Chronicle Security Analytics: Key to Address Security Data Overload - Build What's Next

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Chronicle Security Analytics: Key to Address Security Data Overload

Google Cloud’s Chronicle is a security analytics platform built for modern use cases to combat modern threats. In today’s world, enterprises have undergone significant changes in all aspects, and must adapt to their security needs to counter threats and attacks. Watch the video to learn Google Cloud’s initiative to help businesses improve their security by 10x to catch up with the way the world is changing and how Chronicle Security Analytics is poised to help address data security challenges.

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Google is a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics

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Gartner's evaluation covers Google Cloud’s core data analytics offerings, including BigQuery, Cloud Dataproc, and Cloud Dataflow. Here are some of the key takeaways.

As organizations continue to produce vast quantities of data, they increasingly need platforms that allow them to analyze, store, and extract meaningful insights from that data. And research from analyst firms like Gartner offer an important way for organizations to evaluate and compare cloud data warehouse providers.

Earlier this year, Gartner named Google a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics (DMSA) (report available here). This evaluation covers Google Cloud’s core data analytics offerings, including BigQuery, a serverless, managed data warehouse, Cloud Dataproc, a managed Spark and Hadoop service, and Cloud Dataflow, which enables you to stream and batch-process your data. Here are a few takeaways:

Simplicity and speed

BigQuery’s performance permits complex queries on large-scale data sets to return in seconds, and a substantial number of BigQuery customers maintain data warehouses that store more than 50 terabytes (and a few customers now use more than 100 petabytes). More than half of these customers are loading data either continuously or many times per day. These customers value the ability to extract, transform, load, and analyze their data on a serverless platform, all without maintaining any underlying infrastructure.

A versatile serverless data warehouse

One of BigQuery’s major advantages is its ability to allow customers to address a wide variety of use cases—from a traditional data warehouse to data science. Over the past year, we’ve worked hard to introduce new features in BigQuery like data types for financial and monetary uses, BigQuery GIS for geospatial data, and machine learning capabilities through BigQuery ML. BigQuery’s continuous ingest capabilities make it suitable for an operational—or a real-time—serverless data warehouse.

An expanding ecosystem

With overall increased market adoption, our analytics offerings continue to benefit from a fast-growing partner ecosystem of service providers, and business intelligence (BI) and data integration vendors. In particular, in 2018 we expanded our partnerships with established industry providers, including Confluent, Dell Boomi, Informatica, Looker, Reltio, Tableau, and ThoughtSpot.

More and more organizations are finding value in Google Cloud’s serverless data warehouse and analytics offerings. If you’d like to learn more, you can download a complimentary copy of the Gartner Magic Quadrant for Data Management Solutions for Analytics on the Google site (requires an email address).

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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The New and Upcoming Infrastructure for Google Cloud’s AI and ML Solutions

How does Google manage to provide its customers a differentiated compute platform experience and define ways to fully leverage its infrastructure supporting its cutting-edge AI and ML offerings? Easy-to-use, scalable and ability to create innovative products and services to end-users at low cost of ownership is the narrative behind Google Cloud’s AI and ML solutions. Explore Google Cloud’s ML infrastructure and accelerator innovation for 2021.

Watch the video to find out how Google Cloud’s leadership in AI through Google research, Deep Mind and also practical application of AI within Google Products drive innovative platforms and offerings that cater to customers’ AI and ML use cases!

Trend Analysis

2022 Healthcare Trends: Healthcare Data, M&As, Better Patient Care, AI in Drug Development & Strategic Partnerships

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Despite the challenges that put global healthcare systems to test, the COVID-19 pandemic was an opportune moment to embed technology at the core of healthcare and life sciences. This year, unlocking data will dictate its growth and value generation!

The COVID-19 pandemic continues to push the healthcare and life sciences industry in entirely new ways. In record time, we’ve witnessed public health officials, vaccine developers, equipment manufacturers, and essential workers take life saving actions—regularly putting their own lives at risk—to respond to the exceptional challenges of our time. 

Yet after all the turmoil and uncertainty of the pandemic, record breaking levels of investment continue to come into the market to fuel innovations. 

Vaccine development is now measured in weeks rather than years; providers are leveraging telehealth technologies to improve the physician and patient experience; and individuals have embraced a variety of devices to assume greater ownership and control of their personal health. 

With that in mind, here are five of the many innovations I see driving healthcare and life sciences for at least the next 12 months: 

1. Unleashing the power of healthcare and life sciences data

People have arguably never had as much access and understanding to their personal health data than they can in 2022. They have the ability to understand their genetic makeup, medical history, family history, and activity levels to ensure they are living a healthy lifestyle. Taken together, this longitudinal profile can become the basis for more personalized medicine. Add wearable devices to the mix and patients gain real- or near-real-time updates on their health status. 

