Updated October 2023

The modern CFO isn't just about leases, ledgers, and line items. No, today's finance organizations have bigger fish to fry, focusing on the data analytics driving those bastions of accounting and finance, not to mention chasing every CFO's holy grail – predictive, far-reaching business intelligence.

But with complex operations, mountains of spreadsheets, and teams scattered to the four winds, generating that sweet, sweet business intelligence is easier said than done. At least for companies still sitting on the cloud analytics sidelines. Because, as we're about to discuss, cloud-based analytics tools are the secure, convenient, agile solutions every bustling office of the CFO needs to drive better decision-making.

On that note, we’re going to show you how your organization can successfully navigate even the bumpiest of operational roads by diving into critical topics like:

• What is Cloud Analytics?
• The Benefits of Cloud Analytics for Finance
• Overcoming Common Concerns and Misconceptions
• Managing Massive Data Volumes with Cloud Analytics
• Cloud Analytics Use Cases for CFOs
• Choosing the Right Platform & Provider
• Tools of the Trade: Get to Know Cloud Analytics Tools
• Implementing a Cloud Analytics Solution

So let’s jump right in, shall we?

What is Cloud Analytics?

Cloud analytics refers to the process of analyzing vast amounts of data to uncover actionable insights, patterns, and trends. Data is stored in the cloud – a network of remote servers hosted on the internet – which store, manage, and process large datasets.

Through the trifecta of cloud-based data storage, advanced analytics tools, and machine learning algorithms, you gain the power to make better and more informed decisions faster. Obviously, such instant insights and streamlined decision-making processes can make cloud analytics an indispensable tool for financial management. A veritable game-changer, if you will.

The Benefits of Cloud Analytics for Finance

But what does all of that really mean for a modern CFO in a practical sense? Well, for starters, cloud analytics offers numerous advantages that traditional data analysis can't touch. By combining unparalleled convenience and immediacy with the wonders of big data, machine learning, and hyper-nuanced insights, finance organizations can use cloud analytics to make better-informed decisions that drive growth and profitability. At the highest level, cloud analytics benefits CFOs by providing:

Processing Power

Offering unparalleled computing power, cloud analytics allows CFOs to crunch massive volumes of data. For example, cloud analytics is the secret behind Stripe's ability to process billions of daily payment transactions. At the same time, the service provides instant insights and streamlines financial decision-making for thousands of businesses, all made possible by cloud computing and analytics.

Scalability

Cloud analytics platforms can quickly adapt to the growing needs of an organization. As your organization expands, takes on more clients or customers, and begins dealing with vast datasets and transactions, you can scale cloud resources up or down to accommodate them without needing expensive infrastructure upgrades or additional in-house IT support.

Access from Anywhere and on Any Device

With cloud analytics, your teams can access critical financial data and insights from anywhere in the world. All you need is a device with an internet connection. So, the next time you find yourself at the airport and your board of directors suddenly wants to know last quarter's objectives and key results, cloud platforms and apps make reporting and delivering on-demand information a breeze. 

Managed Services

Cloud analytics service providers handle the maintenance and security, freeing up your valuable time and resources. You don't need to be tech-savvy, your organization doesn't need its own computing and storage servers, and you never need to worry about your system being down or losing your data. You can focus on your balance sheets and cash flow reports instead of the health of your cloud infrastructure and analytics capabilities.

Real-Time Data

Old, stale data is about as useful as a gallon of expired milk. According to research from Workday, only 44% of finance leaders are confident in the integrity and usability of their data. Thankfully, cloud analytics can provide accurate, timely insights high-performing CFOs need for more accurate decision-making. So, whether you need to reconcile actual results with forecasted data or monitor live transactions to check for credit card fraud, cloud analytics has all the real-time insights you need. And it can accomplish all of this through a convenient, intuitive user experience – like a data dashboard on a mobile device, for instance.

Cost

Like any new system or tool, setting up cloud analytics involves upfront costs. However, the ROI for cloud analytics far outweighs the long-term alternative of owning, maintaining, replacing, and running an in-house IT infrastructure with the hardware, software, and personnel team to run similar analytics.  

Other Benefits

Honestly, we could go on and on about the benefits of cloud analytics for CFOs and their teams without getting hyperbolic for a single second. But there’s a lot to cover and you have an organization to help run and grow. So, aside from the benefits we just listed, businesses can also look forward to:

• Faster processing times thanks to powerful servers provided by cloud services like Amazon Web Services (AWS).
• Better collaboration between teams through seamless data-sharing capabilities
• Data security measures tailored specifically for highly regulated industries.
• A single source of truth rather than playing version control bingo, resulting in trustworthy data points that are consistent, accurate, and fuel faster decision-making without adding to workloads or your headcount.

