Advice and Information for Finance Professionals

The role of IT when the finance department wants to use cloud ERP

Charles Homs
VP Global Competitive Strategies

When your organization plans to use enterprise applications in the cloud, expect a lot of changes, both for end users as well as the IT department. End users will need to learn to trust new technologies like machine learning (ML). At the same time, organizations face more and more data, collected by their own internal enterprise applications, as well as data from business partners and external sources. IT plays a major role in providing that level of confidence and facilitating the monetization of data.

For end users, cloud applications will change the way they work with finance applications, and it will give them a higher level of self-sufficiency—in other words, less dependency on IT. McKinsey states that there is significantly more at stake than colorful UIs that come with cloud applications: “Full value of ongoing Next-Gen-ERP transformations can only be realized if the transition is combined with re-engineering of processes, establishing the future operating model.” (McKinsey perspective on ERP, September 2018)

When moving to cloud, you should think about how your organization uses data and how it can benefit from data. It’s an opportunity to reconsider how to redesign business processes, so that you can leverage data to the maximum. What is so different about new technology that you didn’t have in on-premise or hosted legacy applications?  How can we leverage the data that is gathered?

Oracle builds transformational technologies into its ERP applications—technologies like machine learning, digital assistants and blockchain. But an organization cannot simply introduce these new embedded transformational technologies and hope that—somehow—end users will trust the outcomes. Many will see as a black box, where something happens and somehow an outcome is presented, without explaining why or how. These end users need to be confident that the data actually makes sense and learn how to benefit from it.

So how do end users gain confidence that it all works correctly? That is where IT will have a major role.

An often-repeated refrain is that IT needs to be closer to the business. The Gartner report “How to model your cloud ERP support team” [i] makes the same argument: “By 2023, organizations that update their support team for SaaS ERP will realize 50% more business value than those that keep their on-premise model.” (Gartner, “How to Model Your Cloud ERP Support Team”, 17 December 2019)

What concrete new activities should IT take on to get closer to the business? At Oracle, we believe that IT must start to support monetization of data. According to Wikipedia, this means, “generating measurable economic benefits from available data sources and monetizing data services.” Your organization doesn’t just capture data because it can, and you won’t win in business by gathering more data than your peer group.

Data gathering must be purposeful and targeted to monetize it. IT needs to carefully plan to help the business and to facilitate data monetization. Look at data monetization to create higher returns; that’s where IT will have a significant role when companies are using cloud applications.

In the next blog, we will talk more about how finance teams can leverage data monetization. But first, IT needs to get ready to support this. Here are steps that IT needs to make in preparation of data monetization:

IT activity

Business Impact

Establish a data office

Establish the role of the Data (Governance) Officers and Analytics Officers. Define a message and strategy that focuses on data intelligence.

Examine data sources

Consider data sources like internal, external, from business partners, acquired data sources, IoT (sensor data), blockchain, streaming data and social sources. Leverage centralized, decentralized, and federated data repositories inside your company. Consider data bartering and selling data too.

Develop master data framework

Define test data and master data. Understand data assets to establish provenance and identity relationships. Consider the structure of the data, with Oracle Enterprise Data Management.

Establish data governance

Define and monitor data policies and standards—for example, quality, access and archiving. Create a risk-intelligent culture by automating security analysis and digitizing audit and compliance using the Oracle Risk Management Starter Kit.

Define monetization needs and benefits

Define how different personas will benefit from the data, how it can help them in their job, how it can be used to help their customers, and even advance their careers.

Start preprocessing

Facilitate a data management infrastructure. Develop and distribute company-specific and function-focused data products.

Define user confidence strategy

Increase business data ownership and data literacy by training users. Address both data literacy (“skills”) and data-driven culture (“wills”) among the workforce.

Analyze regulatory requirements

Include GDPR and CCPA. Don’t assume that privacy principles categorically discourage and prohibit the monetization of personal data. Use Oracle Risk Management and Compliance to reduce risk.

Create SLA with the business. Ensure data governance and risk management

Define SLAs that covers privacy, security, quality, trust, availability, retention, ethics, disaster recovery, and failover.

Develop analytics and advanced analytics strategy

Consider Oracle ERP Analytics for services like auto-discovery, classification, auto-detect sensitivity, auto-assign business terms, auto-analyze data quality, tagging, and comment annotation. Use the Oracle extensible analytics architecture to data from other sources.

ML modeling

Help users understand that machine learning is not a black box that will take over people’s jobs. It’s there to predict, recommend, classify data and discover patterns, with predictive capabilities and narratives to explain the data and graphs. All of this will make users more efficient and make better decisions based on better data.

Define data metrics and KPIs

Develop metrics that track progress toward the organizational objectives. Quantify data that will contribute to the defined goals. Establish early warnings in case the business objectives may not be met. Define metrics that are quantifiable and can drive actions. An example from retail: you can increase integration of physical and digital channels by launching a “buy online and pick up in store” service with one-hour collections.


Learn more about running finance in the cloud.

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