Data breaches in the financial sector have become a major concern for businesses and consumers alike. With the increasing reliance on digital transactions and data storage, it has become more important than ever for financial institutions to ensure that their data is secure. Unfortunately, due to the complexity of financial systems and the potential for malicious actors to exploit weaknesses in security protocols, data breaches are becoming more common in this sector. According to American Banker, 79 financial institutions reported data breaches affecting over 1,000 customers in 2022. This figure is likely to increase in the years to come.
Unfortunately, the financial institutions themselves aren’t the only potential target for cyberattacks. While data breaches can affect many customers at once, it is also possible for hackers to target individual users. This is especially common as more hackers use AI-driven strategies to commit cyberattacks.
This article will explore the causes of these data breaches, as well as potential solutions that can be implemented to protect against data breaches.
How Traders Can Mitigate the Risks of Data Breaches and AI-Driven Cyberattacks
Online trading carries with it a certain degree of risk. Cybersecurity is an important factor to consider when engaging in online trading. It is essential for traders to be aware of the potential risks and take steps to mitigate them. This article will discuss the various cybersecurity risks associated with online trading and provide strategies for reducing these risks. We will also look at some of the use cases of AI-based tools that can help traders protect their data and investments from malicious actors. It is more important than ever to take the right precautions as more hackers are leveraging AI to take advantage of their victims, as this article from CNBC pointed out last September.
Online trading has become a paramount part of the investment world. However, it is every trader’s right to know that the convenience of online trading comes with several risks of cyber threats. Therefore, traders must understand the need for secure platforms to take all necessary precautions to protect their investments. In this article, we’ll explore the importance of security in online trading and highlight some examples of secure trading platforms and ultimately help the reader to have the power to thrive over the vulnerabilities where cyber criminals looking to exploit their accounts. You can read this article that we previously covered on preventing cyberattacks.
Phishing Attacks
Needless to say, cyberattacks have become more sophisticated and internationally targeted in recent years, affecting financial institutions and traders worldwide. To put this in perspective, one of the most significant threats facing traders is phishing attacks, where the attacks come in the form of an email or a message, usually pretending to be from a legitimate financial institution or even a trading platform. The message will most probably contain a link to a fake login page, where the trader enters their login credentials, allowing the hacker to access their account.
One such high-profile example of this type of attack comes from the 2016 hack of the Bangladesh Bank where hackers sent a fake payment instruction to the Federal Reserve Bank of New York, resulting in the transfer of a whopping $81 million to defrauding accounts. Clumsy as it is, the hack was made possible by the hackers obtaining the login credentials of Bangladesh Bank officials through a phishing attack.
In order to prevent phishing attacks, every trader or institution should always double-check the URL of the trading platform they are using and avoid clicking on links in unsolicited emails or messages, and as an additional security measure, traders should enable two-factor authentication (2FA), which requires them to enter an additional code along with their login credentials.
Malware
One other significant threat to traders is malware. And unfortunately, malware can come in many forms, including viruses, Trojan horses, and spyware which is hard for even a little tech-savvy person to detect sometimes. And once installed on a trader’s device, malware will work to collect sensitive information, such as login credentials or personal information, and send it to the hacker.
This type of attack, a big one happened in the 2013 Target breach, where hackers installed malware on Target’s payment processing system, allowing them to steal credit card information from millions of customers. And to prevent malware attacks, traders should always use up-to-date antivirus software and avoid downloading files or software from untrusted sources. Additionally, traders should consider using a secure trading platform, such as the MetaTrader 5 Web Terminal, which is designed to be secure and regularly updated to protect against new threats.
In 2022, it was also reported by researchers that one of such infamous malware, Medusa Android banking Trojan’s infection rates have increased and that more geographic regions are becoming targeted. It is of concern that the malware aims to steal online affected users’ credentials to go on and perform financial fraud. The readers can find the timeline of many such cyber incidents to understand how vulnerable even so-called very strong institutions, banks, and even block-chain private companies were. As a result of these happenings, financial institutions and traders alike should take substantial and robust security measures. Now let’s look at several ways that will aid this matter.
Through Public Wi-Fi networks
Finally, traders should be aware of the risks associated with using public Wi-Fi networks. Public Wi-Fi networks are often unsecured, making them a prime target for hackers looking to intercept data transmitted over the network.
An infamous example of this type of attack is the 2014 JPMorgan Chase breach. In this case, hackers accessed the bank’s network through a compromised employee’s computer connected to a public Wi-Fi network. The breach resulted in the theft of personal information from over 76 million households and 7 million small businesses.
To prevent attacks on public Wi-Fi networks, traders should avoid logging into their accounts or transmitting sensitive information while connected to public Wi-Fi. Instead, traders should use a virtual private network (VPN) to encrypt their data and protect against interception.
Apart from Phishing attacks, Malware attacks, and Ransomware attacks, there are also risks, attacks, and threats such as Distributed denial of service (DDoS) attacks, Insider threats, Weak passwords, Unsecured networks and devices, Social engineering attacks, Third-party security risks which one needs to be afraid of. To prevent them here are some measures which an old or new trader or institution should make mandatory and follow.
Multi-factor authentication
To highlight again, one-way traders can protect themselves is by using secure trading platforms. These platforms offer features such as multi-factor authentication, encryption, and secure login procedures to ensure traders’ accounts remain protected from unauthorized access.
GDPR
Compliance with regulations such as the General Data Protection Regulation (GDPR) is a crucial feature and key characteristic of protected and secure trading platforms. By ensuring that traders’ information is secure, the GDPR minimizes the chances of data breaches by introducing guidelines for handling and storage.
