A B E T T E R W A Y T O T U R NI N S I G H T S I N T O A C T I O N

INSIGHTS DEMOCR ATISATION

T A B L E O FC O N T E N T S

Collecting more data than we understand ..............................................................

Insight democratisation: the missing piece of the puzzle .....................................

Roadblocks between data and insight .....................................................................

Creating an Insight-Driven Organisation .................................................................

Cloud-enabled capabilities .........................................................................................

The building blocks of an IDO ...................................................................................

The roadmap to becoming an IDO .........................................................................

Industry-leading examples ......................................................................................

Why choose us? .........................................................................................................

What’s the first step you should take? ....................................................................

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Collecting more datathan we understand

Organisations have been harvesting data since the Sumerians first put stylus to cuneiform. What’s changed over the past couple of decades is the exponentially increasing amount we’re collecting.

Personal data on e-commerce sites, location data provided by GPS-enabled devices, and social media clicks that track consumer behaviour: these all add up to “big data” that has improved customer service, sharpened operational efficiency and guided the decision-making process.

But those biometrics, credit card numbers and location data, multiplied by millions of customers and users, all add up. By 2025, the global datasphere will constitute some 175 zettabytes (1 zettabyte=1021 bytes)—up over 500% from 2018—thanks to tremendous growth "at the core (traditional and cloud datacen-ters), the edge (enterprise-hardened infrastructure like cell towers and branch offices), and the endpoints endpoints (PCs, smart phones, and IoT devices)."1

21IDC, "The Digitization of the World From Edge to Core,” November 2018.

Given the exponentially increasing rates of data collection, it’s all too easy for organisations to get caught up in the data-gathering process, sacrificing the ability to gain actionable insights from those datasets.

Today's organisations spend so much time and many resources collecting, updating and classifying data – creating datasets so large and complex that traditional data management and analysis tools cannot process them in conventional timeframes.

Organisations might also inadvertently silo their data, limiting its access to certain people or departments. If the right people can’t get the data they need when they need it, it hinders the process of extracting actionable insights from the data.

It’s no wonder that many organisations feel they’ve been subjugated to their data, not the other way around: furiously gathering datasets from different sources, while lacking the capacity to convert them into actionable insights.

Data Decisions

Insights Actions

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O V E R W H E L M E D B Y D A T A

E F F I C I E N C Y & T I M E T O I N S I G H T

The disruption of the past year has increased the urgency for organisations to turn their data into insights, and data-driven insights into action. Organisations also need to become moreagile and dynamic, to uncover insights and actions that their competitors cannot.

All told, today’s data-driven environment has raised the bar for businesses, who need to significantly improve performance in the following areas:

Efficiency. Converting data into insightsmakes the decision-making process moreefficient by streamlining it. The focus ofhuman input should be on making decisionsand acting on them, instead of the process ofcollecting, updating, correcting and analysing data.

Time to insight. The quicker you can transform data to insights, the better you can position yourself for your customers and your operations across any disruption. Organisations that can instantly access and understand data make quicker decisions.

There is now a new approach to data analytics that helps businesses make informed – and better – decisions. And it involves getting data-driven insights.

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Insight democratisation: the missing piece of the puzzle

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Data abhors a hierarchy. Modern organisations are now more dependent than ever on the free flow of information, not just within the C-suite but also between departments further down the line.

Frontline staff must have information customers need—and fast. Workforces need to use and share information with their colleagues—whether in the same office or on the other side of the world—to make better business decisions.

Management needs immediate access to information to respond better (and faster) to business challenges. And the executive team needs to understand information flowing into and across the organisation to create a more effective overall strategy.

In today’s data-saturated age, how do you ensure that all levels are able to derive the insights they need from the available data? And do they have access to the data they require in the first place?

N O G A T E K E E P E R S , N O S I L O S

More companies are realising the benefits of democratising insights across all levels of the organisation.

Instead of data being siloed per department—or allowing access only to a limited few—access to data should be granted to decision-makers at all levels, from frontline staff all the way up to the C-suite.

Instead of limiting analytics tools to executives or specialised technicians, these tools should be provided to all levels so staff can make sense of the data they’re seeing, and in turn, help them make better decisions and uncover business opportunities. .

