Gone are the days when technology was relegated to the sidelines of a business, only considered essential for industries and professionals directly dealing with it. Today, it is not just a value-add, but an integral part of every enterprise’s operations and ultimately, its success. This has wrought many changes in the way we work, the most noteworthy of which is creating the necessity for every executive – not just the CIO & CTO – to familiarise themselves with the technologies of the future.

Among these is cloud computing, which is essential to doing business in the 21^st century. As the IT infrastructure needs of enterprises grow, and we inch closer to true mobility, it’s no surprise that cloud adoption is increasing by leaps and bounds. Unfortunately, too many C-suite executives dismiss cloud computing as being outside of their area of expertise. And while ignoring the technology aspect may have been possible for CEOs and CFOs in the past, it has now infiltrated every aspect of work, and arguably life itself. This makes it essential for executives across different functions and departments to familiarise themselves with at least the very basics of up-and-coming innovations.

One myth that has followed cloud computing is that it is a one-size-fits-all solution that businesses can merely install and use for storage. And while cookie-cutter solutions do exist, they end up doing more harm than good in the long term. To avoid this pitfall, it’s important for enterprises to understand the range of offerings at their disposal, and pick the one that most closely aligns with their needs.

Most people associate cloud deployment with subscribing to a third-party cloud service provider, which owns and manages all the resources that a firm might require. Known as the public cloud, this is the most popular option, especially for small and medium businesses that cannot bear the cost of IT infrastructure. Cost-effectiveness isn’t the only upside of this technology, though, since public clouds offer the added benefits of increased security, greater scalability, maximum uptime and ease of setting up.

Though the public cloud is the most widely used, it’s far from the only option that a business has. Enterprises seeking an added degree of customisation might find private cloud solutions to their liking. These offer an added layer of privacy, which may be mandated by the industry or jurisdiction that they operate in. Moreover, private clouds can allow access even in cases where geography or connectivity is unreliable. One of the factors that has held people back from switching to the private cloud has been the fact that they might need to buy and maintain the requisite infrastructure. In such cases, the Virtual Private Cloud (VPC) lets businesses tap into resources stored on a public cloud, within an isolated environment, with more granular control over virtual networks.

A fourth solution, known as the hybrid cloud, also offers the best of two worlds by utilising resources that are on-prem as well as on the public cloud. It is ideal for companies that already have money invested in IT infrastructure, but still want to make the most of the emerging technology. It can also serve as a stepping stone for ventures that are looking to make the switch, but have their reservations about the public cloud.

Each of these offerings comes with its own set of advantages and disadvantages. The key lies in evaluating your needs against the options at your disposal, and making sure that the two align. Only then will you be able to reap all the rewards of cloud implementation.

There are multiple reasons a growing number of database administrators, enterprise architects, application developers and other technology practitioners are moving to Google Cloud’s various database services.

Some are being driven by missing features in offering from other providers such as AWS. In Gartner’s Magic Quadrant for Operational Database Management Systems, the research and advisory firm points out that, “AWS’s surveyed reference customers scored its overall product capabilities one standard deviation (STD) below the mean. Their responses identified missing features such as multiregion writes and autosharding.”

Others are moving to database services on Google Cloud Platform driven by a few benefits. According to Gartner, “Reference customers repeatedly commented on Google’s ease of use and implementation, reliability and integration (with other services and other systems). Reference customers scored Google a full STD above the mean for satisfaction with GCP’s pricing; it received the second-highest satisfaction score of any vendor in this Magic Quadrant.

If you are looking to leverage the power of Google Cloud database offerings—but were unsure of which database services comes closest to the service you are currently using, here’s a handy map to find your way.

Access to the cloud has advanced the distribution and consumption of financial information on a global scale. In parallel, the global financial data landscape has been transformed by an influx of alternative data sources, including social media, meteorological data, satellite imagery, and other data. Exchanges and market data providers now find they need to include these new datasets to enrich their products and compete, which has meant they now must consider cloud-based models to keep up with the demands of their customers who expect easy, quick, flexible and cost-efficient ways to consume market data.

To address these needs, today we’re announcing the general availability of Datashare for financial services, a new Google Cloud solution that brings together the entire capital markets ecosystem—data publishers, and data consumers—to exchange market data securely and easily.

Datashare helps organize third-party financial information, making it accessible and useful to market data publishers and data consumers. We open-sourced the entire Datashare solution so market data publishers can now onboard their licensed datasets to Google Cloud securely, quickly and easily, while data consumers can consume that data as a service in tools of their preference, such as BigQuery.

Three ways to distribute and consume your data

Batch data delivery

Datashare provides a batch data delivery mechanism for data publishers to deliver their reference data, historical tick data, alternative market data sources and more via BigQuery, reducing the administrative burden on data consumers to extract insights from data. 

Real-time data streaming delivery

By using this event-based data delivery channel for rapidly changing instrument prices, tick data, orders, news and others via Pub/Sub, data consumers can reliably process individual messages or rewind to a point in time to replay a prior market scenario and test model changes.