To stay ahead in the game, every trader should also be aware of platforms that offer rich security features like advanced order management and a wide range of technical indicators. Making informed decisions can minimize the risk of losing money if traders take advantage of these features.
Trading online can be tricky, and looking for a reliable online trading platform can be a daunting task but finding the right platform can make all the difference. Take, for instance, the MetaTrader 5 Web Terminal not only does it adhere to GDPR guidelines, but it also offers advanced encryption technology to keep your data safe. And top of that, one can access it from any device, without having to download any software. This Web Terminal, adheres to six principles of security, including the latest encryption technology, data protection, and secure login procedures, with that we can say it is a new standard for online trading and for a thankful portfolio, as it also allows traders to work directly from their browsers.
And that’s not all, It was re-created from scratch, to work even faster; and with market depth and technical indicators, a web terminal makes it easy to make informed decisions and stay ahead of the curve. It’s no wonder why it’s quickly becoming the go-to platform for traders of all levels. Hence, always prioritize security when choosing an online trading platform.
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Investors Must Reduce the Risks of Cyberattacks and Data Breaches When Making Trades Online
Traders should prioritize security that is up-to-date when choosing an online trading platform. Traders face significant risks from cyber threats, including phishing attacks, malware, and attacks on public Wi-Fi networks. Cyber threats are a significant risk for traders in the digital age. To protect against these threats, traders should always be vigilant and take steps to secure their accounts, such as using two-factor authentication, up-to-date antivirus software, secure trading platforms and others, and traders should conduct thorough research to find the best platform for their needs. By taking these steps, traders can help protect themselves from cyber-attacks and trade with confidence knowing that their accounts are secure.
Predictive analytics technology has had a huge affect on our lives, even though we don’t usually think much about it. Therefore, it should not be a surprise that the market for predictive analytics tools will be worth an estimated $44 billion by 2030.
There are many ways that predictive analytics is changing the way we live. One of the biggest changes is that more people are using this technology to forecast prices.
Predictive Analytics Helps Consumers Forecast Prices on Amazon
Predictive analytics can be especially valuable for Amazon shoppers trying to get the best possible prices. Amazon has become the most popular online shopping destination thanks to its vast product collection and affordable prices. However, because Amazon prices can change quickly, it can be challenging for customers to know whether they’re receiving the lowest price. This is where tracking product prices with data analytics becomes a good idea. By keeping an eye on the product’s cost over time, customers can make knowledgeable decisions about what to buy and feasibly end up saving money.
This article will offer advice on how to track a product’s price on Amazon. We will focus mainly on how to use price tracker tools. You can use the data from the tools to forecast future prices with predictive analytics tools. You may become a knowledgeable shopper on Amazon and possibly make savings on your purchases by paying attention to these pointers.
How To Track A Product’s Price On Amazon?
If you frequently purchase on Amazon, you undoubtedly already know that the prices there change frequently. Often a product’s price may go down dramatically, and on other occasions, it may increase. You may save money by keeping track of a product’s pricing, especially if you’re looking for a discount or attempting to find the best possible offer.
Is predictive analytics actually useful for forecasting prices? Research indicates that it can be very beneficial. One analysis shows that it helps brands improve pricing by up to 20%. Therefore, it is logical to assume that it helps consumers predict prices easily as well.
On Amazon, you can conveniently track a product’s price in a few different ways. Using a price tracker website or browser plugin is one of the simplest methods to monitor a product’s price on Amazon. With the help of these tools, you can immediately search for a product on the website or enter its URL, and they will track its price for you and let you know when it changes.
The most excellent way to save money and get the best bargain is to keep tabs on a product’s pricing on Amazon. Every smart buyer will find it helpful to keep an eye on prices, whether they utilize a price tracking website or plugins, add the item to their wishlist, or sign up for price drop notifications from Amazon.
What Is An Amazon Price Tracker?
The price of goods displayed on Amazon’s website is tracked using an Amazon price tracker. Users may set up alerts to be notified when a product’s price reduces or rises, empowering them to make wise buying decisions. The following are some essential details about what an Amazon price tracker is:
To assist consumers in making more informed purchase decisions, it enables them to set up alerts for when a product’s price reduces or rises.
Accessible via websites, browser add-ons, or mobile applications.
Users may keep track of changes in a product’s pricing over time.
While some Amazon price monitors are free, others charge a monthly fee.
Accessible via websites, browser add-ons, or mobile applications are Amazon pricing trackers.
It might be helpful for customers to save money while making purchases on Amazon by telling them about price reductions and promotions.
How To Use Amazon Price Tracker To Buy At The Best Price?
You can keep track of price fluctuations and ensure you’re getting the most terrific deal by using an Amazon price tracker. Keep reading to learn how to use an Amazon price tracker.
Choose a trustworthy pricing tracker: You may use these tools to enter the product’s URL or search for it directly on the website, and they will then track its price for you and let you know when it changes.
Put the product on your tracker: Add the item you wish to purchase to your price tracker once you’ve decided on one. When you add the product, the tool will constantly track the price fluctuations.
Set up price alerts: Price notifications for the products you’re interested in can also be set up. As soon as the price reaches your target level, you can get notifications.
Use price history charts: Some credible price trackers include price history charts so users can see how a product’s price has changed over time. This might help you identify trends and decide if the cost is fair.
Be patient: Being patient is necessary while using a price tracker tool. Wait until the price is at the level you are willing to spend before purchasing.
