory Services.
In the restaurant industry, they say that you’re only as good as your last meal. The same is true with technology. You have to keep up with the latest and be the greatest to remain competitive. It is a dynamically changing industry, and staying ahead of key trends is of the utmost importance. The most recent talk is around a new concept described as “composable analytics.”
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.
The smart pairing that will turn your numbers into dynamic C-level insights Financial and tax management has evolved enormously since the advent of computational technology. These days, you can’t just get by on spreadsheets and ledgers – everywhere you look you’ll hear the buzz of the age of automation. Your data needs to be sophisticated, offering essential storytelling to help support strategic insights.
Automation is your key to success in Finance and this includes your close. Anyone who has been through a close knows a clunky one is painful, and it arrives routinely whether you are prepared or not. A recent survey by Hanover Research found that a staggering 49% of Finance professionals felt unable to execute their tasks completely because their current manual processes were too time consuming. And those manual processes impacted their ability to execute essential tasks efficiently and effectively.
Having a governance strategy gives you data control and visibility.
Whether your goal is to leverage the newest technology or to stay up to date with your Oracle ERP, migrating to the cloud is a complex, but worthwhile undertaking. It requires time to install, train, and embed new processes, but the effort is rewarded by the ability to leverage more agile workflows and increase ROI. Although many companies run their own on-premise servers to maintain IT infrastructure, 48% of organizations already store data on the public cloud.
As the need for greater interactivity and data access increases, more and more companies are making the move to adopt cloud computing. Microsoft is investing in and pushing customers towards its cloud ERP offering, Dynamics 365 Business Central (BC), which is experiencing a staggering 200% annual growth rate. But the significant hurdles posed by cloud migration can make it a daunting task to consider.
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.
With Fivetran webhooks, developers can use real-time messages to power user experiences, transform data, drive error alerting and more.
Global research reveals that 77% of enterprises lack real-time access to ERP data, leading to poor business outcomes and lost revenue.
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.
Change data capture helps you make faster and more accurate decisions with real-time data movement.
It’s no secret that modern data professionals are under immense pressure to deliver more data and insights to more business users, more quickly than ever before. Data is the lifeblood of your business. And frontline business people need personalized, actionable insights to make data-driven decisions. But before these users even touch a self-service Live Analytics platform like ThoughtSpot, the data must be appropriately modeled by analytics engineers.
As companies go all in on the cloud to dominate the decade of data, agility, flexibility, and ease of use are critical to success. That’s why we’re so excited to announce ThoughtSpot’s support for Amazon Redshift Serverless which allows customers to leverage the Modern Analytics Cloud to run and scale analytics on Amazon Redshift without having to provision and manage any data warehouse infrastructure.
Many organizations are turning to Snowflake to store their enterprise data, as the company has expanded its ecosystem of data science and machine learning initiatives. Snowflake offers many connectors and drivers for various frameworks to get data out of their cloud warehouse. For machine learning workloads, the most attractive of these options is the Snowflake Connector for Python.
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.
Growing companies rely on equity-based compensation to attract and retain top talent. They must also comply with stringent regulations regarding financial reporting and disclosures. It’s common practice in many startups–and even in some more mature public firms–to make do with manual processes and low-cost solutions for managing disclosures and cap tables. As a company grows, however, the complexity surrounding these processes increases.
Reporting is more important than ever. Long gone are the days of filling out excel sheets at year end and filing them away into oblivion, never to be seen again. The reports you file now, are not only more transparent than ever, but they have much more impact in the future and trends of your business than ever before.
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%.
Tech leaders debate why SQL is the lingua franca of data analysis, the unbundling and rebundling of software and emerging trends in data governance within the modern data stack space.
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.
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.
Today, we are excited to announce the availability of CodeSpot, a searchable repository of ThoughtSpot blocks and code samples to help developers embed engaging analytics experiences into any app for the modern data stack. CodeSpot harnesses the knowledge and experience of ThoughtSpot Everywhere developers, data analysts and engineers, and product experts to build a broad ecosystem of shareable assets to accelerate development projects and benefit our developer community and customers.
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.
Why turnover among chief data officers is so high and what can be done about it.
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.
Data has become the lifeblood of most organizations. Yet, despite using data almost daily to make critical business decisions, few organizations have complete visibility into the health and usage of their data. Moreover, as the acceleration of data usage has increased, so too has the complexity of data systems, increasing the risks of data-related issues and making it even more difficult to identify and resolve issues related to data quickly.
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.
Being a finance professional is stimulating and rewarding, but it comes with a set of unique challenges. The accuracy and timeliness of information is essential, yet for most organizations, the volume and complexity of financial information is growing continuously. Simply working harder doesn’t seem to solve the problem; finance professionals must work smarter, applying the right tools to get the job done quickly, efficiently, and accurately.
Thousands of enterprise ERP customers have discovered the convenience and flexibility offered by insightsoftware’s Wands products. If you’re currently running any of the Wands for SAP solutions, you should know about some recent updates to the product line that will enhance your team’s productivity, increase the options available for calendar-year reporting, and improve the underlying technology on which the Wands products are built.
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.
You simply cannot afford to get equity management wrong. Investors and employees expect precision. You must create accurate tax records. As your company scales, compliance often grows more complex. SEC, FASB, and IFRS regulatory requirements frequently introduce complexities that make equity management especially challenging. Many companies are tempted to simply go with a broker solution. After all, several of the larger brokerage houses offer inexpensive or even free equity management solutions.
In our last article, we introduced the topic of SLAs (Service Level Agreements) and how they are necessary within organizations to help both consumers and producers agree on expectations around data usage and quality. Not only do SLAs provide visibility into what needs to be achieved to ensure data reliability and avoid surprises, but SLAs also create communication flows between consumers and producers that help ensure an alignment on expectations.
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.
In today’s highly competitive hiring market, aggressive growth companies are doing everything they can to attract and retain top talent. That means offering equity compensation plans that incentivize performance and encourage the best people to stay on board for the long haul. Equity management can get complex quickly, especially as a company scales up.
The provisioning of software and services that help companies grow is the end goal of Human Capital Management (HCM) organizations around the world. Software specialists in this space ensure that their clients are in compliance with Human Resources (HR) laws and dispel the need to hire full-time, back-office staff. The delivery of these specialized services has changed in recent years. Many providers of HCM transmit via the cloud and on a subscription basis.
Imagine the following scenario: You’re building next year’s budget in Microsoft Excel, using current year-to-date actuals that you exported from your enterprise resource planning (ERP) software. You also have this year’s approved budget on hand for reference. During this process, you notice that maintenance and repair expenses were especially high in June and July. That increase wasn’t in the budget, but maybe you should budget for it next year.
