Archives October 2023

Built with BigQuery: Drive growth through data monetization

Built with BigQuery: Drive growth through data monetization

Editor’s note: The post is part of a series showcasing partner solutions that are Built with BigQuery.

Since the launch of Built with BigQuery was first announced at the Data Cloud Summit in April 2022, we’ve gained some incredible momentum. Now, over 1000 ISVs and data providers are using BigQuery and Google Data and AI Cloud to power their applications.

At Google Cloud Next this year, we were lucky enough to be joined by some of these partners in person, where we talked about driving growth through data monetization.

The four companies we spoke with, Exabeam, Dun & Bradstreet, Optimizely and LiveRamp are all leaders in their fields, so we wanted to share some of the key ways they are using BigQuery and Google Data and AI Cloud to drive growth through data monetization.

Data monetization

Organizations across industries have seen an explosion in the data they generate over the last decade and it’s often said to be a company’s most valuable resource. However, the reality is that the raw data generated is often little more than a byproduct of other processes, and can be costly to store, protect and govern. To have value, and to be monetizable, raw data must be transformed into a data product. So what do we mean by a data product? At a minimum, data products must:

Have a development lifecycle, roadmap and distributionProvide value to customers by satisfying a need or wantProvide value to data providers through direct (i.e. net new revenue via upsell & cross sell) and/or indirect (e.g. Increased differentiation, usage & stickiness) monetization

When working with ISVs and data providers across the ecosystem, we see four key recurring patterns that use the BigQuery suite to power data products.

Embedded BI: Embedded visual analytics & self-service discovery with Looker. For an excellent deep-dive into this topic, see the Google Cloud Next ‘23 session Data monetization through embedded analytics.Gen AI/ML: Predictive and Generative AI with BigQuery ML & Vertex AI. To hear more about the latest innovations in this space, check out the webinar: How ISVs can accelerate Gen AI adoption with BigQueryData sharing: Simple, secure, zero-copy sharing and clean rooms with Analytics Hub. Learn more about the latest innovations in data sharing and data clean rooms, at the Google CLoud Next ‘23 session: Share securely with data clean roomsData warehouse as a service: Partners can enable their customers with the full Analytical and Data Warehouse capabilities of BigQuery, by providing them as a service. A great example of this is Bloomreach Engagement BQ.

Foundational to all of these is the need to unify datasets into a common data and analytics platform.

In the stories that follow, you’ll see examples of how companies are leveraging these patterns to monetize their data assets and deliver value for their customers.

Exabeam delivers consistent and repeatable security investigations

Exabeam, Google Cloud Tech Partner of the year for Security Analytics and a global cybersecurity leader delivering AI-driven security operations, provides security operations teams with end-to-end Threat Detection, Investigation, and Response (TDIR).

Andy Skrei, VP of Product Management, explained that when Exabeam adopted BigQuery two years ago, their key goal was to deliver a platform-level data store that could collect and normalize all data once, and then use it in different ways through different applications and data products from search through detection and response. One of the primary drivers was security analytics, and the company needed a data store that could scale to drive the detection capabilities that its customers expect.

Exabeam also wanted to give customers access to the data store to query it during security investigations. For this, they needed customers to have the ability to ingest all of the data they care about; to be able to retain that data for up to 10 years based on compliance and investigation needs; and be able to access that data at scale quickly. They also needed to make sure that customers had the ability to craft complex queries, access the right insights, and aggregate that data.

The key to all of these need is making sure that the solution can scale based on demand. When you’re in an incident or a breach, you may have 10x the number of queries, and people using that service need it to be highly available.

Their platform approach has also enabled them to quickly layer on additional capabilities like advanced AI-based behavior analytics that normalizes and learns the behavior of every user and device to identify anomalies and threats. They have been using AI for years to detect and defend against threats, and they’re now looking at generative AI to ​​derive new insights and accelerate the ability to understand problems. This includes using generative AI to tell multiple sides of a security event. For example, the information and level of detail that a CISO will need is different than that of an analyst. Generative AI can be used to tell and enhance each of these stories, even though they’re reporting on the same incident.

“We’ve been using AI from the very beginning to help detect threats and behaviors but generative AI is a really interesting space for us now. Google is one of the only companies that offers a commercially available LLM focused on cybersecurity and having that security expertise built-in is important so we can start to leverage and productize generative AI for our customers.” – Andy Skrei, VP Product Management, Exabeam

Dun & Bradstreet prioritizes security, infrastructure and scale

Dun & Bradstreet is a leader in data and business information serving over 525 million companies across the globe.

Leigh Luxenburg, Senior Director of Product Management explained that Dun & Bradstreet needed a single location where all of its data could work together, efficiently — and this was a big driver in the company’s decision to move not just its data, but all of its platforms, to Google Cloud.

In doing so, Dun & Bradstreet is able to fuel its products, data, SaaS offerings and models for its customers that use the data much more efficiently and consistently, and without latency issues. After choosing BigQuery for its efficiency and consistency, Dun & Bradstreet’s customers are now noting how fast and smooth its processes are.

Data sharing at scale is at the heart of what Dun & Bradstreet does and BigQuery Analytics Hub has enabled them to open new sales and marketing avenues by creating a catalog of data offerings that can be easily subscribed to by BigQuery customers.

We were thrilled that Leigh announced Google Cloud’s new generative AI partnership, with Vertex AI already being a foundational technology in their AI lab. The new partnership will allow customers to develop applications and models using Dun & Bradstreet data, so they can understand things like who they should be targeting from a sales and marketing perspective, as well as who they should do business with from a risk perspective.

They also plan to use AI to deliver new data products that make information more understandable and readable for customers, for example, automated chat agents that can help provide information across a number of data sets easily and conversationally, without having to know teams of people behind the scenes providing that information.

“With 180 years of data and business information for over 525 million companies across the globe, having one place where all that information can work together efficiently was a big driver in our decision to move not just our data but all of our platforms to Google Cloud.” — Leigh Luxenburg, Senior Director of Product Management

Optimizely helps customers experiment and drive action, fast

Optimizely is a leader in delivering personalized and optimized digital experience delivering 2 million experiments across 10,000 customers.

According to Spencer Pingry, VP Software Architecture, a range of products at Optimizely are powered by Google Data and AI Cloud, but their partnership also extends into the wider Google products suite. For example, using BigQuery, they have integrated with Google Analytics to establish common reporting so customers can view Optimizely experiment results within Google Analytics. Soon, they will be able to take audiences defined in Google Analytics and activate them directly through Optimizely Experimentation – so the audiences that they define can be delivered all the way through to the experience while measuring outputs.

Building on the data sharing theme, Spencer explained how their customers take Optimzely’s rich data and integrate it into their own BigQuery datasets to make it available across their teams.

Google’s partnership has provided Optimizely with access to the tools and training needed to start using generative AI. This enables product teams and engineers to reduce friction and move quickly — starting with enhancing their content generation solutions, but also more broadly through building a range of data products that can leverage the customer context across Optimizely’s entire suite of solutions.

“We’re really interested in how to think about data products internally and then map them across the suite of our products so that we can apply these AI models, not just in a specific point use case, but really with the context of our clients across all of our products. Google’s been a great partner in helping us enable those things.” – Spencer Pingry, Optimizely

LiveRamp enables identity-powered data collaboration

LiveRamp, Google Cloud Global Partner of the year for Industry Solution Technology, enables marketers and data teams to accurately and safely enrich, match and activate audiences for marketing.

Erin Boelkens, VP Product Management, explained that LiveRamp runs on Google Data and AI Cloud and the company has deep integrations with Google Marketing platform, making customer transitioning and onboarding easier and faster. With full visibility across the data organization suite, the company is able to optimize budgets and resources. Being hosted on Google Cloud and integrated with Google products provides the enormous benefit of everything being in the same place. The more consolidated the data, the easier it is to transact and have full visibility across a customer’s entire data product suite. This unification of data breaks down silos and offers a true 360 view across customer data so businesses can understand how their customers are interacting with them, no matter what channel it’s through.