While making purchases, employing an Amazon price tracker might help you save money and obtain the best deal. You may ensure you obtain the item you want at the greatest price by selecting the appropriate price tracker tool, adding the item to your tracker, configuring notifications, watching the price history, and waiting for the ideal time to buy.
Using Price Trackers and Predictive Analytics Can Help Amazon Customers Get the Best Prices
Predictive analytics tools help us in many ways. One of the biggest advantages of this technology is that it can help you get the best prices on Amazon.
Consumers trying to save costs and get the best bargain possible may find it helpful to monitor product pricing on Amazon and use predictive analytics to make better purchasing decisions. Consumers may make smart judgments about their purchases and perhaps save money by using price tracker tools, configuring notifications, and keeping an eye on the pricing history of items. Anybody can become an expert at watching a product’s price on Amazon and save money by doing so with a little bit of work and smart purchasing abilities.
Data is one of the most important assets of any enterprise. It is essential for making informed decisions, improving efficiency, and providing a competitive edge. However, managing data comes with the responsibility of preventing data misuse. Especially in regulated industries, mishandling data can lead to significant financial and reputational damage. Negative outcomes such as data exfiltration, data access by unauthorized personnel, and inadvertent data deletion can arise if data is mis-managed.
There are multiple ways to help protect data in enterprises. These include encryption, controlling access, and data backup. Encryption is the process of encoding data into ciphertext. If done right, this makes it impossible for unauthorized users to decode the data without the correct key. Access control is the process of limiting access to data to authorized users only. Lastly, being able to audit your data management actions can help in proving that you are following the current regulations that affect your company while protecting one of your core competitive advantages.
It is important to choose the right security solutions for your enterprise. The potential cost of a data breach needs to be considered against the cost of protecting your data. Data security is an ongoing process. It is important to regularly review and update your security processes and tools.
In this blog post, we discuss how you can discover, classify, and protect your most sensitive data using Cloud DLP, Dataplex, and the Dataplex Catalog and Attribute Store. This solution automates complex and costly data practices so you can focus on empowering your customers with data.
In most organizations, data is gathered regularly and this data can fall into one of two categories:
1. Sensitive data for which a specific policy needs to be attached to it according to the contents of the data (e.g. bank account numbers, personal email addresses). These data classifications are generally defined based upon:
a) Applicable regulatory or legal requirements b) Critical security or resilience requirements c) Business specific requirements (e.g., IP)
2. Non-sensitive data
In order to protect sensitive data and be able to follow the compliance requirements of your industry, at Google Cloud we recommend the usage of the following tools:
Data Loss Prevention (Cloud DLP) helps developers and security teams discover, classify and inventory the data they have stored in a Google Cloud service. This allows you to gain insights about your data in order to better protect against threats like unauthorized exfiltration or access. Google Cloud DLP provides a unified data protection solution that applies consistent policies to data in a hybrid multi-cloud environment. It can also de-identify, redact or tokenize your data in order to make it shareable or usable across products and services.
Dataplex is a fully managed data lake service that helps you manage and govern your data in Google Cloud. It is a scalable metadata management service that empowers you to quickly discover, manage, understand and govern all your data in Google Cloud.
Cloud DLP integrates natively with Dataplex. When you use a Cloud DLP action to scan your BigQuery tables for sensitive data, it can send results directly to Data Catalog in the form of a tag template.
This guide outlines the process of sending Cloud DLP results to Dataplex Catalog.
Furthermore, to define how certain data should be treated we are also providing the ability to associate data with attributes through Dataplex’s Attribute Store. This functionality represents a major shift in the approach to governing data as, previously, governance policies could only be defined at the domain level. Now, customers can support compliance with regulations, such as GDPR, by defining data classes, such as Personal Identifiable Information ‘PII data’, mapping the relevant PII attributes, and then defining the associated governance policies.
With Google Cloud, customers can govern distributed data at scale. Dataplex drastically increases the efficiency of policy propagation by mapping access control policies to tables and columns, and applying them to data in Cloud Storage and BigQuery.
Further guidance on how to set up attributes can be found here.
Since Attribute Store, currently in Preview, supports tables published by Dataplex (in a Cloud Storage bucket, mounted as an asset in Dataplex). Soon, we expect Attribute Store will be able to attach attributes to any table.
A reference architecture is shown below, outlining the best practice for securing data using Attribute Store, in conjunction with Data Catalog tags which provide an explanation of the data.
In the above diagram, we see that Table columns are both informationally tagged (using Data Catalog) and associated with an attribute (using Attribute Store). Attribution tagging helps facilitate data protection at scale while the Data Catalog uses tags to describe the data and enhance searchability.
It is important to note that Data Catalog tags are indexed. Therefore, we begin the process by creating this matching DLP infoType for the relevant Data Catalog Tag and Attribute. Then, when DLP matches the infoType, a Data Catalog Tag is created and an Attribute is associated with the data.
Implementing this approach to discovering, classifying, and protecting your organization’s data can help to ensure that you handle this incredibly valuable asset accordingly.
Next steps
To learn more, please refer to the Dataplex technical documentation or contact the Google Cloud sales team.
The growing popularity of cloud solutions is not surprising. After all, there are clearly a number of major benefits of cloud computing. However, there are also some drawbacks.
One of the biggest concerns about storing data on the cloud is the growing risk of a data breach. Unfortunately, cloud systems are not as secure, unless they are setup properly. The good news is that you can enhance cloud security by using a Cloud Access Security Broker (CASB). We will cover the benefits of them in this article.
Companies Need to Use Solutions Like CASBs to Promote Cloud Security
Many companies store their data on cloud servers because it makes it easy for their employees to access them from any location. This makes them vulnerable to data breaches and attacks from cybercriminals. As we mentioned in a recent article, companies need to take data security seriously in the big data age. Unfortunately, this becomes tricky when so much data is poorly secured on the cloud.