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Having access to data empowers individuals in your organisation; it devolves upon them the responsibility to use the insights in the decisions relevant to their roles. Given the right tools to draw insights from this data, they can tackle complex business problems and answer essential “what's next” questions.

Organisations are beginning to see the advantages of insight democratisation. In a Google Cloud/Harvard Business Review paper2, 97% of industry leaders considered democratising access to data and analytics as important to business success.

M A C H I N E L E A R N I N G : K E Y T OI N S I G H T S D E M O C R A T I S A T I O N

Many present-day organisations count on spreadsheets, visual analytics tools or interactive statistical packages to make sense of their data. But as the technology and data environment has changed, these platforms are no longer sufficient to satisfy organisations' insight needs.

Past platforms relied heavily on linear regression analysis. These tools are quite capable at deriving limited insights from past data, but have difficulty “re-decisioning” based on ever-changing factors.

New cloud-based tools can now use logistic regression models, with the ability to predict a wider range of outcomes. This represents the next step forward: giving organisations the capability to make rapid, real-time decisions using powerful predictive analytics tools.

Automated Machine Learning (AutoML), for instance, makes machine learning available to non-experts through its methods and processes, thus improving efficiency. It also tests overall hypotheses and individual model attributes at a much quicker speed. The faster the decision-making process, the greater advantage a business has over competitors that don't use insights to make business decisions.

2Google Cloud, "The democratization of insights: Empowering data analysts and business users," 3 November 2020.

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An organisation that leads with insights is more agile and able to deal with disruptions. The quick decision-making process is a strategic advantage, with data converted into measurable business value in a matter of minutes—or even seconds—instead of days.

Businesses can leverage digital capabilities to bring insights to everyone, improving the speed and quality of decisions while reducing decision-making costs.

Before they can reach this ideal, many companies may have to contend with the following institutional hurdles.

Roadblocks betweendata and insight

Automated machine-learning tools can generate advanced analytical insights at a much faster and broader level than ever possible. It's a necessary shift because data and algorithms help to enhance human capabilities, thus enabling us to accomplish more.

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C O R P O R A T E C U L T U R E

It's not enough to give everyone in an organisation access to insights and leave them to it. Even with the best data insights, they are unusable if nobody in your organisation is willing to or knows how to derive valuable business decisions from them.

It's crucial to create a culture where everyone lives and breathes insights, a workplace where data-driven insights shape decisions in the organisation. This is what we like to call an Insight-Driven Organisation (IDO).

L I M I T A T I O N S O F T R A D I T I O N A LD A T A W A R E H O U S E S

Data warehouses used in the past—and in present times—hold organisations back in several ways.

Speed: Traditional data warehouses cannot scale fast enough to keep up with the volume of data being collected in the current digital climate. They may take a couple of weeks to gather data, create the workflow and run reports—that’s far too slow to react to customers in real time, or to respond to developing macroeconomic events. By the time you get the insights you're looking for, they're outdated.

Cost: Organisations are unable to invest in new data warehouses due to their high costs. The differential cost between an on-premises data warehouse and one in the cloud can be as much as 60%!

Obsolescence: While traditional data warehouses can still process large volumes of data, their inherent inefficiencies pose major challenges when it comes to deploying more advanced analytics processes. Data access restrictions inhibit collaboration. Plus, traditional data warehouses do not support machine learning and artificial intelligence (AI) initiatives.

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For an Insight-Driven Organisation (IDO), analytics is a core capability across the organisation.

IDOs entrench data, analysis and reasoning into their decision-making process, and promote a culture of data-driven decision-making. Every decision made by an IDO, at every level, across the organisation, is based on data and actionable insights derived from them.

Insights are embedded into the business' processes, becoming central to decision-making on both micro and macro matters. The tools to derive these insights might not be deployed across the organisation, but they should be put to use in essential areas of the business where their competitive edge matters.

Analytics helps leaders make more informed,more effective and more intelligentbusiness decisions. In an IDO, thisstrength is amplified; decisions madeusing analytic insights drive theirlong-term strategy, creating positive results.

Creating an Insight-DrivenOrganisation

W H A T I S A N I D O M A D E O F ?

There is no single element that makes an organisation an IDO. A multi-dimensional approach consisting of data and tools, talent and culture allows organisations to fully leverage the insights they derive and embed them into decisions and actions.