Data clean rooms are a powerful way of enabling identity resolution for customers in a safe and effective manner. LiveRamp’s solution, the LiveRamp Data Collaboration Platform, is a great example of a DWaaS that enables them to provide a trusted environment to customers with a ready-to-go first-party data strategy.

More recently, LiveRamp has been an early design partner for Google’s Data Clean Room solution, based on Analytics Hub, which was announced at Google Cloud Next ‘23, in private preview.

At the heart of LiveRamp is its AI-powered identity graph. They have used several AI methodologies over the years but one that continues to be leveraged is the transformer architecture, which was invented by Google, along with their own algorithms to continually drive accuracy and match rates in every LiveRamp product.

“The better we can connect the data in the ecosystem, the better we can help companies understand every touch point in the customer journey, and the more successful they’ll be for their business outcomes. Google’s AI, and our own algorithms, are helping to pave the way.” – Erin Boelkens, LiveRamp

The Built with BigQuery advantage for ISVs and data providers

To learn more about these customer stories you can watch the full session on demand here. Don’t forget to register for our upcoming webcast on Oct 24th to learn how ISVs can accelerate gen AI adoption with BigQuery.

Built with BigQuery helps ISVs and Data Providers build innovative applications with Google Data and AI Cloud. Participating companies can:

Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practicesAmplify success with joint marketing programs to drive awareness, generate demand, and increase adoption

BigQuery gives ISVs the advantage of a powerful, highly scalable unified AI lakehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. Click here to learn more about Built with BigQuery.

Source : Data Analytics Read More

Built with BigQuery: Expedite GTM insights with ZoomInfo B2B data in Analytics Hub

Built with BigQuery: Expedite GTM insights with ZoomInfo B2B data in Analytics Hub

Editor’s note: The post is part of a series showcasing partner solutions that are Built with BigQuery.

ZoomInfo is a go-to-market platform that helps businesses find, acquire, and grow customers. They collect, verify, publish and update millions of contact and company profiles every day to help businesses power go-to-market motions with the most accurate company and contact data. Whether you’re looking to improve data quality, create a single source of truth, or advance your modeling and scoring, ZoomInfo and Google Cloud provide the robust data, platform and white-glove support you need to get there.

Nearly every business leader recognizes the value of data to drive critical decisions across the organization. Yet in 2022, NewVantage Partners found that barely over a quarter (26.5%) of business leaders consider their companies to be data-driven and just 19% feel they’ve established a data-oriented culture. Most organizations face three core challenges:

Sourcing high-quality business dataUnifying and enriching data sets to create a single source of truthLeveraging data for optimal go-to-market (GTM) results

This is why ZoomInfo and Google Cloud recently expanded our partnership to further streamline delivery of ZoomInfo’s Data Cubes into customers’ Google Cloud environments, via Google Analytics Hub. Analytics Hub makes the administration of sharing assets across any boundary easier and more scalable, while retaining access to key capabilities of BigQuery like its built-in ML, real-time updates, and geospatial analytics.

Analytics Hub allows users to efficiently and securely exchange data assets across organizations without replicating the data. This reduces the time and resources necessary to access, ingest and action high-quality third-party data like ZoomInfo’s Data Cubes in BigQuery. With direct access to the data in the systems where they work, data and operations teams can leverage holistic account intelligence in their workflows quickly and easily, accelerating time to insight and saving their sales and marketing teams time, money, and resources.

ZoomInfo Data Cubes are massive, customizable datasets that provide seamless access to ZoomInfo’s industry-leading data at scale, for data science, modeling, and analytics purposes. Some of the most common use cases for Data Cubes in Google Cloud include:

Data enrichment at scale – ZoomInfo collects, verifies, publishes and updates millions of accurate contact and company profiles every day. By combining this third-party data with CRM, MAP, and other first-party data sources, customers can fill data gaps and create a single source of truth in BigQuery to fuel modeling and analytics.Total addressable market (TAM) and ideal customer profile (ICP) analysis – Businesses want to identify their best-fit customers, assign quantifiable scores for account prioritization, and streamline campaigns and prospecting. Teams can leverage Data Cubes to pair nuanced company and contact attributes (such as decision-making authority, industry classification, and online behavior) with internal customer data (like time-to-close, deal size, and app download history) to uncover strong candidates in new and existing industry segments for their solution.Propensity-to-buy modeling – Leverage ZoomInfo’s rich company and contact information, job role insights, technographic data, historical data, and intent data to identify accounts that are most likely to buy and prioritize related sales and marketing resources to win faster.

Customers can now easily find and subscribe to ZoomInfo Data Cubes within Analytics Hub alongside popular Google data sets including Google Trends and Google Analytics.

ZoomInfo Data Cubes can include:

Company data: Expand your TAM with access to 200+ ZoomInfo company data attributes at the HQ and location level, refreshed and delivered quarterly. Advanced attributes, technographics, global data access, and modeling services can also be added.Hierarchy data: Understand the relationship between accounts with unlimited access to ZoomInfo hierarchy data. Refreshed quarterly, this cube contains 55 fields including ultimate parent, domestic parent, immediate parent, subsidiary, headquarters, individual location, and franchisor/franchisee.IP data: Turn page views into pipeline with the most accurate IP-to-company pairings available. This cube provides unlimited access to Zoominfo’s entire database of over 200 million IP addresses. Combine these with company core firmographics to execute campaigns with confidenceIntent data: Prioritize prospects actively seeking a solution like yours with direct access to ZoomInfo Intent Data, refreshed weekly. Subscribe to your choice of 4,000+ standard intent topics, or customize your own.Contact data: Fill the gaps in your accounts and identify your ideal customer profile with criteria-based access to comprehensive contact data attributes, refreshed and delivered monthly. Mobile numbers and job history data can also be added.

Built with BigQuery

With Google Analytics Hub, data and analytics teams will have rich metadata to help find the data they’re looking for, and even leverage analytics assets associated with that data. Privacy-safe, secure data sharing means customers no longer have to share complex permissions. The refreshed UI simplifies visibility and management of ZoomInfo Data Cubes and other data sets within Analytics Hub to improve user experience. And granular roles and permissions makes sharing data at scale with exactly the right internal and external audiences easier than ever.

“We are thrilled to partner with Google Cloud to make it easier than ever before to unlock customer data assets’ performance and scalability,” said Sneh Kakileti, Vice President of Product Management at ZoomInfo. “Analytics Hub eliminates resource-intensive data ingestion and sharing processes, allowing data and operations teams to deliver fast, actionable insights to their sales and marketing counterparts. Ultimately, ZoomInfo and Google Cloud help customers win faster with centralized go-to-market insights at scale.”

To see ZoomInfo’s listing, visit Analytics Hub within BigQuery. Click here to learn more about ZoomInfo and Google Cloud’s partnership.

The Built with BigQuery advantage for ISVs and Data Providers

Built with BigQuery helps companies like ZoomInfo build innovative applications with Google Data and AI Cloud. Participating companies can:

Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practices.Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

BigQuery gives ISVs the advantage of a powerful, highly scalable unified AI lakehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. Click here to learn more about Built with BigQuery.

Source : Data Analytics Read More

Reshaping Flipkart’s technological landscape with a mammoth cloud migration

Reshaping Flipkart’s technological landscape with a mammoth cloud migration

More than a third of consumers in India turn to Flipkart for their shopping needs, giving the platform a user base of more than 450 million individuals. With 1.1 million sellers, the platform offers over 150 million different products across 80 different categories and millions of shipments daily.

Flipkart was looking to reshape their technological landscape and future-proof their operations, and did so on Google Cloud. The fruits of this relationship are evident, as they successfully launched two of their pivotal platforms, Flipkart Data Analytics Platform (FDP), and Content Catalog Object Store, on the Google Cloud infrastructure among other things that were migrated to Google Cloud.

The biggest day of the year for Flipkart is the Big Billion Day (BBD) sale, akin to the global shopping extravaganza Black Friday Cyber Monday. Big Billion Day spans September and October, coinciding with India’s vibrant festival season. Over the course of multiple days, Flipkart experiences a transaction volume surge of six to eight times the ordinary business-as-usual (BAU) volume.