As organizations upgrade their systems and cybersecurity tools, these criminals adapt their strategies to counter them. Thus, companies need to take proactive measures to keep their data secure.
A CASB is a security tool companies use to protect their data. This tool is a gatekeeper between a business’s computer network, cloud servers, and cloud-based applications. It provides real-time insight into the usage of their cloud-based platforms so they can prevent unwanted access, malware infections, and other cyber-attacks.
How CASBs Protect Business Data
CASBs are effective in protecting business data because of their numerous features, and here are some of the benefits of those features:
Oversight of the usage of cloud-based applications
Organizations can use this security tool to gain visibility into employees’ cloud usage. This visibility helps cybersecurity teams detect activities that put the organization at risk so they can mitigate them appropriately. This includes discovering if employees use unsecured devices or applications to access the cloud server. Such action poses a severe threat because it creates security gaps cybercriminals can exploit.
Prevention of data loss
The real-time monitoring feature of CASBs helps prevent data loss. This is because it detects suspicious activity when data goes in and out of the cloud server, allowing management to spot data breaches. Cloud Access Security Brokers also classify data based on their sensitivity so the appropriate security protocol can be applied whenever there is a perceived threat.
Access control
Company executives can use CASBs to control employee access to cloud servers. This prevents unauthorized entry and allows them to enforce security measures to keep out intruders. The tool also notifies stakeholders about blocked entry attempts so they can take necessary action.
Compliance with government regulations
Organizations are expected to comply with government data protection and cybersecurity regulations. CASBs help them do this by encrypting sensitive cloud data and protecting it from cyberattacks. They can also detect suspicious cloud-based activity and immediately direct the appropriate personnel to deal with it. This regulatory compliance also favors consumers since their data will remain secure.
Factors to Consider When Choosing a CASB Tool For Business
Companies can use many CASB tools to secure their data better. These are four factors to consider when choosing the right one for your business:
1. Seamless integration with cloud service providers
Your CASB tool should work seamlessly with your business’s cloud services. It should allow you to monitor activities on all your cloud-based applications.
2. Ease of Use
Your CASB should have a user-friendly interface that makes it easy for administrators to use and manage the cloud protection features.
3. Real-time threat protection
The security tool should have machine learning capabilities to detect cyber threats in real-time so they can be dealt with.
4. Solid data protection
It should be able to protect sensitive data and prevent malicious actors from accessing them.
Endnote
Cloud Access Security Brokers help businesses protect their sensitive data stored on the cloud. They do this by rigorously scanning the traffic flowing in and out of their cloud servers and triggering alerts whenever they detect irregularities. The security tool can also deal with these threats on sight and implement measures to prevent them from reoccurring. These nullify the threats cybercriminals pose and prevent unauthorized access to valuable information.
The market for cloud computing is growing faster than we originally anticipated when we started running this blog. A report by Fortune Business Insights states that the market for cloud solutions reached $480 billion last year.
Many people are still trying to get a better understanding of the possible applications of cloud technology. We usually think about the merits of using cloud computing in a traditional office setting. However, there are a lot of other ways that the cloud can be useful.
One of the ways some organizations are using cloud computing is to manage excavation projects more efficiently and minimize mistakes. This is especially helpful with GPS technology that relies on the cloud. We previously wrote about the benefits of using GPS data for geolocation optimization to improve automotive design, but the role of GPS data is more significant with managing excavation projects.
Cloud GPS Technology Has Significantly Improved the Excavation Process
Managing excavation projects can be a daunting task, and often, the success of the project relies on the accuracy and efficiency of the equipment used. The use of excavator GPS technology is rapidly revolutionizing the excavation industry by improving the accuracy and efficiency of excavation projects.
Excavator GPS technology, also known as GPS for excavators, is an advanced technology that uses GPS satellites to locate and track the precise position of an excavator in real-time. The technology enables the excavator to precisely follow the design plan and ensure that the excavation is completed according to the required specifications. This precision ensures that the excavation project meets the required standards and is completed within the set timeframe.
Hagman reports that the cloud makes GPS systems more accurate, partly because it is easier to store location datapoints. The cloud and big data have made GPS systems more accurate.
Improved Efficiency
Excavator GPS technology improves the efficiency of excavation projects by enabling the excavator to work faster and more efficiently by reducing the time required for excavation. This reduction in time not only improves the overall efficiency of the project but also reduces the overall cost of the project. It works even more efficiently when both the excavator and GPS system are synced to the same cloud portal.
Real-Time Information and Safety
Another benefit of excavator GPS technology is that it provides real-time information about the location and status of the excavator. This information is crucial in ensuring the safety of the excavation project as it enables the operator to detect and avoid potential hazards on the job site.
Excavator GPS technology also improves the overall productivity of the excavation project. The technology enables the operator to work more efficiently by reducing the time required to complete the excavation. This improved productivity not only benefits the excavation project but also improves the profitability of the project.
In conclusion, the use of excavator GPS technology is rapidly revolutionizing the excavation industry by improving the accuracy, efficiency, safety, and productivity of excavation projects. The technology is a must-have for any excavation project as it enables the operator to precisely follow the design plan, work more efficiently, and detect potential hazards on the job site. If you are involved in an excavation project, consider incorporating excavator GPS technology to ensure that your project is completed to the required specifications, within the set timeframe, and at a reduced overall cost.