Simply put, cloud analytics is a win-win for any organization willing to embrace something a step outside of its traditional comfort zone, providing unlimited upside if you're thoughtful and deliberate about it.

Overcoming Common Concerns and Misconceptions

Granted, moving your organization's financial analytics operations into the cloud might seem daunting at first. However, a wee bit of planning and understanding of available options – along with an experienced implementation partner – can address common misconceptions and challenges, including:

1. Data Security: The best public cloud providers have robust security measures in place, designed specifically for heavily-regulated industries. For example, the financial services industry can whole-heartedly rely on reputable public cloud providers that prioritize stringent security measures when storing sensitive information like clients’ personal data.
2. Data Storage Costs: In many cases, keeping massive datasets on external servers can be more economical than having physical, on-premises equipment. While there is an upfront cost to setting up cloud analytics, the long-term ROI far outweighs the alternative of owning and maintaining expensive data centers. According to Gartner, the majority of global IT expenditure will be allocated to public cloud services by 2025.
3. Adoption Challenges: Once again, many cloud analytics software platforms are user-friendly and designed for non-technical users, making the transition smoother than you might think.

In short, embracing cloud analytics in finance can lead to valuable insights that drive better business decisions while reducing costs and increasing efficiency. The key is finding the right platform and provider that align with your organization's unique needs.

Key Takeaway:

CFOs are embracing cloud analytics as it provides instant insights, faster decision-making processes, and better collaboration. Cloud analytics offers data accessibility from anywhere with an internet connection, along with innovative technologies like machine learning algorithms to discover trends and valuable insights that might otherwise go unnoticed. In addition, reputable public cloud providers prioritize stringent security measures when storing sensitive information such as large enterprises' data sets, all with a long-term ROI that typically far outweighs owning and maintaining expensive data centers.

Managing Massive Data Volumes with Cloud Analytics

As we mention in our guide, The Business Intelligence Revolution: The Road to Data-Driven Decision-Making, companies have data coming out of their ears. It's a deluge. An avalanche of operational information that, without the right tools to collect, analyze, and report on that information, it's almost impossible for leadership to see the actionable forest through the business data trees.

In fact, by 2025, the world will generate a staggering 463 exabytes of data every day. To state the obvious, that's a whole lot of information to sort and process. But that's why robust infrastructures provided by cloud-based platforms are such an operational coup for CFOs, allowing their teams to efficiently process and report massive amounts of information, along with other critical benefits.

Flexibility without On-Prem Hassles

The sheer volume of financial data generated daily can be overwhelming for traditional, on-prem storage systems. But once again, cloud analytics tools provide virtually limitless scalability that grows with your business needs. This means no more worrying about running out of space or investing in expensive hardware upgrades as your organization expands its operations.

Further, companies can eliminate the need for physical data centers and their associated costs by relying on public cloud service providers who maintain powerful servers. That's while maintaining the security and collaboration we already discussed.

Harnessing Advanced Data Processing Techniques

Cloud analytics isn't just about storage and scalability, though. From machine learning algorithms that help identify patterns within complex sales analytics to performance analytics tools that aid CFOs in making informed decisions, there's a wealth of resources and benefits at your fingertips.

1. Data Visualization: Transform raw financial data into visually appealing charts and graphs, enabling you to quickly identify trends and make informed business decisions.
2. Data Silos: Break down barriers between departments by integrating disparate data sources into a single, unified platform for improved efficiency and collaboration.
3. Agile decision-making: Stay ahead of the curve with instant access to up-to-date information that empowers your finance team to react swiftly to changing market conditions or emerging opportunities.

Tapping into the power of cloud analytics can revolutionize your organization's approach to managing massive volumes of financial data. Leveraging scalable storage solutions and advanced processing techniques will let you efficiently handle even the most daunting datasets while driving innovation across various business functions.

Key Takeaway:

Cloud analytics is a game-changer for finance departments dealing with massive data volumes. Cloud storage solutions offer scalability advantages, eliminate the need for physical data centers, and provide advanced data processing techniques such as machine learning algorithms and real-time insights that can help CFOs make informed decisions.

Cloud Analytics Use Cases for CFOs

We won't belabor the use cases for cloud analytics since they're so abundant. And that's a massive understatement. However, at a high-level, CFOs can use cloud analytics to drive:

Financial Forecasting and Budgeting

Access real-time data, historical trends, and use predictive data modeling to create more accurate financial forecasts and budgets – particularly when it comes to objectives and key results (OKR) reporting and revenue forecasting. With these tools, you supercharge your ability to allocate resources more effectively, identify potential financial challenges, and capitalize on emerging opportunities. Put another way, cloud computing can turn an FP&A function into Zeppelin-level rockstars.