Migrating large-scale, complex data clusters within a short timeframe

Flipkart’s existing data platform was built on top of open-source big data technologies, with extensive customizations tailored to cater to Flipkart’s unique business requirements. This intricate environment was spread across self-managed data centers located in Chennai and Hyderabad. Facing tight schedules for migration within approximately eight months, Flipkart was faced with a range of challenges:

Migrating large-scale Hadoop clusters: Flipkart needed to move substantial Hadoop clusters with significant computational power and memory resources to Google Cloud.Complex data migration: The migration spanned beyond infrastructure, encompassing the transfer of over 10k facts, journals, and snapshots, along with 15,000 ETL (Extract, Transform, Load) jobs. This intricate operation involved processing a colossal 10PB of data in batch and 2PB of data in near-real-time, every dayData ingestion layers: To facilitate data ingestion at scale, the team needed to transition from on-premises Kafka to Google Cloud’s Pub/Sub. This intricate operation involved ingesting 130+ billion messages per day, 60 TB of compressed data on a daily basis during regular operations, and 500 TB per day during peak event periods. Furthermore, the process encompassed 3,000 topics, spanning both general data and sensitive Personally Identifiable Information (PII).Data processing and analysis: Flipkart needed to create a robust processing platform capable of handling a staggering 1.25 million messages per second for real-time analysis. This was done using Dataproc. Additionally, the platform also needed to efficiently process around two petabytes of messages in real-time and ten petabytes of messages daily, in batch mode.Migrating self-managed Hive: The migration extended to critical user-facing components, including the migration of the existing self-managed Hive to Hive on Dataproc. This transition aimed to service the requirements of approximately 20 teams and a user base of nearly 350 technical users. Remarkably, the team only had four months with which to accomplish this.Security assurance: Ensuring a robust security posture for both GoogleCloud- and self-managed services was of the utmost importance, and demanded meticulous planning and rigorous implementation. Google security experts collaborated closely with the Flipkart team, conducting a thorough evaluation of all migrated components to verify their security status and evaluate potential risks and solutions. This assessment process utilized frameworks such as security posture reviews.

Navigating these challenges was not undertaken in isolation, as Flipkart collaborated with the Google Cloud team, which shared its expertise throughout the migration. What was initially planned as a pre-BBD lift-and-shift migration, but a subsequent modernization phase post-BBD quickly evolved into a much more intricate and nuanced process. Recognizing the complexity of Flipkart’s use cases, a multidisciplinary team comprising more than 85 members from Google Cloud and partner organizations was convened to steer this ambitious migration journey.

Exceeding SLAs with a solid cloud infrastructure

With Google Cloud, the team built 15 tools, each designed to cater to unique requirements and challenges presented by Flipkart’s intricate ecosystem. As the migration encompassed various elements, including data ingestion, batch processing, messaging, and more, these utilities served as vital cogs in the migration machinery.

Fine-tuning and performance optimization efforts were relentless, ensuring that the new infrastructure met and exceeded service level agreements (SLAs). Rigorous testing, including several rounds of scale testing, reaching up to five times the BAU volume, solidified the foundation for a smooth execution of the BBD sale event on the Google Cloud infrastructure.

The impact of this migration was transformative. Flipkart completed their data platform migration from on-premises to Google Cloud within just eight months. The migration included the smooth transfer of substantial components, such as Hive clusters with a significant number of vCPU cores and a colossal volume of Google Cloud Storage, processing a massive amount of messages in batch and real time. Not to be overlooked, the migration of the content management store, encompassing 5.4 billion objects, stands as a testament to the scale and complexity of this endeavor.

Future-proofing the company through a journey of transformation

The real-world impact of the Google Cloud infrastructure coming to life became apparent very

quickly. Not only did Flipkart successfully execute the BBD sale event with a 7x increase in transactional data over BAU, but the event’s smooth execution fostered a sense of trust and confidence among internal and external stakeholders alike. This migration journey was not only a technological feat but also a testament to collaboration, innovation, and the potential for transformational change within the realm of ecommerce.

Looking back, both Flipkart and Google Cloud learned invaluable lessons. The migration served as a real-world stress test, pushing the boundaries of large-scale infrastructure within the JAPAC region. And as industries continue to evolve, Flipkart’s migration to Google Cloud stands as a model of successful partnership and technological advancement. It not only highlights the power of collaboration, but also the boundless potential of combining innovative technology and human ingenuity.

If you’re ready to migrate, read more about the Rapid Migration Program (RaMP) by Google Cloud, or sign up for a free IT discovery and assessment, to figure out what your IT landscape looks like and ensure a successful migration.

Source : Data Analytics Read More

Reduce costs and improve job run times with Dataproc autoscaling enhancements

Reduce costs and improve job run times with Dataproc autoscaling enhancements

Dataproc is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, and 30+ open source tools and frameworks. Google Cloud customers use Dataproc autoscaling to dynamically scale clusters to meet workload needs and reduce costs. In July 2023, Dataproc released several autoscaling improvements that enhance:

Responsiveness – improved autoscaling operation speedPerformance – reduced autoscaling operation durationReliability – fewer autoscaling cluster error occurrencesObservability – logged descriptive autoscaler recommendations

To demonstrate the potential impact of these enhancements, we ran a test that executed the same set of Spark jobs on two clusters — one cluster with all the autoscaling enhancements, and another without any of the enhancements. The new Dataproc autoscaling enhancements, in our tests, reduced cluster VM costs by up to 40%* and reduced cumulative job runtime by 10%.

Most of the enhancements are available in image versions later than 2.0.66 and 2.1.18.

In the following sections, we will highlight the impact on cost or runtime for each of the enhancement categories.

Improving job performance with responsive upscaling

The Dataproc autoscaler continuously monitors YARN memory and CPU metrics to make scale-up and scale-down decisions. During the graceful decommissioning scale-down phase, autoscaler continues to monitor cluster metrics, and evaluates the autoscaling policy to decide if a scale-up is needed to meet job demands. For example, job completions can trigger a cluster scale-down. If there is then a surge in new job submissions, the autoscaler cancels the scale-down and triggers a scale-up operation. This improved upscaling prevents graceful decommissioning from blocking scale-up operations, making the cluster more responsive to workload needs.

The following Cloud Monitoring charts for the test clusters illustrate the effects of responsive upscaling. For the cluster without enhancements, the autoscaler does not respond to changes in YARN-pending memory. By contrast, for the cluster with enhancements, the autoscaler cancels the scale-down and scales up the cluster to meet demand as shown by the YARN-pending memory line. The vertical dotted line indicates when the scale-up operation is triggered.

See When does autoscaling cancel a scale-down operation? for more information.

Increasing visibility into autoscaler operations

Enhancements to the autoscaler logs also indicate the evaluations that highlight the decision to cancel the scale-down operation in favor of a scale-up.

Reducing Dataproc cluster costs and scale-down times

Intelligent worker selection for faster scale-down

Dataproc now monitors multiple metrics to determine which workers to scale down. For each worker in the cluster, the selection criteria considers:

Number of running YARN containersNumber of running YARN application mastersTotal Spark shuffle data

Selecting idle workers based on the above metrics reduces the scale-down times.

Faster and more reliable scale-down for Spark with shuffle data migration and shuffle-aware decommissioning

Running executors and the presence of shuffle data on a worker slated to be decommissioned slows the scale-down operation. To speed up decommissioning, we introduced the following enhancements:

Prevent active executors on decommissioning workers from accepting new tasks. This makes the executors complete sooner, allowing YARN to decommission the worker sooner.Immediately decommission workers that are not running any executors or hosting any shuffle dataMigrate shuffle data off decommissioning workers to immediately decommission the worker

Going back to our two clusters, we see that in the ‘before’ scenario, the graceful decommissioning operation triggered lasts for an entire hour, the gracefulDecommissionTimeout in the autoscaling policy. In the ‘after’ scenario, it completes earlier even though jobs are still running on the cluster.