For over a decade, the technology industry has searched for ways to store and analyze vast amounts of data that can handle an organization’s volume, latency, resilience, and varying data access requirements. Companies have been making the best of existing technology stacks to tackle these issues, which typically involves trying to either make a data lake behave like an interactive data warehouse or make a data warehouse act like a data lake — processing and storing vast amounts of semi-structured data. Both approaches have resulted in unhappy users, high costs, and data duplication across the enterprise.
The need for architecture designed to address complex data needs for all users including data analysts, data engineers, and data scientists.
Historically for analytics, organizations have implemented different solutions for different data use cases: data warehouses for storing and analyzing structured aggregate data primarily used for business intelligence (BI) and reporting, and data lakes for unstructured and semi-structured data, in large volumes, primarily used for data exploration and machine learning (ML) workloads. This approach often resulted in extensive data movement, processing, and duplication, requiring complex extract, transform, and load (ETL) pipelines. Operationalizing and governing this architecture took time and effort and reduced agility. As organizations move to the cloud, they want to break these silos.
Moving to the cloud brings otherwise disparate data sources together and paves the way for everyone to become part of the data and AI ecosystem. It is undeniable that organizations want to leverage data science capabilities at scale, but many still need to realize their return on investments. According to a recent study, 91% of organizations increase investments in data and AI. Yet only 20% see their models go into production deployment. Business Users, Data Analysts, Data Engineers, and Data Scientists want to become part of the Data and AI ecosystem.
The rise of the analytics lakehouse
Google Cloud’s analytics lakehouse combines the key benefits of data lakes and data warehouses without the overhead of each. We discuss the architecture in detail throughout the “Build an analytics lakehouse on Google Cloud” technical whitepaper. However, in a nutshell, this end-to-end architecture enables organizations to extract data in real-time regardless of which cloud or datastore the data resides in and use it in aggregate for greater insight and artificial intelligence (AI), with governance and unified access across teams.
By breaking the barriers between data sources and providing serverless architectures, the game becomes choosing the optimal processing framework that suits your skills and business requirements. Here are the building blocks of the analytics lakehouse architecture to simplify the experience, while removing silos, risk, and cost:
What makes Google’s analytics lakehouse approach unique?
Google’s analytics lakehouse is not a completely new product but is built on Google’s trusted services such as Cloud Storage, BigQuery, Dataproc, Dataflow, Looker, Dataplex, Vertex AI and others. Leveraging Google Cloud’s resiliency, durability, and scalability, Google enables customers to innovate faster with an open, unified, intelligent data platform. This data platform is the foundation for Google’s analytics lakehouse, which blurs the lines between traditional data warehouses and data lakes to provide customers with both benefits. Bring your analytics to your data wherever it resides with the the analytics lakehouse architecture. These architecture components include:
Ingestion: Users can ingest data from various sources, including but not limited to real-time streams, change logs directly from transactional systems, and structured, semi-structured, and unstructured data on files.
Data processing: Data is then processed and moved onto a series of zones. First, data is stored as is within the raw zone. The next layer can handle typical ETL/ELT operations such as data cleansing, enrichment, filtering, and other transformations within the enriched zone. Finally, business-level aggregates are stored in the curated layer for consumption.
Flexible storage options: An analytics lakehouse approach which allows users to leverage open-source Apache Parquet, Iceberg, and BigQuery managed storage, Providing users with the storage options and meeting them where they are based on their requirements.
Data consumption: At any stage, data can be accessed directly from BigQuery, Serverless Spark, Apache Beam, BI tools, or Machine Learning (ML) applications. Providing the choice of compute platforms with unified serverless applications, organizations can leverage any framework that meets their needs. Data consumption does not impact processing due to the complete separation of compute and storage. Users are free to choose serverless applications and run queries within seconds. In addition, the lakehouse provides the dynamic platform to scale advanced new use cases with data-science use cases. With built-in ML inside the lakehouse, you can accelerate time to value.
Data governance: A unified data governance layer provides a centralized place to manage, monitor, and govern your data in the lakehouse and make this data securely accessible to various analytics and data science tools.
FinOps: Google’s Data Cloud can auto adjust fluctuations in demand and can intelligently manage capacity, so you don’t pay for more than you use. Capabilities include dynamic autoscaling, in combination with right-fitting, which saves up to 40% in committed compute capacity for query analysis.
“BigQuery’s flexible support for pricing allows PayPal to consolidate data as a lakehouse. Compressed storage along with autoscale options in BigQuery help us provide scalable data processing pipelines and data usage in a cost-effective manner to our user community.” — Bala Natarajan, VP Enterprise Data Platforms at PayPal
Freedom of ‘data architecture’ choice without more data silos
Every organization has its own data culture and capabilities. Yet each is expected to use popular technology and solutions like everyone else. Your organization may be built on years of legacy applications, and you may have developed a considerable amount of expertise and knowledge, yet you may be asked to adopt a new approach based on the latest technology trend. On the other end of the spectrum, you may come from a digital-native organization with no legacy systems at all, but be expected to follow the same principles as process-driven, established organizations. The question is, should you use data processing technology that doesn’t match your organization style, or should you focus on leveraging your culture and skills?
At Google Cloud, we believe in providing choice to our customers — the option of an open platform that minimizes dependencies on a specific framework, vendor, or file format. Not only organizations, but also the teams in each organization, should be able to leverage their skills and do what’s right for them. Let’s go against the school of thought, how about we decouple storage and compute and we do this physically rather than just logically unlike most of the solutions. At the same time, we remove the computational needs with fully managed serverless applications as mentioned earlier. Then the game becomes leveraging the optimal application framework to solve your data challenges to meet your business requirements. In this way, you can capitalize on your team’s skill sets and improve time to market.
