Moving your data to the cloud

Enterprises continue to use cloud technology to build business resilience and agility. Especially when it comes to data strategy, moving data to the cloud allows leaders to rapidly democratize access to data, empower employees to make data-driven decisions more easily, and reduce operational costs.

Cloud also lays the foundation for future innovation. It simply isn't possible to adopt technologies like generative AI successfully without the cloud.

But what sets thriving businesses apart is the ability to put the cloud into context. In other words, technology leaders carefully identify how the cloud will benefit their industry and business units. In fact, Allied Market Research predicts the market for industry cloud solutions and platforms will exceed $350 billion by 2031.

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

Many enterprises want to move data to the cloud. This desire is 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.

But truly living on the cloud takes things a step further. If you live on the cloud, it becomes the foundation of all business decisions and operations. This enables employees to access 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 the cloud as primarily an IT initiative. Without active engagement from other business units, data lakes become unusable data swamps. And teams face complex issues with data quality, outdated operating models, and a lack of governance.

To truly live on the cloud and create industry-specific cloud solutions, we recommend a three-step approach:

1. Develop a bespoke strategy: How will you unite business and IT goals? How will you consider the specific needs of your industry? How will you address process challenges associated with different functions? If this seems overwhelming, consider what partners, with relevant industry and process expertise, you can call upon for guidance.
2. Find structure with cloud data services: Many existing cloud services can accelerate your cloud journey. So how will you use them? Will you connect your entire data and analytics ecosystem using the cloud? How will you create a seamless journey between data sources, business needs, and end consumers?
3. Transform your operating model with talent: People should be at the heart of every transformation. So, how will your operating model and company culture need to evolve to support the cloud? How will you democratize access to 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.

How each enterprise chooses to manage data on the cloud will depend on a variety of factors, including target markets, business size, and industry. However, we believe the best cloud strategies will also incorporate industry, functional, and process expertise. And they will be clearly tied to business objectives that will deliver the returns that senior 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-premises 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 the cloud. Today, analyst productivity has improved, and the bank is able to use its data more effectively to uncover insights into how to attract and retain customers.

However businesses choose to manage cloud data services, developing a comprehensive intelligent data fabric will be crucial for everyone.

A data fabric is an interconnected network of data products where each product connects business objectives and metrics to underlying data elements (see figure). 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 can be refined when new requirements come in from the business. They can also be tweaked to incorporate the requirements or completely redesigned to create an entirely new data product.

With an orchestration layer on top, this data fabric feeds systems of insights and systems of engagement, creating a real-time flow between data sources, the cloud data platform, and consumers. In short, it gives actionable insights to the right employees at the right time.

Case study: Moving from silos to seamless in retail

A leading global retailer struggled with data silos. It had 20 disparate 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 cloud-based data strategy for greater 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 are down 40%–50%.

Enterprise leaders need to develop a different kind of operating model to realize the benefits of moving data to the cloud. They must embrace new ways of working and build a cloud culture where every employee is invested in the cloud journey.

One operating model that has proved successful for more mature enterprises is a hub-and-spoke operating model. Shared services and a center of excellence lie at the hub, and business-specific analytics use cases are addressed at the spokes. Groups of cross-functional and multidisciplinary individuals work together to activate these spokes.

Regardless of the model you use, everyone needs support from a shared services data platform team that manages the infrastructure and how to store, prepare, and serve data-driven insights. Beyond these models, every employee must understand the value the cloud delivers to them and their team. Only then will it truly be embraced across the enterprise.

Case study: Creating exceptional experiences in manufacturing

A leading enterprise started its cloud journey many years ago and had much of the necessary cloud infrastructure in place. However, employee adoption was poor. The business wanted to use the cloud to create a data-driven culture, simplify processes, generate trust in data, and empower employees to make better decisions.

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

Managing business data effectively with the cloud calls for a bespoke strategy, a robust data structure, and a comprehensive 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 truly live on the cloud and soar above the competition, even during turbulent times.