Before, the larger scale-down takes 61 minutes to complete :

code_block<ListValue: [StructValue([(‘code’, “clusterUuid: BEFORErn description: Remove 81 secondary workers.rn operationType: UPDATErn status:rn innerState: DONErn state: DONErn stateStartTime: ‘2023-07-31T09:25:10.379702Z’rn statusHistory:rn – state: PENDINGrn stateStartTime: ‘2023-07-31T08:24:05.455855Z’rn – state: RUNNINGrn stateStartTime: ‘2023-07-31T08:24:05.963905Z'”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3edda0a47ee0>)])]>

After, the larger cluster scale-down is canceled in favor or scale up but a subsequent scale-down takes only 34 minutes to complete :

code_block<ListValue: [StructValue([(‘code’, “clusterUuid: AFTERrn description: Remove 67 secondary workers.rn operationType: UPDATErn status:rn innerState: DONErn state: DONErn stateStartTime: ‘2023-07-31T09:25:46.238910Z’rn statusHistory:rn – state: PENDINGrn stateStartTime: ‘2023-07-31T08:51:31.676781Z’rn – state: RUNNINGrn stateStartTime: ‘2023-07-31T08:51:32.016452Z'”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3edda0a47820>)])]>

The YARN resource manager logs moving workers to DECOMMISSIONED state as soon as shuffle data migration completes:

code_block<ListValue: [StructValue([(‘code’, ‘DEBUG org.apache.hadoop.yarn.server.resourcemanager.DecommissioningNodesWatcher: Total Spark shuffle data on node in bytes 0rnINFO org.apache.hadoop.yarn.server.resourcemanager.DecommissioningNodesWatcher: Decommissioning node READY because it had no shuffle data’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3edda0a47220>)])]>

This particular enhancement will be supported only in dataproc image versions 2.1.18+.

Prompt deletion of decommissioned workers

During scale-downs, Dataproc now monitors the decommissioning status of each worker and deletes a worker when it is decommissioned. Previously, Dataproc waited for all workers to be decommissioned before deleting the VMs. This enhancement results in cost savings when workers in a cluster with a long graceful decommissioning period are taking significantly longer than other workers to decommission.

Test setup

To demonstrate the benefits of the new autoscaling enhancements, we created two clusters with the same configuration:

n2d-standard-16 master and worker machine types with 2 local SSDs attached, 5 primary workers, and between 0 and 120 autoscaling secondary workers.Indentical default cluster configurationThe same autoscaling policy:

code_block<ListValue: [StructValue([(‘code’, ‘scale_up_factor: 1.0, # aggressive scale ups without dampeningrnscale_up_min_worker_fraction: 0.0, # allow all scale ups with no minimum thresholdrnscale_down_factor: 1.0, # aggressive scale downs without dampeningrnscale_down_min_worker_fraction: 0.0, # allow all scale downs with no minimum thresholdrngraceful_decommission_timeout: “1h”‘), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3edda2bd6e50>)])]>

Both clusters ran the same jobs:

a long-running custom Spark job that consumes a fixed set of resources, in parallel withmany short Spark jobs that simultaneously execute a few modified TPC-DS benchmark queries on a 1TB dataset with an idle time between runs to simulate bursts and idle behavior.

We created one cluster with a Dataproc image version of 2.1.3-debian11, without any of the autoscaling enhancements. The other cluster was created with a newer image version of 2.1.19-debian11 with all the enhancements.

The autoscaling enhancements are available by default on 2.0.66+ and 2.1.18+ image versions. You do not need to change cluster configurations or autoscaling policies to obtain the benefits of these enhancements. Spark shuffle data migration is only available in 2.1 image versions.


In this blog post, we highlighted the cost and job performance improvements resulting from Dataproc autoscaling enhancements, without the administrator needing to change the cluster configuration or autoscaling policy. To get started with Dataproc check out the Dataproc Quickstart and learn about Dataproc autoscaling.

* As of August 2023, at list prices, with the mentioned test setup

Source : Data Analytics Read More

Startups Use Data and Agile for Portfolio Management

Startups Use Data and Agile for Portfolio Management

“Everybody needs data literacy, because data is everywhere. It’s the new currency, it’s the language of the business. We need to be able to speak that.” – Piyanka Jain. Data-driven business management has emerged as an invaluable tool for businesses of all sizes, from startups to large corporations.

With a powerful suite of analytics tools available today – such as predictive analytics, prescriptive analysis, customer segmentation and lead scoring – organizations now have access to critical information that can equip them with the power to make data-driven decisions quickly and accurately. 

How startups leverage data for agility and competition

Each year, companies that use data grow by more than 30%. That’s hard proof that using data is a winner’s move in business. It gives the power to make decisions fast and right. Think of wanting to find the best tool for your team to work together online – a common challenge today. One can simply look at what other users say about different tools or how often people use each one. This takes us straight towards the right choice without second guessing. A person might favor one choice over another because they feel like it, not because facts support it. But when there is clear information in sight, making fair choices becomes easy. Startups use data to move fast, stay agile, and keep ahead of the competition. 

How do they do this? Simply by turning vast amounts of data into smart business decisions. Consider drug manufacturers for example, where big data acts as their hidden ally. They harness its power to simulate clinical trials which leads to significant cuts in cost and time – from five years down to two. What’s more is that it alleviates patient suffering during these trials. Both patients and manufacturers reap the benefits here. Then there are giants like Netflix, Amazon, Facebook and LinkedIn who all owe part of their success story to big data’s helping hand. Each one extracts relevant information from past consumer behavior allowing them to deliver personalized services or recommendations. Even Google feeds off keystrokes you punch in to predict your next search query- yes, every single one of those amazing features you love so much is enabled by Big Data

Designing Strategic Portfolio Management that Drives Success

SPM begins by choosing clear-cut objectives for your business. These are the goals that guide the projects you take up. The more value a project adds, the higher its priority. Next comes connecting these objectives with your chosen projects and their outcomes—sharing this strategy company-wide fosters teamwork toward achieving common targets. Using tools like OKR helps provide real-time updates on what works and what doesn’t, thus guiding adjustments where needed to maximize results. The next step is to set up robust governance — think of it as drawing clear lines on a playground where everyone knows the rules and their roles. It’s not scary; it just ensures all team players know what they should do when handling work and goals.

Then comes value creation: this phase prioritizes projects that yield high returns. Imagine betting on horses — you’d want to invest in the swift one rather than one with minimal chance of winning. Finally, a key aspect affecting successful SPM strategy execution is continuous evaluation and improvement. Think of it like cleaning out an old closet – Only those items adding significant value remain while everything else should be discarded or improved upon. By embracing Strategic Portfolio Management, companies can assess their performance against the set targets more systematically. They spot profitable opportunities precisely using quality data insights, propelling them closer to exceptional growth and achievements.

As technology becomes further integrated into our lives, enabling businesses to operate more efficiently is increasingly important. Dаtа-ԁriven business mаnаgement is the key for making strategic portfolio choices that will drive success in any industry or sector. By empowering companies with analytic insights thаt leverаge big ԁаtа across all teams—from marketing operations down through product engineering—organizations can gain a competitive edge by understanding their audiences’ buying behаviors better thаn ever before. 

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Jira & Zendesk Aid with Analytics-Driven Management

Jira & Zendesk Aid with Analytics-Driven Management

Description: Looking for a comparison between Jira Service Management and Zendesk? Discover the key differences between these two popular platforms in our comprehensive guide.

Big data and technical support services are twin pillars of successful organizations.

There are a growing number of platforms that help companies use analytics to offer better technical support. This is one of the reasons that companies are projected to spend over $680 billion on analytics by 2030. Zendesk and Jira Service Management are two of the most popular.

Using the Right Analytics Platforms Can Help Provide Better Customer Support

In the world of ticket management solutions, two prominent names consistently stand out: Jira Service Management vs Zendesk. These platforms offer robust capabilities for managing tickets and customer requests, making them indispensable tools for various businesses and organizations.

Both of these platforms have complex analytics algorithms that help technical support professionals offer higher quality service. While both Jira Service Management and Zendesk offer robust ticket management solutions, a direct comparison isn’t entirely straightforward. Here’s why:

Zendesk for Service: This platform excels in providing a user-friendly ticketing system, making it an ideal choice for customer support teams.