Further, global regulatory agencies have continued to mandate the need for providers to maintain a longitudinal health record that provides a holistic view of the patient across all the encounters they have had with a health system. Physician surveys conducted by the The Harris Poll and Google Cloud show that a 360-degree view of the patient, across all provider encounters, leads to faster, more accurate diagnosis, and better outcomes. 

If secure data access and interoperability can begin to include insurers, researchers, public health officials, and others in the field, the powerful network effects benefiting patients will only grow. When combined with those longitudinal phenome profiles, the opportunities for personalized and preventative medicine enter a whole new era.

2. Healthcare and life sciences M&A boom continues

Although the pandemic initially slowed activity on mergers and acquisitions in the early part of 2020, the healthcare and life sciences industry has seen a rapid rebound and acceleration of deals ever since. The success of COVID-19 vaccines, the importance of telehealth, and the focus on molecular modeling and genomic-based drug development have all boosted investment as organizations look to enter new markets, develop new therapies, and leverage low interest rates while they last. 

In 2021, the total funding of US digital health startups surpassed $29 billion across 729 deals, according to advisory Rock Health. That’s almost double the levels of 2020, which itself set records. Analysts at PWC meanwhile estimate M&A investments in biopharmaceutical and life sciences could approach $400 billion this year across all sub-sectors

Clearly, the pandemic has been a primary driver of investment as the focus on healthcare has dramatically increased. Yet the increased activity also reflects changing business models and emerging technologies that are now required to compete in the rapidly evolving space. For organizations to capitalize on these investments, it will take not only great vision and intellectual property but also the right technologies—like cloud—and the right data interoperability models, to make partnerships and acquisitions more scalable, feasible, and seamless.

3. Transforming the patient experience at a new rate

The pandemic has shined a spotlight on the inefficiencies and complexities that exist in healthcare markets across the globe. As wave after wave has surged, global healthcare systems remain overwhelmed on most every aspect of patients’ treatment journeys. Even before COVID-19, healthcare was already one of the largest spend areas for governments around the world. The pandemic has only exacerbated the known issues. 

Clearly, administrators, regulators, physicians, nurses, and patients would agree that the processes and models need to change. There’s a need to maintain this momentum and even increase the tempo to achieve lasting change.

Take telehealth. Within months of the start of the pandemic, providers moved to provide more remote capabilities so physicians could still meet with patients virtually to ensure health and safety on all sides. Payers recognized the importance of telehealth and began to update reimbursement rules. And organizations are now reimagining policies in areas such as prior authorization, submission, and adjudication to reduce complexity and bureaucracy while improving responsiveness.

Looking beyond the system, organizations are also recognizing and deepening their understanding of the structural and social determinants of health that impact patient care and health outcomes, especially for historically underserved communities.  Private and public sectors are learning from, and increasingly partnering with, the social sciences, public health, biomedical informatics, computer science, public policy and community groups around how to build a mI’ore equitable and inclusive consumer products and Health IT strategies. 

The newfound levels of transparency, visibility, and accountability that patients, caregivers, and organizations are achieving will ultimately increase competition and provide a more effective, equitable, efficient and, above all, healthier marketplace for all patients. As we move past the worst of the pandemic, regulators and organizations should keep fighting for progress over business as usual.

4. AI is now a core competency for Drug Development

The ability of organizations like Pfizer, Moderna, Johnson & Johnson, and Astrazeneca to develop COVID-19 vaccines has been a remarkable accomplishment—particularly the historic speed with which they were created and deployed. This innovation acceleration was largely enabled by the use of new drug development platforms that allow researchers to use artificial intelligence and machine learning to model protein and cellular interactions to rapidly advance the science.

No longer must researchers rely on traditional laboratory testing (and retesting). With their improved understanding of the molecular and genetic structure of a patient and, for example, their tumor, researchers can use AI to enable simulations on computers rather than testing in live conditions. This technology can process thousands, even millions of simulations to help  identify high-potential candidates for treatment consideration and subsequent analysis. 

AI-enabled drug discovery models can eliminate months and years from the research process, which can reduce the time to develop a drug and accelerate the time to treatment for an individual patient. As just one example, consider the work on AlphaFold2 by Google’s DeepMind unit who leverages AI to predict effective protein shapes for new drugs. Healthcare and life sciences organizations already recognize the potential of AI. Now comes the investments to leverage this rapidly evolving technology to support their efforts now and in the future.   