Revenue and Expense Analysis

Cloud analytics lets you perform in-depth analysis of revenues and expenses, making cash and cash flow reports easier and more accurate. Your organization can then use this information to optimize pricing strategies, improve cost management, and drive profitability.

Risk Management and Compliance

By identifying potential vulnerabilities and staying informed of regulatory changes, cloud-analytic-based risk reports help you implement proactive strategies to protect sensitive data, mitigate risks, and ensure compliance with relevant regulations.

Performance Management and KPI Tracking

Cloud analytics makes it easy for you to track and analyze different metrics and key performance indicators (KPIs) across the organization, particularly for internal productivity reports. As a result, you can identify areas for improvement, allocate resources more effectively, and better drive accountability throughout your organization, the proverbial rising tide that lifts all operational boats.

Key Takeaway:

Cloud computing and analytics can redefine your finance organization's relationship with operational data. But the relevant use cases aren't just limited to finance and accounting. Although we recommend beginning in the finance organization, some quick wins will soon have folks from marketing, HR, and other key departments wanting to hop on the cloud analytics train. And if one of your primary goals is to make the CFO position a crucial business partner to the entire enterprise, then cloud computing is an investment that will pay off in several different ways.

Choosing the Right Platform & Provider

Transitioning your financial team to new cloud-based systems may seem overwhelming at first. But like anything else, a little – or a lot – of due diligence can go a long way in ensuring you choose the right tools for your needs. So, aside from partnering with third-party specialists to help you sort through, choose, and implement the best solutions for your organization and team, understanding what's available will help you make an informed decision.

Comparing features across different providers

Begin by evaluating the different characteristics provided by the various cloud analytics solutions. Consider aspects like data storage capacity, processing power, and ease of integration with existing systems. Then, evaluate the capability of each platform to manage massive cloud data sets and offer immediate analysis for making better business choices.

Assessing security measures offered by each platform

Data security is crucial in highly regulated industries. Therefore, be sure your chosen provider has robust measures in place to protect sensitive information from breaches or unauthorized access. Additionally, look for certifications such as ISO 27001, adherence to industry-specific regulations like GDPR, and encryption standards used during data transmission and storage.

Tips for choosing the right cloud analytics provider:

• Evaluate their experience working with similar-sized organizations or those within your industry.
• Determine if they offer customizable solutions tailored specifically toward meeting your unique requirements.
• Analyze customer reviews and testimonials along with case studies showcasing successful implementations previously carried out.
• Consider the level of support provided during the implementation and post-deployment phases, including training resources and ongoing assistance.

Key Takeaway:

Remember, adopting cloud analytics is a long-term investment. Taking the time to thoroughly evaluate potential providers will ensure you select one that aligns with your organization's goals while still offering valuable insights for driving growth and innovation in today's data-driven world. Once again, this is an area where partnering with the right experts can be your data-driven ace-in-the-hole.

Tools of the Trade: Get to Know Cloud Analytics Tools

We'll be the first to admit there's a learning curve involved in moving to cloud-based computing. But that isn't anything you aren't already used to, right? Every new system you've ever implemented for your organization has involved a learning curve, some more severe than others.

In the case of cloud analytics, the learning curve isn't necessarily steep but can be expansive depending on your current state and how much it differs from your objectives with cloud analytics. So, since you have to walk before you run, let's look at some of the key terms and concepts you might run into as you embrace your new cloud-based future.

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) Tools

ETL and ELT tools form the foundation of an effective cloud analytics solution by enabling seamless data transfer and integration. These tools help organizations consolidate data from disparate systems into cloud-based data storage or processing platforms, often using automation to lead the way.

ETL (Extract, Transform, Load): This process involves extracting data from multiple sources, transforming it into a standard format, and loading it into a data warehouse or other target systems. ETL tools are generally used to move and prepare data for analytics and reporting purposes.

ELT (Extract, Load, Transform): With ELT, the data is first loaded into a data lake or another target system, then transformed within the destination itself. This approach is more suitable when dealing with vast volumes of unstructured data, which can be subjected to transformations and analytics in a distributed computing environment like Hadoop.

For reference, examples of popular ETL and ELT tools include Azure Data Factory, Alteryx, Talend, and Apache NiFi.

Data Storage

Data lakes, warehouses, and lakehouses provide the storage and consolidation platform for all the data collected and processed by an organization. These repositories help centralize and organize data, making it easier to perform analytics.