IDOs hold insight democratisation as a key principle, with an empowered workforce operating on shared, flexible data sets. They have a clear vision enabled by a powerful platform.

Every person in an IDO must be empowered to know where to find the insights (and quickly), plus how to understand them. And an IDO holds enterprise data governance as a central principle, in order to effectively deal with high volumes of data and ensuring insights.

Training in data analytics is a plus: in organisations where all employees have been trained on analytics, 88% exceeded business goals compared to just 61% in companies where only a select group of employees experienced analytics training.3

Analytics projects in IDOs often begin in pockets of the business; they then figure out how to scale these projects across the entire organisation. This ensures a shorter distance between ‘experimentation’ and ‘industrialisation’.

A shift in an organisation's mindset is essential; it needs to evolve into one that's data-driven and insight-based. Culture also plays a significant role in deciding important issues such as to what extent data is democratised, how this data is viewed across the company, and how data is positioned.

3Deloitte Insights, “Analytics and AI-driven enterprises thrive in the Age of With”, 25 July 2019. 10

IDOs have a relentless focus on customer service: they believe that customer value drives competitor advantage. Understanding what your customer is looking for allows your organisation to change strategy to address any issues. Insights that provide a better understanding of your target audience promote customised, relevant and engaging experiences with consumers.

An IDO uses insights to drive change via the following steps:

• Make decisions based on insights • Capture data on these decisions and give feedback • Use this feedback to further refine products, services and processes. All these components contribute to the IDOs’ success in increasing revenue, reducing cost, mitigating risk and out-competing the competition in the areas that matter.

The cloud advantage

The cloud unlocks analytical abilities that far outstrip anything that is possible on legacy infrastructure. With cloud-based tools, your insights-driven organisation can leverage real-time data to generate early analyses of trends. You'll uncover new customer segments and anticipate sales accurately. And you'll gain the speed and insight that bolsters your relevance to your customers.

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C O L L A B O R A T E

Data-sharing is often a problem with traditional data warehouses, as they generally limit user access rights. With the cloud, it's easier to collaborate as security-by-design enables granular access to shared data. It encourages more collaboration between business units and between individuals as everyone's working on the same data on the back end. Google Cloud Data QnA, for example, allows users to get data insights without needing to approach business intelligence teams.

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All parties who need to see the data have full visibility,which boosts collaboration and the capacity to reactquickly to changes in customers or the market. It's also possible to curate data so that certain people haveaccess to specific data.

P E R C E I V E

With a platform that can take in external data, process it and pick up trends early, organisations can react more quickly. Streaming companies, for example, collect and analyse diagnostic data from millions of TV boxes in real time. They can then connect individual users with the content and services they enjoy the most, without any bottlenecks in the data collection process. Beyond the day-to-day, companies can use this data to see overall patterns and plan strategy for the long term.

C A T E G O R I S E

The use of AutoML results in better categorisation, which is invaluable in uncovering new customer segments and tailoring experiences for your customers. As you collect more data, the output gets better and more targeted, depending on how strategic you are about using the data on the cloud.

Google Cloud Natural Language API can be used to classify and categorise digital content in real time. A publishing company, for instance, might use this tool to improve speed and accuracy; reduce manual labour; and acquire insights into content consumption.

Such cloud-based tools can help publishing companies gain deeper insights into how their content resonates with its audience. They can then use these insights to personalise content for readers, keeping them more engaged.

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The building blocks ofan Insight-Driven Organisation

P R E D I C T

Cloud-enabled insights help organisations put the right product in the right place or in front of the right customer. Built-in machine learning can predict business outcomes easily, without the need to move data.

R E C O M M E N D

Customers demand real-time relevance from the companies that serve them – and organisations are eager to comply. This is where the cloud steps in – keeping you one step ahead by making accurate, data-driven recommendations.

AI can help organisations recommend products to customers based on their individual preferences and history. Helping your products become intimately relevant to your customers will inevitably help your bottom line.

We know that the right mindset in an IDO is essential, but there also needs to be action. An IDO asks the right questions, performs the right analysis, and takes the right actions. And it establishes an appropriate information and data foundation across five essential elements.