Monetizing licensed datasets

Market data publishers can onboard their licensed datasets to Google Cloud and make them available via a one-stop-shop on Google Cloud Marketplace, enabling a new sales channel to expand market reach.

Reference architecture

Check out the diagram below to see how you can share your batch and real-time data directly to your Google Cloud customers with BigQuery and Pub/Sub.

As you can see in the above reference architecture, both publishers and consumers can derive several benefits from the solution:

Benefits for data publishers

• You no longer have to maintain your own delivery and licensing infrastructure.
• You can easily package and deliver granular data products and experiments with SQL.
• You can have a solution that scales with your business as data volumes and number of customers grow.

Benefits for data consumers

• Your data is ready for analysis and machine learning (ML)— you no longer have to maintain extract, transform, and load (ETL) pipelines to load files and transform data.
• You can avoid the expense and burden of maintaining multiple copies of large data files.
• You can be more targeted with consumption of data using BigQuery queries, improving performance, and compliance, and reducing cost.

Accessing the datasets

Google Cloud has been working with multiple industry firms on innovating in the market data space. By using Datashare for publishing, data publishers can make their entire datasets available on Google Cloud. Early adopters of Datashare include firms such as OneTick and Accern. OneTick’s datasets include reference and historical futures data (that can be accessed in our console with your login). Accern’s datasets include alternative data such as market sentiment and credit analysis data (that can be accessed in our console with your login).

To make it more helpful, we partnered with Accern to create a hypothetical scenario to describe the data acquisition and analytics process step-by-step.

Accern use case 

As a sustainability analyst, you require an economic, social and governance (ESG) dataset to determine which sector is the most widely covered ESG sector by analysts, and to also identify the sector with the lowest ESG sentiment score. Now, you can discover and acquire an ESG dataset in Google Cloud.

Step 1. Navigate to the Financial Services solutions page in the Google Cloud console:

Step 2. Click a dataset, for example Accern AI-Generated ESG Insights, then review the overview details, plans and pricing, documentation and support information. To view the available pricing tiers, click ‘View All Plans’. Once you’ve decided on a tier that you would like to subscribe to, click ‘Select’, choose a billing account and review and accept the terms of service to complete the subscription. Once the steps are complete, click ‘Subscribe’ at the bottom. An overlay window will appear, click ‘Register with Accern’ to activate and complete the subscription.

Step 3. Once activation is complete, you’ll be directed to the Datashare ‘My Products’ screen. Voila! You are now subscribed to Accern’s ESG Scores dataset and can access it in your Google Cloud instance using BigQuery. To access the data, click the hour glass icon on the corresponding ‘My Products’ record that you just purchased. An overlay will present you with the details on the dataset and/or table. Click the ‘Navigate to Table’ button to navigate through to the BigQuery console.

Step 4. Now that you have access and are in the BigQuery console, it’s time to generate data insights.

^{For this example, we’ve eliminated the company identifying information that is included as part of the subscription and aggregated company ESG in a view where each row represents a day, an industry sector, a specific identified ‘ESG Issues’ (event_group and event) and the respective ‘ESG Sentiment’ per issue.}

For example, row 1 indicates that within the ‘Healthcare’ sector, there was a ‘Social – Civil Society’ issue identified and it had a negative ESG sentiment score of -15.35.

Step 5. Generate a report by exporting it to Data Studio to build visualizations and conduct additional analysis on the ESG data.

Select ‘Export’ and ‘Explore with Data Studio’.

Step 6. Build a simple/basic report.

Now that the ESG data appears in Data Studio, you can start by building a simple chart to help you understand which industry sectors have the highest volume of discussions around ESG and the overall ESG Sentiment per industry sector.

To build the chart:

• Select the chart type ‘Table’.

• Include Entity_Sector as your dimension to aggregate results by ‘Industry Sector.’
• Include Signal_ID as a measure to count the number of ESG passages identified per ‘Industry Sector.’
• Include AVG(Event_Sentiment) as a measure to display the overall ESG Sentiment per ‘Industry Sector’ across ESG Issues.

You can see sectors that are  most discussed when it comes to ESG related topics and their corresponding ‘ESG Sentiment’ scores.

Step 7. Build your final report in Data Studio.

As a next step you can further drill into the data to understand ESG data specific to each ‘Industry Sector’ and identify positive and negative ESG practices. 

Accern has built a more complex sample dashboard and made it available publicly here. You can interact with this report and play around with the data. The dashboard can help to identify material ESG insights for each sector to inform your investment and risk processes. If you have additional questions, you can reach out to Accern directly.

Discovering, accessing and analyzing licensed datasets is quick and easy. Stay tuned for more updates on new licensed datasets.

Publishing your data via Datashare

If you are a publisher of market data, alternative, or exotic data, you can use Datashare to get it published on Google Cloud Marketplace.