Organizations that want to build their analytics lakehouse using open-source technologies can easily do so by using low-cost object storage provided by Cloud Storage or from other clouds — storing data in open formats like Parquet and Iceberg, for example. Processing engines and frameworks like Spark and Hadoop use these and many other file types, and can be run on Dataproc or regular virtual machines (VMs) to enable transactions. This open-source-based solution has the benefits of portability, community support, and flexibility (though it requires extra effort in terms of configuration, tuning, and scaling). Alternatively, Dataproc is a managed version of Hadoop, which minimizes the management overhead of Hadoop systems, while still being able to access non-proprietary, open-source data types.
Bring ML to your data
There are many users within an organization who have a part to play in the end-to-end data lifecycle. Consider a data analyst, who can simply write SQL queries to create data pipelines and analyze insights from BigQuery. Or a data scientist who dabbles with different aspects of building and validating models. Or an ML engineer who is responsible for the model to work without issues to end users in production systems. Users like data engineers, data analysts, and data scientists all have different needs, and we have intentionally built a comprehensive platform for them in mind.
Google Cloud also offers cloud-native tools to build an analytics lakehouse with the cost and performance benefits of the cloud. These include a few key pieces that we will discuss throughout the whitepaper:
Different storage options and optimizations depending on the data sources and end users consuming the data.
Several serverless and stateful compute engines, balancing the benefits of speed and costs as required by each use case for processing and analytics.
Democratized and self-service BI and ML tools, to maximize the value of data stored in the lakehouse.
Governance, ensuring productive and accountable use of data so that bureaucracy does not inhibit innovation and enablement.
Advanced analytics and AI
BigQuery supports predictive analytics through BigQuery ML, an in-database ML capability for ML training and predictions using SQL. It helps users with classification, regression, time-series forecasting, anomaly detection, and recommendation use cases. Users can also do predictive analytics with unstructured data for vision and text, leveraging Google’s state-of-the-art pre-trained model services like Vertex Vision, Natural Language Processing (Text) and Translate. This can be extended to video and text, with BigQuery’s built-in batch ML inference engine, which enables users to bring their own models to BigQuery, thereby simplifying data pipeline creation. Users can also leverage Vertex AI and third-party frameworks.
Generative AI is a powerful and emerging technology but organizations are lacking a way to easily activate AI and move from experimentation into production. Integration with Cloud AI for Generative AIwill embed advanced text analysis with your analytics lakehouse. This opens up new possibilities for your data teams to use AI for sentiment analysis, data classification, enrichment, and language translations.
Automate orchestration of repeatable tasks
Underpinning these architectural components is resilient automation and orchestration of repeatable tasks. With automation, as data moves through the system, improved accuracy instills confidence in end users to trust it, making them more likely to interact with and evangelize the analytics lakehouse.
To learn more about the analytics lakehouse on Google Cloud, download the complimentary white paper.
Editor’s note: Today we hear from Lytics, whose customer data platform is used by marketers to build personalized digital experiences and one-to-one marketing campaigns using a data science and machine learning decision engine powered by Google Cloud.
Data clean rooms are a powerful, and often underutilized, tool for leveraging information across a business, between its brands, and with its partners. Think of a data clean room as a data-focused equivalent of a physical clean room, where the objective is to keep what’s inside the clean room protected. From marketing to IT to the C-suite, clean rooms are a way to transform how teams use data, and according to IAB data, are also now essential business solutions for audience insights, measurement, and data activation. Still, this only scratches the surface of what’s possible with clean room technology. And as a result, IAB predicts that companies will invest 29% more in 2023 to make the most of their data clean room capabilities as they look ahead.
Lytics’ customer data platform (CDP) enables organizations to connect more meaningfully with their customers. And together with Google Cloud, we recognize that it’s time to fundamentally reimagine what retailers and consumer packaged goods (CPG) brands can really accomplish with data — and in particular, with data clean rooms.
Why are clean rooms so valuable?
Clean rooms have been used for many years in the finance and health industries to enhance data while maintaining security, but are now becoming increasingly ubiquitous among retail and CPG brands that are navigating a heavily privacy-minded present.
Data, collected by brands directly as opposed to via a third-party, cross-site cookie, is only going to continue to rise in value. Clean rooms make it possible to embrace, with ease and with confidence:
Data sharing and activation. Having better information available to marketing, IT, and executive teams strengthens the entire enterprise. At the same time, you can keep your PII protected and eliminate concerns about data risks.
Enhanced customer profiles. Data today comes from a myriad of sources. By pooling data in clean rooms, you can augment your existing customer profiles with more details, enriching the view of your customers across teams.
Contact sharing. Contact information is critical for outreach. Profiles without accurate contact information are not actionable, so adding these details using clean rooms is invaluable.
Match lists. Comparing lists and matching contacts to certain traits or identities becomes possible, with more data points available.
Joint campaigns. Campaigns run in collaboration with other parts of the enterprise help with efficiency, accuracy, and consistency of message.
Identifying clean room use cases across an enterprise
Data clean rooms are critical to retail and CPG organizations that want to commoditize the information they collect and store, but they often get mistaken as a tool designed primarily to benefit the technical and data teams responsible for enterprise-wide data quality and pipeline health. On the contrary, there are a long list of core benefits of using a data clean room across internal units.
Compliance is an ever-present, ever-growing aspect of marketing operations today, as the compliance landscape is constantly shifting, adding new updates and mandates to consider. Using a data clean room provider lets marketers eliminate the guesswork of compliance: staying up to speed on all the possible variances and changes. For marketing teams, it’s a tremendous savings of time and money to ensure that your data policies and usage are compliant.