It's not just about data and technology; an IDO must also incor-porate their strategy, people and processes. Every element in these five areas needs to be fully in place and functioning optimally before beginning your analytics journey.

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PROCESS

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DATA

TECHNOLOGY

STRATEGY

PEOPLE

PROCESS

Analytics Vision

ValueDrivers

StakeholderManagement

OperatingModel

Leadership

Innovation

Ideationand

PrioritizationGovernance Agility

ProcessRe-engineering

Insight-ledDecisionMaking

OrganizationalDesign

Cross-FunctionalAlignment

AnalyticalCompetency

KnowledgeManagement

InformationModel

DataAccessibility

andUsability

ReferenceArchitecture

DataQuality

DataManagement

AnalyticalMethods

DataDiscovery

Tools

EnterpriseAnalytics

Tools

Cloudv.s.

On-Premise

CognitiveApplications

S T R A T E G Y

What are you trying to accomplish with analytics? Set out your key business objectives and find out how AI can help you get there. Key stakeholders must be aligned with this vision and supportive of your journey.

Your organisation's strategic planning processes must identify key strategic priorities, and zero in on areas where data and analysis are most needed to realise core objectives. Describe your vision, build a business case, commit to continued improvements and gains, and maintain key stakeholder support.

P E O P L E

Having data and technology experts isn't enough to be an IDO; other skills are vital too. IDOs build 'purple teams' by combining those with 'red skills' (data scientists, software developers) and 'blue skills' (change managers, political navigators). This results in the right mix of technical, analytics, communication and business acumen.

But it's not just about having the people with the right job skills. There also needs to be a culture across all the decision-makers to ask the important questions that will drive the organisation's decisions and actions.

IDOs adopt an organisational design that empowers their people to rely on the insights that their data uncovers. Everyone needs to be incentivised and organised to support the IDO journey. And this is the culture that exists all around – from those in the head office to the frontline staff.

P R O C E S S

IDOs go beyond just capturing and distributing the right data. They also value the process that turns data into insight and helps them act upon that insight.

To make this happen, an IDO needs a solid governance framework and operating model, embedded measurement frameworks, and a feedback mechanism. A robust governance framework ensures that your analytics priorities align with your corporate vision, and your tools and processes are efficiently managed. Additionally, the framework ensures that the right insights are delivered to the right people at the right time.

The framework should also account for the challenge of manage-ment and adherence to regulatory and legal requirements; and the reality of data explosion and disruptive technologies down the line.

Successes and best practices should also be shared across the organisation. The framework should ensure that the operating model matures through learning events, sharing successes and rewarding innovative and collaborative behaviours.

Embedded measurement frameworks help to quantitatively assess the effectiveness of the insights and the actions taken in response to them. Finally, a feedback mechanism empowers teams to continuously improve processes and refine focus areas in response to real-world results.

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The disruption of the past year has increased the urgency for organisations to turn their data into insights, and data-driven insights into action. Organisations also need to become moreagile and dynamic, to uncover insights and actions that their competitors cannot.

All told, today’s data-driven environment has raised the bar for businesses, who need to significantly improve performance in the following areas:

Efficiency. Converting data into insightsmakes the decision-making process moreefficient by streamlining it. The focus ofhuman input should be on making decisionsand acting on them, instead of the process ofcollecting, updating, correcting and analysing data.

Time to insight. The quicker you can transform data to insights, the better you can position yourself for your customers and your operations across any disruption. Organisations that can instantly access and understand data make quicker decisions.

There is now a new approach to data analytics that helps businesses make informed – and better – decisions. And it involves getting data-driven insights.

D A T A

IDOs don't confine their analytics focus to internal structured data sources. Tapping external and unstructured data sources can enhance decision-making, risk analysis and scenario planning. These sources include social channels, third-party statistics, economic trends and even weather patterns.

Instead of drowning in large volumes of data, IDOs understand that they don't need to work with all their data; just relevant data. These organisations put well-designed information models into place and adopt a realistic approach to data quality while ensuring regulatory compliance.

IDOs also carefully consider the ethical implications of how they use their data. It applies to what they should do with the data they have collected so they don't breach stakeholder trust or damage their reputation. In short, organisations have a moral responsibility to treat their customers' data with integrity and respect.