Jira Service Management: Tailored with advanced IT service management (ITSM) features, it’s particularly well-suited for IT support teams.

Whether you’re steering a customer support team or an IT support unit, this guide will shed light on the distinct advantages and functionalities of these two leading platforms. You need to pay attention to the signs that your business needs an IT support service.

Jira Service Management vs Zendesk: Choosing the Right Ticket Management Solution

Let’s provide more detailed information for each feature of Jira Service Management and Zendesk:


Jira Service Management provides extensive ticketing features, but its user interface might seem complex, particularly to newcomers. Getting the hang of it demands more effort and training for agents.

Zendesk’s ticketing system stands out for its user-friendliness. Its intuitive interface simplifies issue handling and resolution, making it easier for agents to manage customer inquiries. This streamlined approach fosters a smoother support experience, and Zendesk’s ticketing system is known for its simplicity and efficiency.


Jira Service Management distinguishes itself through its readily available automation templates and triggers designed for customer service and ITSM requirements. These pre-made templates streamline the automation of routine tasks, making it easier for teams to initiate automation processes.

Zendesk offers customizable automation and triggers, allowing you to create workflows that fit your specific needs. While this flexibility is an advantage, it’s important to note that the platform lacks pre-built automation templates. Users will need to configure automation rules from scratch, which can be a more time-consuming process.

Knowledge Base

Navigating the backend of Jira Service Management‘s knowledge base can be quite a challenge. It’s missing a preview option, so you can’t see how changes will look to end-users before they’re published. The interface can be complex, making content management less straightforward. Additionally, it lacks features like comment moderation, which can be essential for controlling user-generated content.

Zendesk stands out in managing knowledge bases. It offers a straightforward interface for adding sections, moderating discussions, and previewing changes. This user-friendliness extends to content creation and maintenance, making it a valuable tool for organizations aiming to provide comprehensive self-service resources.


While Jira Service Management supports reporting, it offers fewer customization options compared to Zendesk. It lacks built-in formulas, which can limit the depth of data analysis. Retrieving data from specific channels may not be as intuitive, and the reporting process might require more technical knowledge.

Zendesk offers robust reporting capabilities. Users can generate multi-channel reports, with numerous customization options. The platform also includes built-in formulas, allowing for more advanced data analysis and insights into customer interactions.


Jira Service Management boasts an impressive selection of over 3,000 integrations, providing extensive compatibility with various tools and services. This wide range of integrations can enhance the platform’s functionality and connectivity with other business systems.

Zendesk provides extensive integration options. This makes it easier for businesses to connect Zendesk with their other tools and systems, enhancing overall efficiency.


JSM offers guided pop-up tutorials and external video guides to help users get started. However, some interfaces within Jira Service Management can be complex and technical, particularly for those who are new to the platform. This may require additional training and onboarding efforts.

Zendesk prioritizes user-friendliness with features such as pop-up tutorials, embedded video tutorials, and an AI chatbot. These resources aid in onboarding, helping new users become familiar with the platform’s features and functions.


Zendesk offers a range of pricing plans to cater to different customer support needs. The Team plan starts at $19 per user per month, the Professional plan is priced at $49 per user per month, and the Enterprise plan costs $99 per user per month. This pricing structure provides scalability and flexibility based on your organization’s requirements.

Jira Service Management’s pricing structure offers different plans, including a free plan for a limited number of users. The Standard plan starts at $21 per user per month, the Premium plan is priced at $47 per user per month, and the Enterprise plan is available for $134,500 per year for 201-300 users. The Enterprise plan, while more expensive, comes with additional features compared to Zendesk’s Enterprise plan.

Zendesk is designed to handle immediate, everyday customer and employee support issues efficiently. It’s your go-to for quick resolutions. On the other hand, Jira Service Management is more geared toward analyzing issues in-depth and finding long-lasting solutions. If your focus is primarily on straightforward, day-to-day problems, Jira may offer more features and complexity than you actually need. Your choice should align with your support operations’ specific demands and scale.

Zendesk and Jira Service Management Are Great Analytics-Driven Technical Support Platforms

All companies should be aware of the different technical support options available to them. Some of them use analytics technology to offer higher quality service to their valued customers. Jira Service Management and Zendesk are two of the most popular, so you should be aware of their features an costs.

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5 AI Tools for Rocking Business Presentations

5 AI Tools for Rocking Business Presentations

There are a lot of articles on making presentations about AI technology, such as this article from Medium. However, AI can also be used to create powerful presentations on just about any topic.

AI technology has turned the process for coordinating conventional business meetings on its head. More organizations are using sophisticated AI tools to improve engagement and communicate more effectively. This is one of the reasons that the market for AI is expected to grow 2,000% by 2030.

If you are planning on preparing a business presentation in the near future, you should be aware of the best AI-driven tools that can help you make the most of your message. Keep reading to learn more.

What Are the Best AI Tools for Making Quality Business Presentations?

In our AI-driven era, the traditional methods of presenting are being swiftly overshadowed by a myriad of innovative tools that rely heavily on machine learning. From boardrooms to virtual conferences, the way we share information, tell stories, and engage audiences is undergoing a radical transformation.

To truly captivate your audience and deliver a memorable presentation, it’s crucial to stay abreast of these emerging artificial intelligence tools and understand how they can be leveraged effectively. With the right AI tools at your disposal, you can transform any presentation from mundane to magical.

Interactive Mapping Tools

In an increasingly globalized world, geographical context is paramount. Visualizing geographical data with AI not only adds a layer of depth to your presentation but can also significantly enhance audience engagement. This is where an interactive mapping tool comes into play.

Imagine giving your audience the ability to zoom in and out of a detailed map, pinpointing specific regions of interest. Custom markers and annotations can help emphasize specific points or locations. Integrations with various data sources ensure that your maps are always up-to-date, presenting real-time insights. What’s more, with layers and overlays, you can seamlessly showcase different data points on a single map, allowing for a comprehensive view of the situation.

The benefits? An interactive map can transform passive viewers into active participants. The spatial context provided by these maps makes complex geographical data more palatable. And let’s not forget, a visually appealing map provides a delightful break from the monotony of text-heavy slides.

Dynamic Infographics Creators

Data drives decisions. However, raw data, no matter how valuable, can seem bland and overwhelming. Enter dynamic infographics creators. These tools empower presenters to transform complex data into visually stunning graphics that tell a story. AI can help you bring your presentation to life.

With pre-designed templates available, creating an infographic becomes as easy as selecting a style that resonates with your theme. The customization options are vast, from colors to fonts, ensuring your infographic seamlessly fits into your presentation’s aesthetic. The drag-and-drop interfaces on most platforms are a blessing, making design accessible even to non-designers.

The benefits are manifold. Infographics, by design, make data visually appealing. They break down dense information into digestible chunks, making it easier for your audience to grasp and remember. Plus, the flexibility to edit on the fly ensures you’re always ready for those inevitable last-minute changes.

Animated Video Software

If a picture is worth a thousand words, imagine the value of a well-crafted animation. Animated video software allows presenters to bring abstract concepts to life, weaving narratives that captivate and educate.

Modern platforms offer a plethora of animation styles, from whiteboard sketches to intricate 3D renderings. Integration options for voice-overs mean you can narrate your story as it unfolds on screen. With expansive libraries of characters, backgrounds, and props, the creative possibilities are virtually endless.

Why should you consider this? Animated videos are attention magnets. They demystify abstract concepts, turning them into relatable stories. They enrich the presentation narrative, making it both informative and entertaining. Whether your audience is viewing your presentation online or in person, a well-executed animation can leave a lasting impression.

Interactive Polling and Q&A Platforms

Presentations are not just about speaking; they’re about listening too. Interactive polling and Q&A platforms are redefining the presenter-audience dynamic, fostering real-time engagement. In fact, 70% of marketers believe that interactive content has a higher efficacy in engaging audiences.

