ISG Provider Lens™ Google Cloud Partner Ecosystem - Data Analytics and Machine Learning - Europe 2022
Data and sustainability drive the Google ecosystem
In recent years, Google has moved far beyond its origins in search engines and consumer technologies to become a major force in cloud and business-to-business (B2B) technologies. As one of the largest hyperscale public cloud providers, the technology titan provides technologies that underpin the operations of thousands of enterprises of all sizes across the world, helping them sell goods and services, organize supply chains and communicate with customers. Google’s advanced capabilities in AI, data analytics and machine learning are becoming critical to business decisionmaking, providing unique insights into every aspect of business, from consumer analysis to financial forecasting to product inspection, among many others.
Despite this growing prevalence, many enterprises are still relatively new to the Google Cloud Platform (GCP), at least compared with those of the other hyperscalers, and they often struggle to fully capitalize on the platform’s native capabilities and functionalities. They, therefore, turn for help to the Google Partner ecosystem, a complex web of global system integrators (GSIs), service providers, independent software providers (ISVs), big data and analytics specialists, and boutique consultancies. With this ecosystem now approaching a critical mass in Europe in terms of both depth and variety of services, ISG is focusing on the ecosystem for the first time to provide IT and business decision makers with a clearer view of the relative strengths and weaknesses of different providers across five quadrants: Implementation and ntegration services, Managed Services for Google Cloud, Data Analytics & Machine Learning (DAML), SAP Workloads and Google Workspace Services.
Enterprises are turning to GCP for many reasons, but fundamentally to take advantage of three core strengths, as well as differentiators, of the platform: data analytics; sustainability and environmental performance; and affinity for cloud-native architectures.
First, getting greater value from data is key for enterprises when looking at cloud providers. While many enterprises have moved from on-premises to public cloud or hybrid cloud, they are still struggling to effectively extract value from their organizational data, either because it is trapped in silos or too unstructured for effective aggregation. Google Cloud comes equipped with a vast arsenal of advanced data analytics and machine learning tools, notably BigQuery, a highly scalable, multicloud data warehouse enabling real-time, predictive analytics for business users across vast data spaces. GCP also brings integrated platforms for data scientists (Vertex) and machine learning modelers (Auto-ML), conversational AI tools such as Dialogflow, translation and video AI tools, and lowcode applications such as AppsSheets for the budding citizen developers.
Ecosystem providers build on these native data and ML tools in many ways. In some cases, providers create custom point solutions for clients to address a specific business need, for example, a customer marketing platform using geo-spatial data from Google Maps, or a visual inspection tool for a manufacturing plant. Some providers are using the Google-native tooling to help enterprises bring stronger governance and searchability to their organizational data, for example by moving beyond traditional data warehouses and data lakes to the creation of organizationwide data meshes that allow domaindriven searching by individual business functions. Other providers are helping integrate the Google tools with external, third-party data sources. Many providers offer data advisory and assessment services to provide assistance with datamaturity benchmarking and assessment, data strategy and roadmap creation, and data-estate modernization. No matter what the model is, the core goal is to enable enterprises to extract greater value from their data.
The second core strength of GCP is its importance in helping organizations achieve their sustainability goals. Following the COP26 Climate Agreement in late 2021, the achievement of net-zero carbon emissions targets by 2030 has become the paramount sustainability goal for many large enterprises worldwide. Providers told us that environmental performance has, over the past year, become a key consideration for enterprises looking to migrate from onpremises data centers to the public cloud. Despite massive increases in computing power, hyperscalers’ data centers have achieved remarkable improvements in energy efficiency over the past decade, and Google Cloud, in particular, stands out for its carbon-neutral data centers and its commitment to sustainable computing. More broadly, the Google Cloud offerings and toolset play a key role in helping enterprises and industries achieve their broader sustainability goals. Providers can harness Google’s data and machine learning tools, for example, to help with solutions such as more accurate carbon accounting, optimization of cloud usage to lower carbon footprints, better measurement of ESG performance, optimization of manufacturing processes to reduce energy and materials consumption, or improved monitoring of sprawling supply chains. Providers are also creating workspace solutions to support remote and hybrid working, which again has a beneficial environmental impact through reduced travel.
The third core strength of GCP lies in its strong alignment with cloud-native technologies and ways of working. While containerized applications and Kubernetes orchestration platforms can be deployed across any public cloud, GCP is particularly suitable for such environments because of its highly scalable and composable architecture, its rich range of cloud-native tools, and its pioneering role in cloud-native operations and the development of site-reliability engineering principles. Google Anthos provides a unique platform for enterprises that wish to use the Google-native tooling across different hyperscaler environments. Ecosystem partners offer a range of implementation and integration services for GCP, from the basic lift-and-shift approach to full-scale modernization on the platform. The GSIs also typically offer a range of managed services, encompassing services such as multicloud management, operations support, observability, security, FinOps, reporting, predictive analytics, cloud automation and cluster provisioning.
The Google ecosystem continues to evolve rapidly, growing in scale, depth and complexity. ISG has identified several key trends shaping this still-emerging ecosystem:
First, we are seeing the emergence of Google-native industry clouds. Although these are not yet at the level of development of the Microsoft industry clouds initiative, providers are beginning to craft Google-native industry clouds in sectors such as banking, financial services and insurance (BFSI); healthcare and life sciences (HCL); retail; manufacturing; communications; utilities and others.
Second, ecosystem providers are using Google’s AI/ML capabilities to create very targeted, persona-driven services and solutions. These solutions include CFO data analytics solutions that provide forecasting of cash flow or other financial metrics, or CMO analytics offerings that provide insights into customer behavior or enable the optimization of marketing spend across different social media channels.
Third, the Google ecosystem is helping democratize access to powerful ML-based technologies. As one DAML provider put it to us, “AI is no longer the preserve of the global tech giants.” Previously, small and midsize enterprises could only look and marvel at the AI-based recommender engines of companies such as Netflix and Amazon that had the compute resources to train such engines on vast quantities of data. With the emergence of Google’s low-cost, cloud-based AI tools, nearly every enterprise can now access similar ML capabilities.
Fourth, providers are harnessing the DAML capabilities of Google Cloud to help enterprises craft new data architectures. The goal is better and faster access to data for different business users across an organization. In particular, we are seeing a move beyond centralized data lake architectures to cutting-edge data mesh constructs, in which data remains distributed across an organization in its own databases but is accessed through domain-driven machine learning capabilities. Different business users define and own the relevant domain, for example, financial data for finance users or customer data for marketing analysts, which then makes the relevant data discoverable and searchable.
Finally, the massive shift toward remote and hybrid working has given significant impetus to Google Workspace, Google’s suite of workforce communication and productivity tools. Although still relatively nascent compared to other platforms, Workspace is gaining traction due to its collaborative equity; it provides consistent performance across different devices, works equally well in remote versus office environments, has low training requirements and makes most features available to users independently of the licensing level.
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