We’ve taken a deep dive into the intricacies of each in the past, extolling the virtues of the data lakehouse as an incomparable pillar for your enterprise’s data storage and environment. But just to be thorough: 

Data Lakes: Data lakes are storage platforms designed to hold vast amounts of raw, unprocessed data in its native format, allowing users to store and access data without worrying about structure or schema. They are highly scalable and capable of handling both structured and unstructured data. Popular data lake solutions include Hadoop, Amazon S3, and Azure Data Lake Storage.

Data Warehouses: Data warehouses store large volumes of structured data, specifically designed for fast and efficient querying and analytics. They usually follow a schema-on-write approach where data is structured and modeled before being loaded into the warehouse. Some popular data warehouse solutions include Snowflake, Google BigQuery, and Amazon Redshift.

Data Lakehouses: This is the best of all worlds, a hybrid of warehouses and lakes from Dataricks that flexes to changes in data structures while also tracking different iterations of your data. Lakehouses use a structure like a warehouse, only for both structured and unstructured data, driving far more efficient reporting or anything else you want to throw at them.

Processing Layers for Analytics, Machine Learning, and AI

Processing applications are crucial in handling and processing data, performing analytics, and employing machine learning and artificial intelligence tools to gain insights. This layer may include tools and platforms for data processing, analytics, machine learning, and AI, such as Apache Spark, AWS SageMaker, Google AI Platform, and TensorFlow.

Application Layers

The application layer refers to the software platforms and services that interact with the underlying data. These applications not only create and manage the data, but also allow users to access and analyze the information. Examples of application layer tools include NetSuite, Salesforce, Tableau, and Power BI, amongst others. These applications can be standalone solutions or integrated with other cloud analytics components, providing users with a seamless experience while working with data stored in the cloud.

Implementing a Cloud Analytics Solution

Building on our insights thus far, let's look at what an implementation might look like, at least at a high level. As you know, the successful adoption of any new technology relies on careful planning and execution – cloud analytics being no exception.

Although every enterprise is different and, thus, will have different needs during the implementation phase, most businesses will find success by following a general series of steps.

1. Assess Your Organization's Current State

Begin by evaluating your organization's current state – existing data analytics tools, processes, infrastructure, and so forth. Next, identify any gaps, limitations, and pain points cloud analytics can help resolve. Similarly, determine the key financial challenges and opportunities cloud analytics can address within your organization. Examples include improving financial forecasting, optimizing cost management, or enhancing risk mitigation and compliance. This comprehensive assessment will serve as a baseline for your cloud analytics journey.

2. Evaluate and Select the Right Cloud Analytics Provider

Research various cloud analytics platforms and providers, considering the factors that matter most to you – cost, scalability, ease of use, security, and compatibility with existing ERP, CRM, and other critical systems being the most common. Next, consult with your IT team and involve key stakeholders in the decision-making process to ensure buy-in and alignment.

3. Develop a Plan for Implementation and Change Management

Create a detailed implementation plan, outlining the necessary steps, timelines, and resources required for a successful transition to cloud analytics. As you go, remember that change isn’t always easy, so prepare to address potential barriers. Develop strategies to make the change as seamless as possible for all affected employees, utilizing either or both in-house or outside project and change management specialists. Also, establish clear goals and KPIs to measure your progress and success.

4. Train Your Finance Team on the New Cloud Analytics Tools

Moving to cloud computing isn't a set-it-and-forget-it solution. It's going to take time and effort to ensure your team is equipped with the necessary skills and knowledge to utilize the new cloud analytics platform effectively. To that point, as part of your change management, provide comprehensive training, create user guides, and offer ongoing support to facilitate a smooth transition. Find out whether your cloud analytics provider offers training or resources to help with the transition.

5. Continuously Monitor and Optimize Your Cloud Analytics Usage

Once your cloud analytics platform is up and running, regularly monitor its usage and effectiveness. Gather user feedback, track progress towards established KPIs, and identify areas for improvement. Continuously optimizing your cloud analytics processes and tools will ensure you maximize ROI and their impact on your organization's financial performance and decision-making.

A Final Word from Embark

Has this sounded like a massive paid ad for cloud analytics? Yep. But it’s for good reason. While we certainly remain product and provider-agnostic, the underlying technologies are indisputable wins for CFOs and their teams. 

Ultimately, cloud analytics can completely transform decision-making through data-driven insights, starting with the finance organization but spreading across your enterprise as you demonstrate its abilities and potential. Our job at Embark is to ensure you get from your current state to an ideal future state as quickly and smoothly as possible. And to be honest, we’re darn good at our job. So let’s sit down and see what cloud analytics can do for your organization.