Live polling can electrify your presentation. Pose a question, and watch as responses flood in, offering instant insights. Q&A sessions, facilitated through these platforms, allow audiences to voice their queries, making the experience more collaborative. And for those who prefer discretion, anonymous feedback collection ensures everyone’s voice is heard.

The outcome? A presentation that feels like a conversation. It encourages audience participation, ensuring the message resonates. By gauging audience reactions in real time, presenters can adjust their delivery, ensuring key points hit home. It’s a win-win.

Virtual Reality (VR) and Augmented Reality (AR) Integration Tools

The frontier of immersive presentations is here, with VR and AR tools taking center stage. These technologies allow audiences to step into the presentation, experiencing content like never before.

Embedding VR or AR experiences within slides can be a game-changer. Whether it’s exploring a 3D model of a product or walking through a simulated environment, these tools bring depth and dimension to your content. And with compatibility options for popular headsets, the experience is smoother than ever.

The advantage? A presentation that’s not just seen, but felt. These tools offer unparalleled immersion, turning passive viewers into active explorers. They offer a unique way to convey complex scenarios, ensuring your presentation stands out in a sea of sameness.

AI Technology Can Help You Make the Most of Your Business Presentation

Each presentation has its unique needs, and the tools should serve to amplify the message, not overshadow it. So, the next time you’re gearing up for a presentation, consider weaving in one or more of these AI tools. The results might just surprise you.

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AI Technology is Invaluable for Cybersecurity

AI Technology is Invaluable for Cybersecurity

AI poses a number of benefits and risks for modern businesses. One of the most striking examples is in the field of cybersecurity. One poll found that 56% of companies use AI to enhance their cybersecurity strategies.

A number of hackers are using AI to exploit their targets more easily. On the other hand, many cybersecurity professionals are also using AI to safeguard their digital assets.

AI technology is helping with cybersecurity in a myriad of ways.

Cybersecurity, often known as information security or IT security, keeps information on the internet and within computer systems and networks secure against unauthorized users. Cybersecurity is the practice of taking precautions to protect data privacy, security, and reliability from being compromised online.

The importance of AI-driven cybersecurity

Artificial Intelligence (AI) has revolutionized the field of cybersecurity, providing advanced tools and techniques to protect digital assets from an ever-evolving landscape of threats. The significance of AI in cybersecurity in today’s digital landscape, where information is increasingly being stored, transferred, and accessed digitally, cannot be understated. The threat of cyber-attacks is expanding across all industries, affecting government agencies, banks, hospitals, and enterprises. A successful breach can result in loss of money, a tarnished brand, risk of legal action, and exposure to private information. Cybersecurity aims to stop malicious activities from happening by preventing unauthorized access and reducing risks. In addition, cybersecurity protects companies’ intellectual property, trade secrets, and other private information, helping them to sustain a competitive edge and encourage creative problem-solving.

With persistent threats from state-sponsored espionage, hacktivist organizations, and cyber warfare, having an effective cybersecurity strategy has become increasingly important for the protection of national security. Protecting sensitive information, vital infrastructure, and the dependability of military systems requires advanced cybersecurity solutions and the expertise of trained professionals. Specialists in cybersecurity help in taking appropriate precautions to secure sensitive data and individual privacy in the modern digital environment.

AI-driven systems can analyze massive datasets in real-time, identifying anomalies and potential breaches more effectively than traditional methods. Machine learning algorithms can adapt and improve over time, enabling them to recognize new, previously unseen attack patterns. Additionally, AI enhances threat detection and response, automating the identification of vulnerabilities and assisting security professionals in making faster, more informed decisions. The integration of AI in cybersecurity has become an indispensable component in safeguarding sensitive information and critical infrastructure from an increasingly sophisticated and pervasive array of cyber threats.

Additionally, with the rise of e-commerce, social networking, and other web-based businesses, people’s private information is increasingly stored on various sites online. Criminals can utilize these vulnerabilities to steal money and identities or launch targeted attacks, endangering their victims’ privacy and personal information in the process.

The demand for cybersecurity specialists?

The proliferation of cybersecurity firms reflects the increasing sophistication of cyber threats in today’s technology-driven society. As businesses increasingly rely on digital systems to store confidential data, protecting such data from hackers has become a top priority. Therefore, cybersecurity experts with the knowledge and experience to safeguard critical infrastructure from cyber threats are in high demand.

The rising frequency of cyber-attacks, such as high-profile data breaches and ransomware attacks, has brought attention to the peril of cyber vulnerabilities. As a result, businesses across many industries have been spending increasingly large sums on security technology and services, driving demand for trained specialists fluent in the latest preventative measures.

The expansion of the digital economy has spawned a new set of cyber-security concerns. Rapid growth in the use of recently developed technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing has introduced new security threats and vulnerabilities. These bolstered entry points provide even more potential for data breaches and disruption.

What do cybersecurity specialists do?

Cybersecurity professionals protect computer systems, networks, and data from harmful intrusions and attacks. They are primarily responsible for identifying and managing security risks by locating system vulnerabilities and potential threats. They do in-depth risk assessments, evaluate existing security measures, and propose changes to bolster an organization’s security. After evaluating potential risks, cybersecurity professionals implement various preventative actions. This process requires enterprise-specific security procedures, policies, and techniques to be developed and implemented. They set up and use security measures such as firewalls, intrusion detection systems (IDS), and antivirus software to prevent threats, including hacking, malware infection, and other malicious activity.

Cybersecurity professionals often perform penetration testing and vulnerability assessments to identify security flaws in systems and networks. They try to reduce the likelihood of exploitation by applying security updates and upgrades to systems. In addition to their technical duties, cybersecurity professionals also focus on spreading security awareness and training. Employees are taught recommended practices such as recognizing phishing attacks and social engineering techniques.

Specialists foster a culture of security awareness within the company by hosting training sessions and making educational resources available. This empowers employees to adequately support the firm’s security goals.

They also uphold relevant regulations and protect systems, data, and communications. They conduct audits to assess security measures and identify potential vulnerabilities. Cybersecurity experts also investigate data breaches and provide further context for their effects. Together with law enforcement, they can look into cyberattacks and find entry points. The involvement of cybersecurity professionals in such tasks helps maintain a compliant and secure environment, protecting sensitive data and lessening the likelihood of being attacked. Cybersecurity specialists also log comprehensive security incidents, inquiries, and resolutions. They also deliver reports outlining the organization’s security posture and proposed solutions to management and other invested parties.

Cybersecurity professionals also contribute to the design and implementation of secure infrastructure. They work with software developers and system administrators to ensure that security is prioritized from the start of development. Cybersecurity professionals must always be on the lookout for emerging cyber threats and attack methods. They use security technology and threat intelligence sources to proactively monitor systems, identify potential attacks, and take preventative action.

The essential skills and qualities to be a cybersecurity specialist

To become a cyber expert, you must develop essential skills and personal qualities. You must have a solid foundational understanding of computer architecture, operating systems, networking, and protocols. This includes the Transmission Control Protocol/Internet Protocol, network architecture/routing, and popular OSes, including Microsoft Windows, Linux, and Apple macOS. It is also essential to have a thorough comprehension of information security theories, principles, and methods. Subjects such as incident response, risk management, access control, and cryptography fall under this category.

Learning and using scripting languages like PowerShell and Bash and programming languages like Python, Java, and C++ are also helpful. Knowing how to construct scripts and automate procedures can save time and effort when doing system analysis and security measures.

Understanding penetration testing and vulnerability assessment techniques can boost one’s security knowledge. Exploiting system and network weaknesses can allow for a greater understanding of the security of a network or system. It is important to stay updated on industry-standard security practices. Security software, like firewalls, IDS/IPS, antivirus, and vulnerability log analysis tools, are all crucial for staying secure. As the IoT and cloud security continues to grow, it is essential to stay educated on the latest technology developments.

One must be well-versed in both incident response and digital forensics. It is critical to have skills in evidence collection, preservation, and analysis, as well as detection, response, and recovery from security breaches. Successful risk management requires analyzing potential threats, pinpointing areas of weakness, and putting forth concrete plans to address those areas.