2. Leveraging data anonymization
Imagine the possibilities of gaining information on your customers or groups of customers without knowing their identities. Anonymization is an effective way to market while protecting customer data privacy. In fact, the infamous personalization-privacy paradox can be solved using anonymized data via data clean rooms. Even if that data does contain personally identifiable information, it’s usable because it can be scrubbed via encryption or hashing in the clean room.
3. Embracing better profile optimization
Data clean rooms let you analyze, manipulate and filter data contained in consumer profiles. For example, you can strip away third-party data and look at just first-party data, or take your existing first-party data and overlay it with third-party data. Clean rooms let you consider different factors, such as groups that may respond to one email message or product feature but not another. For A/B testing, segmenting and list building, the data clean room gives you powerful capabilities. All the while, data will remain protected and secure.
For IT and data teams, clean rooms offer possibilities that can enhance and expand the impact of your data:
1. Leveling up your data privacy and security
Privacy and security are top of mind for IT professionals across industries, but especially in retail and CPG where customer-centricity is critical to success. Every day, new cyberthreats emerge that can jeopardize business operations and brand reputations. A compromised system due to a ransomware attack or a data loss can be exceedingly costly. A data clean room can offer your brand a safe environment in which the business can manage, organize and use data. It’s a powerful solution that allows you to work with data while reducing the risk of exposure or compromise.
2. Enhancing machine learning (ML)
Artificial intelligence and machine learning are increasingly used across IT teams in multiple applications. The key is to have enough data to feed into the algorithms to improve learning. Data clean rooms offer expansive arrays of rich, actionable information. This information can help improve how machines learn and adapt, building better models that are smarter and more accurate. With better models, you’ll be able to expand the insights provided and generate better results.
For executives and leadership teams, the list of use cases continues:
1. Driving more revenue and reducing costs
Data sharing means opportunity, both in the costs of managing data and the financial possibilities. For one, streamlining your data management processes means organizations can significantly lower data management expenses. It will also lower your operational costs through the efficient access, analysis, and use of the data you have.
2. Locking in more partnerships
By partnering in the sharing of data, organizations are able to create new relationships that can safely and richly expand the brand. With new data partners and new datasets, you can find new commonalities that can create new and unforeseen business opportunities. The potential impacts on the organization are expansive. Retailers can, for example, leverage the data and relationships to forge new opportunities in product development, customer service, marketing and sales.
All of that data inevitably will lead to deeper insights and discoveries. These new observations can lead to new income streams that monetize data in heretofore unimaginable ways. These may be new streams, new products and services, and, in some cases, new business models.
About the Lytics Clean Room Solution, built with BigQuery
Lytics and Google Cloud have developed a scalable and repeatable offering for securely sharing data, utilizing Analytics Hub, a secure data exchange, within BigQuery and the Lytics Platform.
Lytics Clean Room Solution is a secure data sharing and enrichment offering from Lytics that runs on Google Cloud. The solution boasts an integration with BigQuery that makes Lytics an ideal application to simplify and unlock data sharing by unifying and coalescing datasets that help businesses to build or expand existing BigQuery data warehouses. With Analytics Hub, Lytics offers capabilities that improve data management needs on behalf of organizations focused on maximizing the value of that data, and can decrease the time to value in complex data sharing scenarios, meaning that partnership collaboration is safe and secure — and cross-brand activation can be done in just a few hours.
With the Lytics Clean Room Solution, retailers and CPG brands alike can securely share data hosted on BigQuery and activate shared data into customer profiles. The solution provides tighter control of mission critical data for faster activation, and can also be leveraged to comply with stringent privacy constraints, industry compliance standards and newer regulations.
Data clean rooms provide an extraordinary opportunity to transform the way you do business and connect with customers. Especially in retail, technologies that are complementary, integrated and scalable, like clean rooms, allow you to maximize their capabilities and turn your enterprise data tools into business accelerators — but only if you have the foresight to make (and maximize) the investment.
Read the new ebook from Lytics and Google Cloud to learn more about how retail and consumer brands can unlock business value with data that is connected, intelligent and secure.
We’re excited to announce that Dataflow Prime now supports vertical autoscaling for batch jobs.
Addressing out-of-memory errors in batch Dataflow Prime jobs
While Dataflow is a reliable service, and supports pipelines that have been running for years unchanged, there are situations where workers may experience memory issues, leading to out-of-memory (OOM) errors. In the current system, when a work item fails due to an OOM error, it is retried up to four times. If the work item still fails, the entire job fails, causing all successfully processed work to be discarded. This not only results in wasted processing costs, but also produces no output.
Moreover, resolving OOM errors often involves relaunching the job with increased memory capacity, which can be a time-consuming and costly trial-and-error process.
We developed vertical autoscaling to address the challenges associated with out-of-memory (OOM) errors in Dataflow Prime jobs, enabling you to focus on your application and business logic. We launched vertical autoscaling for streaming pipelines in August 2022, and are excited to announce the GA launch of vertical autoscaling for batch Dataflow Prime jobs.
With vertical autoscaling for batch Dataflow Prime, OOM events and memory usage are monitored over time, and memory upscaling is triggered automatically after four OOM errors to prevent job failures. This ensures that your batch Dataflow Prime jobs are resilient to memory errors without requiring any manual intervention.
By using vertical autoscaling, you can reduce the risk of job failures due to memory errors and improve the overall reliability and efficiency of your Dataflow Prime jobs.