Start by joining the Partner Advantage program by registering for the Partner Advantage Portal and applying for the Partner Advantage Build Model engagement. Visit our getting started guide for information to get started on publishing licensed datasets in the Marketplace. Stay tuned for a future blog post about using Datashare to publish datasets in the Marketplace.

More solutions for capital markets

Check out other Google Cloud solutions for capital markets.

The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.

However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.

The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.

Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.

In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.

Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.

BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.

THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.

While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.

As for performance, look at this chart:

With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.

Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.

With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.

SAP forms the critical backbone of thousands of enterprises, supporting critical business functions such as finance, supply chain, warehouse management, and more. Google Cloud provides a highly scalable and resilient infrastructure to run such workloads and offers tools, such as Smart Analytics and Machine Learning that can accelerate your organization’s digital transformation. 

In fact, a recent study by Forrester found that running SAP on Google Cloud can generate a 160% return on investment and a payback period of six months or less, thanks to legacy infrastructure cost savings, downtime avoidance, and productivity improvements.

How you deploy your SAP systems across your network has a tremendous impact on its availability, resilience, and performance. In addition to separate production and high-availability (HA) environments, SAP deployments typically include sandbox, development, quality assurance (QA), and disaster recovery environments as well. 

Because most of the Google network is virtual, SAP administrators can easily design complex landscapes that suit your organization’s SAP deployment and organizational structure while also meeting security and operational requirements. 

As you get started with SAP on Google Cloud, you’ll need to decide how to configure your networking to ensure the availability and performance of various SAP systems. Here’s a look at your options.

VPC and shared VPC

A virtual private cloud (VPC) is a secure, isolated private network hosted within Google Cloud. VPCs are global in Google Cloud, so a single VPC can span multiple regions without communicating across the public internet. Similarly, subnets can span across zones within a region. A zone represents a single failure domain, so typical SAP deployments place production and HA systems in different zones to ensure resiliency. Google Cloud simplifies this type of deployment, because subnets containing both production and HA systems can span multiple zones.  

This capability also simplifies SAP clustering, since the cluster’s virtual IP (VIP) address can be in the same range as those of the production and HA machines. This configuration shields the floating IP using Google internal load balancers and is applicable to HA clustering of the application layer (ASCS and ERS) and the HANA database layer (HANA Primary and Secondary).

Shared VPCs are a feature unique to Google Cloud that allows an organization to connect resources from multiple projects to a common VPC network. This lets them communicate with each other securely and efficiently using internal IPs. You can also centrally control the network for all SAP projects (service) from the Host project while using firewalls to inspect communication between compute engines in the same subnet, and between those in different subnets. (Best practice is to limit the communication between these systems to only the required ports — typically via SAP remote function call (RFC) communication at Layer 4.)

When designing your network, start with a host project containing one or more Shared VPC networks. You can attach additional service projects to a host project, which allows them to participate in the Shared VPC. It’s common practice to have multiple service projects operated and administered by various departments or teams in your organization.

Depending on your needs, you can deploy SAP on a single Shared VPC or multiple ones. The two scenarios differ in terms of network control, SAP environment isolation, and network inspection. Let’s look more closely at these differences.

Scenario 1: Deploying SAP on a single Shared VPC

If you require only a single network inspection, deploying SAP on a single Shared VPC has the advantage of simplicity and reduces administrative overhead.

• Network control: The Shared VPC serves as the network hub, allowing central network management based on identity access management (IAM) roles for the network team(s) in both production and non-production environments.
• SAP environment isolation: You can create projects and subnets for each SAP environment. Projects help group resources together for finer IAM control and billing visibility, while subnets provide network isolation for individual SAP environments. In service projects, compute engines can communicate by default; however, you can adopt simple firewall rules to block communication between compute engines within a subnet or in separate subnets.
• Network inspection: Use Google Cloud firewalls to allow only the required ports for communication between SAP systems. Leverage network tags and service accounts to define granular control for both north-south and east-west traffic.

Scenario 2: Multiple Shared VPCs for SAP deployment

In scenarios requiring additional network inspections, you can create multiple Shared VPCs, typically one per environment. Use peering between these Shared VPCs to enable RFC communication among the SAP development, QA, and production systems.

• Network isolation: Multiple Shared VPCs are completely isolated from each other except via specific ports opened in Google Cloud firewalls. This allows additional East-West traffic inspection by a Network Virtual Appliance (NVA) within a Google Cloud network. 
• Network control:As the number of Shared VPCs increases, activities such as peering and firewall policies also increase. This diminishes the central network control that Shared VPCs offer, so the network team should plan to manage the policies in each VPC separately.

Hybrid scenarios – for example, one Shared VPC for the production environment and one Shared VPC for all non-production systems — are also possible. This arrangement allows network inspection between production and non-production systems, and limits the number of central network administration layers to two.

Configuring the networking environment for multiple SAP systems can be a complex process. Thanks to Google Cloud’s Shared Virtual Clouds and other tools, SAP administrators can create scalable, secure networks that provide logic, resilience, and visibility to their cloud deployments.Learn more about these networking capabilities and our full offerings for SAP customers.