T E C H N O L O G Y

As technology evolves, it becomes less complex and also less costly to build an effective analytics platform. But there is a need to start small, so the aim is to run pilots or proof-of-concept projects in a sandbox environment.

With this approach, your organisation can generate proof points in a safe setting. It also stops you from making mistakes that could potentially impact your whole organisation.

IDOs need a robust and secure technology infrastructure to be successful. Also, data must be accessible anytime and anywhere. This easy access to reliable and comprehensive information means stakeholders can make informed judgement calls.

Well-thought-out solution architecture is a must, as are people with the requisite technical skills. Also crucial is a delivery model to disseminate insights across the organisation, plus a structured approach for collaborating with third-party technology partners.

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Descriptive | What happened? Human input

Human inputDiagnostic | Why did it happen?

Decision SupportPrescriptive | What should be done?

Predictive | What will happen?

Cognitive computing | Decides what should be done, learn and improve from outcome

Decision Action Feedbackand

LearningHuman input

Decision Automation

D A T A I N F O R M A T I O N I N S I G H T

As you move along the IDO journey, your questions will begin to shift in emphasis.

Instead of asking 'what happened?', you’ll feel empowered to ask instead 'what will happen?':a shift from Descriptive to Predictive.

The next shift will move you from Prescriptive to Cognitive: from asking 'what should be done?', your mindset will now focus on 'decide what should be done, then learn and improve from the outcome'. This frees you to take action, offloading repetitive tasks that require advanced analytics.

Cloud platforms, with their next-generation capabilities and the right data governanceframework in place, make these shifts possible.

The roadmap tobecoming an IDO

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I N S I G H T S D E M O C R A T I S A T I O N

Predictive Analytics Pilots and Prototypes

Advanced Decision Support and Real-Time Pilots and Prototypes

BUILDING THE CORE

The organisation can answer “what has happened?” and

“why did it happen?”

CREATING FORESIGHT

The organisation can now answer “what will happen?”

and “what is the best option?”

ADVANCED DECISION SUPPORT

The organisation can now answer “what should I do”

with decision support rolledout for key operational decisions in real-time

BI & AnalyticsMaturity Scale

Prescriptive

Predictive

Diagnostic

Descriptive

Horizon 112-18 months

Horizon 218-36 months

Horizon 13-5 months

A T Y P I C A L I D O J O U R N E Y

The journey begins by building the core and exploring first use cases, which usually takes between 12 to 18 months.

During this period, the organisation can answer the questions 'what has happened?' and ' why did it happen?’. This era is known as 'Horizon 1': when the organisation undertakes pilots and prototypes to prove to stakeholders the benefits of its IDO journey. As momentum grows, they might even start exploring advanced decision support and real-time pilots, and develop an enterprise data management practice, to effectively manage data.

During 'Horizon 2', which takes place from months 18 to 36, organi-sations create the conditions for foresight, so that they can answer 'what will happen?' and 'what is the best option?'. This is done by adding new data sets and exploring the data in the platform.

The organisation may also begin setting up analytics pods – agile teams consisting of product owners, scrum master, data analysts, data engineers and data scientists. They may also move their analytics capabilities up a notch with the help of AutoML and AI.

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The last stage, 'Horizon 3', is where advanced decision support takes centre stage. The organisation can now answer 'what should I do?', rolling out decision support for key operational decisions in real time. All this happens between years three and five.

This last era is when AI and analytics play a significant role, as the organisation works together with machine learning algorithms and platforms.

The IDO journey is now complete, and organisations can reap the rewards of being a business driven by insights.

Industry-leading examples

Insights are useful no matter what industry an organisation is in. The following examples demonstrate the value of insights in opening new opportunities to win over customers, and map your organisation's future.

T E L E C O M M U N I C A T I O N S

Mobile service providers can use cloud-based analytics tools to tailor recommendations to customers, whether they’re new products or custom promotions. Once they get insights into what their customers do most on their phone (eg. streaming, messaging apps), providers can target promotions to them, leading to higher customer satisfaction.

Insights also allow them to flag customers before their mobile phone contracts expire,so they’ll retain them as customers instead of losing them to competitors.

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B A N K I N G

Banks can leverage insights to provide better customer service, reduce risks and increase employee collaboration. Getting insights into customers and their spending habits also allows banks to custom-package products such as housing loans and wealth management to existing customers.