Getting started
To enable vertical autoscaling for batch Dataflow Prime jobs, head on over to the documentation page for more details.
Inventory management is a critical part of any supply chain. By keeping track of inventory levels, businesses can ensure that they have the right amount of products on hand to meet customer demand, while also avoiding overstocking or understocking. However, managing inventory can be a challenge, especially in today’s complex and ever-changing supply chains.
Data is often siloed within different departments or systems, making it difficult to get a complete view of inventory levels across the entire network of the business. Meanwhile, a focus on supply chain visibility and insights has only increased with the risk of supply chain disruptions, scarcity, and delays along with demand volatility and rapidly changing business dynamics and trends.
The journey to overcome these data challenges has traditionally been a difficult one. Existing solutions have often lacked a unified semantic view for easier adoption by users. Data has often been fragmented and with limited means to integrate. Traditional platforms also lacked unified AI capabilities, making use of AI/ML technically complex and time consuming.
To help with these data challenges, many companies choose Google’s Data Cloud to integrate, accelerate and enhance business insights through a cloud-first data platform approach with BigQuery to power data-driven innovation at scale. Next, they take advantage of best practices and accelerator content delivered with Google Cloud Cortex Framework to establish an open, scalable data foundation that can enable connected insights across a variety of use cases.
Today, we’re excited to announce the latest Cortex Data Foundation release which includes packaged analytics solution templates and content focused on inventory management.
Introducing new analytics content for Inventory Management
Release 4.2 of Google Cloud Cortex Framework includes new data marts, semantic views and templates of Looker dashboards to support operational analytics and relevant metrics for inventory management. These are designed to help overcome the above challenges, and provide a starting point for customers to adapt further to the specific needs of their business. Sample dashboards give visibility to common industry KPIs tracked by everyone from supply chain executives (such as a Chief Supply Officer) to operational managers (such as inventory managers), such as:
Inventory across stock categories (unrestricted, restricted) with ability to drill down to the details
Past inventory, to spot trends and benchmark KPIs (such as Inventory Value, Quantity, and Days of Supply)
Inventory that may age
Slow moving inventory
Inventory management is also closely linked to other aspects of the supply chain. More broadly, these insights can be combined with related content, both in this new release (such as vendor performance) and with existing content to give a broader picture of your supply chain health.
What’s next
This release extends prior content releases for SAP and other data sources to further enhance the value of Cortex Data Foundation across private, public and community data sources. Google Cloud Cortex Framework continues to expand content to better meet our customers’ needs on data analytics transformation journeys. Stay tuned for more announcements coming soon!
To learn more about Google Cloud Cortex Framework, visit our solution page, and try out Cortex Data Foundation today to discover what’s possible!
Enterprises running SAP for procurement and accounts payable are always looking to create greater efficiencies by combining these data sets to monitor vendor performance for quality and reliability, analyze global spend across the organization and optimize the use of working capital to make timely vendor payments that earn the highest discounts.
But in today’s inflationary environment, the need to analyze data from procure-to-pay processes is more important than ever as rising prices threaten to reduce purchasing power and erode real income. Many of these enterprises are looking for accelerated ways to link their enterprise procure-to-pay data with surrounding non-SAP data sets and sources to gain more meaningful insights and business outcomes. Getting there faster, given the complexity and scale of managing and tying this data together, can be an expensive and challenging proposition.
To embark on this journey, many companies choose Google’s Data Cloud to integrate, accelerate and augment business insights through a cloud-first data platform approach with BigQuery to power data-driven innovation at scale. Next, they take advantage of best practices and accelerator content delivered with Google Cloud Cortex Framework to establish an open, scalable data foundation that can enable connected insights across a variety of use cases. Today, we are excited to announce the next offering of accelerators available that expand Cortex Data Foundation to include new packaged analytics solution templates and content for procure-to-pay processes.
Introducing new analytics content for procure-to-pay
Release 4.2 of Google Cloud Cortex Framework includes new data marts, semantic views and template Looker dashboards to support operational analytics on three procure-to-pay topics and the relevant metrics for each.
Leverage the metrics delivered in our new Accounts Payable content to identify potential issues and areas for optimization with respect to short-term obligations to creditors and suppliers which have not yet been paid:
Accounts Payable Balance Due
Days Payable Outstanding
Accounts Payable Aging
Accounts Payable Turnover
Accounts Payable by Top Vendors
Upcoming Payments and Penalties
Cash Discount Utilization
Blocked and Parked Invoices
Analyze procurement spend on goods and services across your organization and identify opportunities to reduce costs and improve efficiency with the following metrics delivered in our new Spend Analysis content:
Total Spend
Spend by Top Vendors
Spend by Purchasing Organization
Spend by Purchasing Group
Spend by Vendor Country
Spend by Material Type
Active Vendors
Use the metrics included with our new Vendor Performance content to improve efficiency and profits by comprehensively analyzing the delivery performance, quality, accuracy and unit costs of your suppliers and then take tactical decisions to improve your bottom line by increasing business with your most reliable and lowest cost vendors:
On-time Delivery Performance
In-full Delivery Performance
Rejections Rate
Invoice Accuracy
Purchase Price Variance
Vendor Lead Time
Open Purchase Orders
What’s next
This release extends upon prior content releases for SAP and other data sources to further enhance the value of Cortex Data Foundation across private, public and community data sources. Google Cloud Cortex Framework continues to expand content to help better meet our customers’ needs for data analytics transformation journeys. Stay tuned for more announcements coming soon!
To learn more about Google Cloud Cortex Framework, visit our solution page, and try out Cortex Data Foundation today to discover what’s possible!