Strong social and communication skills are essential for a career in cybersecurity. You’ll be expected to produce reports on a regular basis, work with a large number of teams, and communicate complex security concepts to stakeholders. You must acquire the skills necessary to share technical information properly.

The field of cybersecurity is dynamic and continually changing. As a result, it’s vital to have an attitude of constant learning and flexibility. You should keep up with the latest business trends, security issues, and technological advancements. Continuous learning can be pursued through certifications, training courses, and involvement in cybersecurity communities.

Finally, as a cybersecurity specialist, you should uphold ethics and professionalism. This entails upholding moral standards and business norms and safeguarding data integrity, confidentiality, and privacy. To fully understand cybersecurity, you must also know the ethical and legal considerations.

How to become a cybersecurity specialist?

There are a few things you should know before pursuing a career in cybersecurity, such as the level of schooling needed, the certifications available, and the amount of experience you’ll need.

When considering how to become a cyber-security specialist, it is vital to integrate the necessary education and skills related to the field. Pursuing a degree in a discipline connected to the profession, such as St. Bonaventure University’s Cybersecurity master’s course, is one way to become a cybersecurity specialist and qualify for a variety of IT careers. With this degree, you will have a solid grounding in computing, networking, programming, and security fundamentals. In addition, you may gain practical experience with cutting-edge technology and software in many of these programs. Upon completing a reputable course such as this, you will be equipped for a wide range of positions in the information technology sector.

Degree courses at undergraduate and graduate levels will teach you all you need to know about cybersecurity, including the theory and practice behind it. Earning a certificate recognized in your field is a great way to establish your credibility and demonstrate your expertise in a specific area of cybersecurity. Certifications are highly valued in the cybersecurity field.

Qualifications can increase your career prospects and income. Certifications showcase cybersecurity knowledge to prospective employers and show a commitment to the industry. Typically, passing an exam is required to obtain certification. Remember that most certification tests necessitate thorough preparation, and others demand prior experience.

Practical experience

Gaining work experience is crucial for success in the cybersecurity industry. It would help if you explored options for using your expertise in the real world. This can be accomplished through internships, co-op programs, entry-level cybersecurity jobs, competitions, and Capture the Flag (CTF) exercises. Gaining work experience is the best way to acquire valuable abilities, learn about business procedures, and expand your professional network.

Career trajectory as a cybersecurity specialist

Experience and skill are prerequisites for promotion to supervisory roles in the cybersecurity industry. Positions such as chief information security officer (CISO), security manager, and security team lead entail managing teams, overseeing cybersecurity initiatives, and making choices with authority to safeguard an organization’s digital assets.

Experienced cybersecurity specialists can transition into advisory positions by working as independent consultants or as employees of cybersecurity consulting firms. They offer knowledge and guidance to companies so they may strengthen their security posture, reduce risks, and ensure compliance operations.

Cybersecurity specialists may also opt to specialize in research and development. These experts can share their knowledge with academic institutions, business research labs, or cybersecurity firms to help identify new methods, tools, and tactics for fending off recently emerging dangers and developing cutting-edge security solutions.

Those with a passion for cybersecurity and an entrepreneurial spirit can launch their own consulting organizations, start-ups, or technological corporations. This is a fantastic chance for them to put their expertise in the field of cybersecurity to use by creating innovative products and services for clients all around the world. They get to create something new and see their concepts come to fruition as profitable firms that safeguard people’s, businesses, and governments’ online lives.

AI is the Key to Improving Cybersecurity

There are a few important things to consider before beginning a cybersecurity specialist career. You must be aware of the dynamic nature of cyber threats, the need to maintain the most recent information and abilities, and the necessity of continual learning. Additionally, you must be ready for the difficulties and obligations in safeguarding sensitive data and important systems.

Success in this sector requires having excellent analytical and problem-solving skills and the capacity to think critically and make judgments under time constraints. Fortunately, AI technology can help with all of these challenges. It’s crucial to follow all pertinent laws and regulations and be mindful of cybersecurity work’s ethical and legal ramifications. You may create a strong foundation for a fruitful and satisfying career as a cybersecurity professional by carefully analyzing these aspects and taking the necessary actions to obtain the relevant education, certifications, and practical experience.

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Serving Customers with Virtual Reality in the Metaverse

Serving Customers with Virtual Reality in the Metaverse

Generative AI is having a huge impact on the customer service profession. One study estimates that the market for AI in this field will be worth nearly $2.9 billion by 2032.

There are a lot of ways that AI is changing the future of the customer service sector. One of the biggest is with virtual reality.

We talked about some of the many ways that virtual reality is changing our world. Customer service is among them.

In the metaverse, you’ll discover a new frontier for customer service. Virtual reality is revolutionizing the way businesses engage with their customers, offering enhanced experiences and immersive interactions.

Virtual assistants are at the forefront, transforming customer support in this virtual world. But there are challenges to overcome, like communication barriers and building trust and security.

As customer expectations evolve in the metaverse era, businesses must adapt to meet their needs. This is just one of the many examples of ways that AI is revolutionizing customer service.

Explore the possibilities and challenges of customer service in metaverse.

The Metaverse: A New Frontier for Customer Service

You can explore the metaverse as a new frontier for customer service. With the advancement of technology, the metaverse has become a virtual reality space where users can interact with each other and the digital world. This immersive environment opens up endless possibilities for businesses to revolutionize their customer service experience.

In the metaverse, you can create virtual representations of your brand and products, allowing customers to interact with them in a realistic and engaging way. Imagine being able to browse through a virtual store, try on clothes, or test out different products before making a purchase. This level of interactivity not only enhances the customer experience but also provides valuable insights into customer preferences and behavior.

Furthermore, the metaverse allows for real-time communication and support. Through virtual chatbots and avatars, customers can get instant assistance and answers to their queries. This eliminates the need for long wait times and frustrating phone calls, providing a seamless and efficient customer service experience.

However, venturing into the metaverse also comes with its challenges. Ensuring data security and privacy, as well as managing virtual identities, will be crucial. Additionally, creating a metaverse presence requires significant investment and expertise in virtual reality technology.

Leveraging Virtual Reality for Enhanced Customer Experiences

By using virtual reality, businesses can create immersive and engaging customer experiences that go beyond traditional methods. CUSTOMER Magazine actually says that VR is the future of customer service.

Virtual reality technology allows customers to interact with products and services in a whole new way, bringing them closer to the brand and creating memorable experiences.

Here are four ways businesses can leverage virtual reality to enhance customer experiences:

Virtual Showrooms: Imagine stepping into a virtual showroom where you can explore different products, examine them up close, and even interact with them. Virtual reality can transport customers to a virtual space where they can browse and experience products without leaving their homes.

Virtual Try-On: With virtual reality, customers can try on clothing, accessories, or even makeup virtually. They can see how the products look on them, experiment with different styles, and make informed purchasing decisions.

Virtual Tours: Virtual reality can take customers on virtual tours of hotels, resorts, or real estate properties. They can explore different rooms, amenities, and even visualize themselves in the space, helping them make more informed choices.

Virtual Events: Virtual reality can create immersive experiences for virtual events, conferences, or product launches. Attendees can interact with virtual booths, attend virtual presentations, and network with others, all from the comfort of their own homes.

Virtual Assistants: Revolutionizing Customer Support in the Metaverse

Virtual assistants have become an integral part of customer support in the metaverse, providing efficient and personalized assistance to users. These AI-powered helpers are revolutionizing the way businesses interact with their customers, offering real-time support and guidance within the virtual environment.

One of the key benefits of virtual assistants is their ability to handle multiple customer queries simultaneously, ensuring quick response times and reducing the need for customers to wait in long queues. They can also provide instant access to relevant information and resources, helping users navigate through the metaverse and resolve their issues more effectively.

Moreover, virtual assistants can offer personalized recommendations and suggestions based on user preferences and previous interactions. By analyzing user data and behavior patterns, they can anticipate customer needs and provide tailored solutions, enhancing the overall customer experience.