C O N S U M E R

Organisations can use insights to decide how to load delivery vehicles or make more targeted operations adjustments. Data points run through machine-learning models include package weight, shape and size, and facility capacity across the network. It's also possible to derive insights for forecasting purposes, which then reduces forecasting uncertainty.

H U M A N R E S O U R C E S

Businesses can use optimised insights to manage workflow within the company and strive to retain high-value employees. It's more expensive to find and train new employees, so insights can help pinpoint top performers who are at the highest risk of leaving.

The human resources department can gauge these employees' performance and value, assess trends proactively, monitor organisational and market factors based on location, and assess their likelihood of departure. This allows the organisation to deal with each case in a more personalised manner.

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Deloitte & Google Cloud –Fulfil your future of possible

What’s your possible? It’s as big and bold as you want to make it. Coupled with the unparalleled power, flexibility, and security of Google Cloud, Deloitte can help you find dynamic new opportunities, quickly navigate challenges, align your goals with real results, and provide industry-specific solutions and experiences to unlock competitive advantage every step of the way.

As a premier partner of Google Cloud and the only one having received the Global Services Partner of the Year four times, Deloitte helps you realise your possible fast—and at scale—no matter where you are on your transformation journey. That means extended presence at the drop of a dime, agile capacity that can anticipate and react to business change quickly, and a future-proof legacy just waiting to be fulfilled.

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T H E G O O G L E C L O U D A D V A N T A G E

Data is in Google Cloud's DNA, and business transformation is what Deloitte does best. Our strengths dovetail seamlessly to offer customers the best in data services and market-leading analytics, with information management services and solutions.

Google Cloud’s powerful tools can make short work of the most common roadblocks between data and insight. Google Cloud’s migration framework offers a rapid gateway to modernisation from legacy virtual machines (VMs), containers, databases, or storage.4

AI Platform, BigQuery, and other Google Cloud services offer access to native integrations, enabling rapid development of intelligent solutions.

BigQuery’s serverless architecture reduces oreliminates capacity planning cycles; and its built-inML capabilities can lower total cost of ownership by 52%.5

Google Cloud DataQnA empowers business users to getanswers to their analytical queries through natural language questions. Users simply ask a question on their company's dataset, and the results are provided via the same tool. DataQnA can be embedded easily in chatbots, spreadsheets, BI platforms and custom-built UIs.

The ability to get answers to analytics on the spot, when you need it, without having deep technical knowledge, leads to a rise in productivity and a dramatic improvement in business outcomes.

4Google Cloud. "Migration to Google Cloud: Choosing your migration path." April 29, 2021.

5Enterprise Strategy Group. "The Economic Advantages of Migrating Enterprise Data Warehouse Workloads to Google BigQuery." March 2019

Before embarking on your IDO journey, ask some vital questions. What competitive advantage do you expect insights to bring to your organisation? And what gaps are you trying to fix in your current operating model?

Decide on the small-scale pilot projects you would like to embark on first. With our assistance, you can then take the following steps to scale up your evolution into becoming a true Insight-Driven Organisation:

• Define your vision for becoming insight-driven • Identify a target operating model to organise data and insight teams for success • Ensure you have the right capabilities and resources to support your strategy • Leverage modern technical platforms where they will deliver the broadest impact • Develop a fit-for-purpose plan across processes and standards to be an innovative leader

Are you ready to take the first step? We’re ready to listen, and help you begin your journey.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties. DTTL and each DTTL member firm and related entity is liable only for its own acts and omissions, and not those of each other. DTTL does not provide services to clients. Please see www.deloitte.com/about to learn more.

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This communication contains general information only, and none of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms or their related entities (collectively, the “Deloitte organization”) is, by means of this communication, rendering professional advice or services. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser.

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© 2021 Deloitte Southeast Asia Ltd.

What’s the first stepyou should take?

L E T ’ S T A L K

Chris LewinAI & Data APAC Lead Deloitte Consulting Southeast Asia chrislewin@deloitte.com

Leonard Jayamohan DirectorDeloitte Consulting Southeast Asialjayamohan@deloitte.com

Jean-Philippe IssalyPartner Development ManagerGoogle Cloudjpissaly@google.com