5. Ecosystem partnerships tackle complexity and spur innovation

As the importance and growth of the healthcare and life sciences industry continues, we will see even more new players and partnerships emerging to address old problems in new ways. This trend will touch all aspects of the healthcare value chain and will, increasingly, see three- and four-player partnerships emerge to address the complex challenges of today’s healthcare marketplace.

Technology will continue to play a key role as capabilities and platforms will transform all aspects of the marketplace. Cell phones, wearable devices, and other technologies will provide real-time updates and notifications to patients on everything from glucose levels to payments for healthcare services. Voice recognition software will document physician and patient discussions to reduce the burden of record keeping. Real-world data will be used to simplify and confidentially recruit patients for participation in clinical trials. 

New players will continue to enter the market to improve health outcomes and reduce costs. Major retailers are among the companies extending their pharmacies to provide additional diagnostic and concierge services, saving patients from additional appointments while boosting prevention. Community organizations are emerging to help identify and care for underserved communities whose health outcomes are significantly lower than the average patient. 

For all the exhausting and heart-wrenching challenges of the past two years, the opportunities the pandemic has laid bare cannot be overlooked. We owe it to those who have worked and fought so hard for every life to forge even more new partnerships—and make it easier to do so—so that the next crisis, when it does arise, will never be as bad as the one we’re now conquering. Technology can be the enabler in this effort and help bring us together to continue to conquer the challenges that lie ahead.

How-to

3 AI Tools You Can Deploy Immediately

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AI has the power to revolutionize every industry—from retail to agriculture, and education to healthcare. Yet many businesses still haven’t begun to adopt AI.

There are a number of factors, including the need for specialized talent and hardware, the right types and quantities of data for training and refining machine learning models, and of course, the broader complexities of introducing a new way of working inside an organization.

But above all, AI can be challenging to apply to very specific business needs.

Google wants to change that, which is why it’s introducing a number of AI solutions aimed at making it easier for businesses to use AI to address many of these specific use cases.

Google’s AI solutions fall into two categories. The first is a set of pre-packaged AI solutions that can be easily integrated into existing workflows. To make these solutions as easy to implement as possible, Google works with popular, trusted enterprise partners who have deep-domain expertise in these workflows.

The majority of Google’s pre-packaged solutions will be delivered through these partners, although it will be offering some directly to customers as well.

The second category of AI solutions is comprised of reference architectures that businesses can use to create highly-custom AI tools. These require more development work than Google’s pre-packaged solutions, but simplify complexity for enterprises that need highly business-specific, integrated AI deployments.

Additionally, businesses can build on these architectures and deploy them to market more quickly, helping your business take advantage of AI, faster.

Learn more about all Google’s AI solutions. Download the whitepaper now!

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Explainer

What is BigQuery?

BigQuery is Google Cloud’s enterprise data warehouse designed to help you ingest, store, analyze, and visualize big data with ease.

Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it readily available for data analysis that supports their strategic decision-making.

You can ingest data into BigQuery either through batch uploading or streaming data directly to deliver real-time insights.

As a fully-managed data warehouse, Google takes care of the infrastructure so you can focus on analyzing your data up to petabyte scale.

BigQuery supports the same Structured Query Language, or SQL, for analyzing your data, which you may be familiar with if you’ve worked with ANSI-compliant relational databases in the past.

If you’re looking to create machine learning models using your enterprise data, you can do so with BigQuery ML.

With only a few lines of SQL, you can train and execute models on your BigQuery data without needing to move it around.

When it comes time to visualize your data, BigQuery integrates with Looker, as well as several other business intelligence tools across our partner ecosystem.

Now, how do you use BigQuery?

Luckily, it’s straightforward to get up and running with BigQuery.

After creating a GCP project, you can immediately start querying public data sets, which Google Cloud hosts and makes available to all BigQuery users, or you can load your own data into BigQuery to analyze.

Interacting with BigQuery to load data, run queries, or even create ML models can be done in three different ways.

First is by using the UI and the Cloud Console. Second is by using the BigQuery command line tool. And third is by making calls to the BigQuery API, using client libraries available in several languages.

BigQuery is integrated with Google Cloud’s Identity and Access Management Service so you can securely share your data and analytical insights across the organization.

What does it cost to use BigQuery?

With BigQuery, you pay for storing and querying data and streaming inserts.

Loading and exporting data are free of charge.

Storage costs are based on the amount of data stored and have two rates based on how often the data is changing.

Query costs can be either on demand, meaning you are charged per query by the amount of data processed, or flat rate for customers who want to purchase dedicated resources.

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