To illustrate the impact of virtual assistants, consider the following table:

Benefits of Virtual Assistants in the MetaverseChallenges of Virtual Assistants in the MetaverseImproved customer support and response timesEnsuring data privacy and securityPersonalized assistance and recommendationsMaintaining accuracy and reliabilityEnhanced customer experience and satisfactionTraining and updating AI modelsEfficient handling of multiple queriesIntegration with existing customer support systemsSeamless navigation and issue resolutionOvercoming language and cultural barriers

Overcoming Communication Barriers in the Virtual World

To effectively navigate the virtual world, businesses must address and overcome the various communication barriers that can hinder customer interactions. In the metaverse, where people from different backgrounds and cultures come together, it’s crucial to ensure effective communication for successful customer service.

Here are some key barriers that need to be overcome:

Language barriers: With customers from all around the globe, language differences can pose a challenge. Implementing real-time translation tools or employing multilingual support agents can help bridge this gap.

Technical difficulties: Virtual environments may encounter glitches or connectivity issues, disrupting communication between businesses and customers. Investing in robust technology infrastructure and providing troubleshooting resources can help overcome these technical hurdles.

Lack of physical cues: In the virtual world, non-verbal communication cues like facial expressions and body language are limited. To compensate for this, businesses can use emoticons, gestures, or even virtual avatars to enhance communication and convey emotions effectively.

Cultural differences: Cultural nuances and customs can vary widely in the metaverse. Businesses need to be mindful of these differences and provide training to their support agents to ensure respectful and inclusive interactions with customers from diverse backgrounds.

Overcoming these communication barriers in the virtual world won’t only enhance customer satisfaction but also foster stronger relationships between businesses and their virtual customers.

Building Trust and Security in the Metaverse Customer Journey

As a business operating in the metaverse, you must prioritize building trust and security throughout the customer journey. In this digital realm, where virtual experiences and interactions take place, customers need to feel confident that their personal information and transactions are safe and protected. One way to build trust is by implementing robust security measures, such as encryption and authentication protocols, to safeguard customer data from unauthorized access or cyber threats.

Transparency is also key in establishing trust in the metaverse. Clearly communicate your privacy policies and data handling practices to customers, ensuring they understand how their information will be used and protected. Implementing a secure and user-friendly authentication process can also enhance trust, as customers will feel reassured knowing that their identities are verified.

Furthermore, providing reliable customer support is crucial in the metaverse. Promptly address any concerns or issues raised by customers and offer assistance throughout their journey. Establishing a strong customer support system, whether through virtual assistants or live chat, can help build trust and provide a sense of security.

Lastly, fostering a sense of community can contribute to trust-building in the metaverse. Encourage customer feedback and engage in open dialogue to show customers that their opinions matter. Develop a strong online presence and actively participate in virtual events and forums to build relationships and establish credibility.

Adapting to Changing Customer Expectations in the Metaverse Era

You should regularly assess and adapt to the changing customer expectations in the metaverse era to stay competitive and meet their evolving needs. As technology advances and the metaverse becomes more integrated into our daily lives, customer expectations are shifting.

To effectively adapt to these changes, consider the following:

Seamless virtual experiences: Customers now expect a seamless transition between the physical and virtual worlds. This means providing a consistent and immersive experience across different platforms and devices.

Personalization: In the metaverse era, customers desire personalized interactions. Tailor your products and services to meet individual needs and preferences, providing a unique and memorable experience.

Real-time support: Customers expect immediate assistance, even in the virtual world. Implement real-time support channels, such as chatbots or virtual assistants, to address their queries and concerns promptly.

Data privacy and security: With increased virtual interactions, customers are concerned about their data privacy and security. Ensure robust security measures are in place to protect their personal information and provide transparent data handling practices.


In conclusion, the metaverse presents exciting possibilities for customer service, but also comes with its fair share of challenges.

With the use of virtual reality and virtual assistants, customer experiences can be enhanced and revolutionized in this new frontier.

However, overcoming communication barriers and building trust and security will be crucial for a successful metaverse customer journey.

Moreover, businesses must be prepared to adapt to changing customer expectations in this era.

Overall, embracing the metaverse can lead to innovative and personalized customer service experiences.

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Reckitt: Transforming consumer insights with Google Cloud

Reckitt: Transforming consumer insights with Google Cloud

At Reckitt, we exist to protect, heal and nurture in the relentless pursuit of a cleaner and healthier world. We work tirelessly to get our products into the hands of those who need them across the world because we believe that access to high-quality health and hygiene is a right and not a privilege. The markets in which we operate can be very distinct with the need to understand our consumers in respective regions, run adapted campaigns leading to a need of capturing and using relevant consumer data. To ensure that everyone, everywhere, has access to the health and hygiene products they need, our regional marketing and ecommerce teams require relevant data insights for each market in which Reckitt operates.

The challenges of navigating a fragmented data landscape

Before we created our consumer data ecosystem with Google Cloud, regional insights and achieving this level of data reporting was challenging. We had good insights into certain markets, brands, or aspects of our business, but because our data was fragmented across consumer databases it was impossible to connect data points across the business for comprehensive views of customers, campaigns, and markets. Our activation data was also stored separately from our sales data, making it difficult for us to understand the efficacy of our marketing campaigns.

Targeting relevant users with consolidated customer data

We needed a more unified approach to leverage consumer data effectively. Working with Google Cloud partner Artefact, we built what we call Audience Engine, which is designed to help us with audience activation. The Audience Engine uses Ads Data Hub to consolidate the consumer data from various sources such as websites in BigQuery, allowing us to analyze the path of consumers through our sites in far more detail than before. With the help of Vertex AI, our audience engine then builds models to show which users are in the market for which product, enabling us to build lookalike audiences and provide the right message on the relevant channels to more consumers. The more data that goes into the engine, the more accurate the modeling becomes, allowing us to channel our marketing resources more effectively. As a result, we have seen an average incremental increase in ROI of between 20% and 40%, depending on the campaign.

Once we realized just how powerful a tool our audience engine was, it was obvious that we should migrate all our consumer data to this newly created consumer data ecosystem with Google Cloud, that would form the backbone of our consumer marketing at Reckitt, while staying true to our Responsible Consumer Data Principles.

Modeling results of future campaigns with historical data

The migration began at the end of 2022, so our data transformation is still very much in its infancy, but we have already started to build some highly effective tools that are helping us to make our marketing operations more efficient.

A good example is our marketing ROI modeling tool, which allows us to predict how effective a marketing campaign will be before it goes live. With our historical marketing data unified in BigQuery, we are able to model potential results of specific planned campaigns to give our marketing teams the insights they need to adjust their campaign before it goes live. This helps us deliver a better ROI when scaling up those campaigns. Again, with our data previously fragmented across databases, such insights would have been impossible, making it harder to target our media spends effectively.

Analyzing campaign performance in near real time

Having all our data in BigQuery also enables far more effective reporting on our marketing performance. We can now deliver an analytics tool that enables our media departments to analyze the performance of everything from cost-per-click to ROI.

With these insights, our teams are not only able to optimize media spends or enforce compliance, they can also gain insights into a campaign’s performance in almost real time. This allows them to respond quickly, adjusting a campaign within hours, instead of days.

Becoming a data-driven organization

As we migrate more and more consumer data into Google Cloud, we have noticed a change in the way we work. We are now able to try out new things more quickly: we have been able to test new approaches with our audience engine in less than a month. We build innovative tools and products in a more agile manner, and are more creative in how we use Google Cloud solutions to achieve our aims. As a result, we feel more empowered to experiment and learn at speed.

For example, we are currently building advanced solutions using Google Cloud for predictive and measurement marketing solutions, helping us to master such capabilities internally.

Unifying all our consumer data in Google Cloud has given Reckitt the foundations we wanted. While we still have some way to go towards full data democratization, this backbone will eventually empower all our departments, and particularly our regional marketing and ecommerce teams, to access, understand, and make use of relevant data whenever they need it, to make timely, informed business decisions. That means empowering local teams to make decisions based on the data specific to local markets, helping us to get more health and hygiene products into the hands of those who need them, wherever they are in the world.

Source : Data Analytics Read More