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Data on cloud: Getting to and successfully living on the cloud

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In our ever-changing world, enterprises are moving to the cloud to adapt at speed, build resilience, and make room for innovation. In fact, the global cloud analytics market is expected to grow from $23.2 billion to $65.4 billion by 2025.  These investments will empower employees across the enterprise to make better and faster decisions that align with overarching business objectives.

Successful transitions to the cloud require a well-considered and agile strategy. With data on cloud, enterprises can rapidly democratize access to data, make data-driven decisions more easily, reduce operational costs, and move from experimentation to
innovation more quickly.

Across every industry, traditional enterprises must make the cloud transition. Particularly as businesses ‘born in cloud’ are acting with agility. These businesses are gaining market share, adapting to customer needs, and attracting fresh talent – more so than less
nimble competitors.

However, there is a clear gap between the cloud aspiration and reality – often because many enterprises fail to realize that cloud is a journey, not a destination.

Getting to and living on the cloud

Getting to the cloud is a goal for many enterprises, largely driven by the need for a single trusted source of data and a cost-effective and agile way to store, access, and generate actionable insights from this data. But living on the cloud is a journey. With a robust data on cloud strategy, enterprises can truly live on the cloud and enable employees to self-serve analytics, act with agility to address new business needs, and move machine learning (ML) concepts from experimentation into production quickly.

Unfortunately, many enterprises treat cloud as primarily an IT initiative. Without active engagement from other business units, data that accumulates in data lakes soon becomes unusable data swamps. Plus, teams face increasingly complex issues with data quality, outdated operating models, skills and culture, and a lack of governance that can limit the benefits of the cloud. 

To get to and truly live on the cloud, successful enterprises follow a three-step approach for data on cloud:

  1. Develop a bespoke strategy: How will you blend business and IT strategy and leverage a design-to-value approach to identify use cases to solve? How will you address specific business issues and underlying process challenges associated with different functions? What partners, with relevant industry and process expertise, can you call upon for guidance?
  2. Find structure with cloud data services: How will you connect your entire data and analytics ecosystem in the cloud? What methodologies will you use to create a seamless journey between data sources, business needs, and end consumers?
  3. Transform your operating model with talent: In what ways will your operating model and company culture need to evolve? How will you democratize data? What steps will you take to bring in cross-functional and multidisciplinary talent to support your vision? 

Let’s explore these steps in more detail.

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Develop a bespoke strategy

The data on cloud strategy will be unique to every enterprise depending on a variety of factors including their target markets, size, and industry. To develop a robust strategy, enterprises must enhance their data, analytics, and technology experience with industry and process expertise. Strategies that are clearly tied to business objectives will deliver the returns that business leaders are looking for.

Case Study

Applying industry expertise in banking

One US bank relied heavily on advanced analytics to uncover insights into its customer base. When the bank began to reach the limits of its analytics capabilities in on-premise infrastructure, it quickly decided to move to the cloud.

By focusing on the nuances of the banking industry and the uniqueness of its processes, the bank developed a bespoke strategy working with both business and IT to identify use cases for a rearchitected data on cloud environment. Today, analyst productivity has improved, and the bank has uncovered new insights into how to attract and retain customers.

Find structure with cloud data services

The way the enterprise structure their data on cloud will be closely tied to achieving their business objectives. Though these objectives may differ with every enterprise, developing a comprehensive intelligent data fabric will be crucial.

A data fabric is an interconnected network of data products wherein each data product connects business objectives and metrics to underlying data elements (figure 1). Just as monolith business applications are getting decomposed with microservices architecture, a data fabric decomposes historical analytical datasets to create data products that are easily discoverable, consumable, understandable, and trustworthy. 

Within the data fabric, data products are continually evolving. They become digital building blocks that need refining when new requirements come in from the business. They can be tweaked to incorporate the requirements or completely redesigned to create an entirely new data product – all without the need to change multilayered warehouse and data lake architecture.

With an orchestration layer on top, this data fabric can feed systems of insights and systems of engagement. This creates a real-time journey between data sources, the cloud data platform, and consumers. Ultimately, it gets actionable insights to the relevant employees at the right time.

Figure 1: An example data fabric

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Case Study

Moving from silos to seamless in retail

A leading global retailer struggled with data silos. It had 20 nonintegrated systems processing 6 million invoices across its stores and warehouses leading to supplier disputes – over 70% of which ended in refunds. The solution was to develop a procure-to-pay data fabric as part of a data on cloud strategy to enable speed and agility. Seamless data flows – enhanced with ML and automation – now match the right invoices to the right receipts. The employee and supplier experiences have improved, and disputes have reduced by 40–50%.

Transform your operating model with talent

Business leaders need to develop a different kind of operating model to realize the benefits of data on cloud. They must embrace new ways of working and a cloud culture that sparks innovation within a flexible, scalable, and agile mindset.

There are different operating model archetypes and depending on their org construct and where they are in their cloud maturity determines which operating model they should adopt. One model is a federated, hub-and-spoke operating model, wherein shared services and a center of excellence lie at the hub, and business units specific analytics use cases are addressed at the spokes. 

Pods of cross-functional and multidisciplinary individuals work together to activate these spokes. To be successful, an enterprise product owner supported by process experts, functional experts, data engineers, data science and analytics specialists, user experience experts, and the IT team leads these pods.

These pods need support from a shared services data platform team that manages the data infrastructure and provides a standardized way to store, prepare, secure, access, and serve data-driven insights. Beyond these pods and shared services, data must democratize so every employee understands what value a data on cloud strategy delivers for them and their team. Only then will cloud truly be embraced across the enterprise. 

Case Study

Creating exceptional experiences in manufacturing

A large enterprise retailer started its cloud journey in 2012 and had much of the necessary infrastructure in place. However, employee adoption was poor. The enterprise wanted to create a data-driven culture, simplify processes, generate trust in the data, and empower employees to make better decisions, inspire innovation through data democratization, and generally transform the employee experience.

The solution was a Netflix-style user experience, which included personalized profiles relevant to individual employee needs and preferences. This democratized access to personalized analytical insights, which empowered employees to make informed decisions at speed and scale.

Reach new heights with data on cloud

Data on cloud calls for a bespoke strategy, data structure, and operating model – supported by multidisciplinary and cross-functional skills. This approach helps enterprises solve problems and achieve business objectives by effectively transforming data into actionable insights. Insights that allow enterprises to live on the cloud and soar above the competition, even during turbulent times.

As the COVID-19 pandemic permanently changed the way people lived and worked, cloud provided lifesaving connectivity for many enterprises. As lockdowns ease, business leaders must fight complacency and continue their cloud journey. The pandemic is just one example of extreme disruption enterprises must use data on cloud to develop the speed, agility, and innovation required for whatever comes next.

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