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Are the key insights you need hidden in spreadsheets?

Bertrand Matthelie
Senior Principal Product Marketing Director
94% of business professionals say data analytics is important to their business growth and digital transformation. Business teams are eager to access ever more data to quickly answer new questions arising every day. However, analysts often struggle to efficiently turn a growing mountain of data into insights. Did you know that CFOs spend on average 2.24 hours/day sifting through spreadsheets? While business teams usually turn to IT for help, IT teams tend to have a hard time keeping up with business users’ demands for increased access to more disparate data sources, larger volumes of data, and faster query times. Only 3% of employees can get data in seconds to make business decisions.

Difficulties with the Typical Data Analytics Process

The typical data analytics process to address business demands has multiple problems:

  • It is complex and slow. It’s also unsecure with various data security risks at the different stages
  • Business analysts are frustrated since despite long hours trying to turn data into insights, a number of stakeholders do not trust their data and predications, and keep making decisions based on gut feelings
  • IT is equally frustrated, being frequently perceived as a bottleneck and/or not sufficiently helping. Plus, they often need to go through the unproductive exercise of providing data extracts or reports to business users for each new request. Setting up data marts can be a concern for IT teams already stretched very thin as they then need to manage those on ongoing basis, and to ensure data security. The result is often a compromise that is far from ideal

The good news? Oracle can help.

The Typical Data Analytics Process

Consider Lisa, a business analyst in finance who, like 85% of her peers, is asked to transform from reporting on “what” to “why”, and to deliver forward-looking, predictive insights to guide business strategy and improve day-to-day decision-making. To analyze new data from different sources, she usually turns to Ben in IT, and the process typically goes as follows:

  • Ben provides data extracts or reports to Lisa, which may take days and require some back and forth.
  • Lisa manually prepares the data, a laborious process.
  • The team of analysts relies on spreadsheets to share and collaborate on the data. Unsurprisingly, this often results in human errors, confusion, complex reconciliations and multiple sources of truth – the so called “spreadsheet nightmare”…
  • The actual analysis tends to be mostly reporting, as opposed to interactive discovery using machine learning capabilities, making it harder to surface unexpected insights.
  • During management reviews, the data lineage (e.g. data provenance, what data sets were combined, what calculations were used) is unclear, which creates a lack of trust in the data and the predictions. Additionally, iterations to address further demands are slow since they require going through the entire process again.
  • Results are shared with stakeholders via spreadsheets or slides.

Figure 1: Common data and analytics process

How to Streamline the Data Analytics Process

The good news for both Lisa and Ben is that Oracle Departmental Data Warehouse is a complete solution enabling business teams to get the deep, trustworthy, data-driven insights they need to make quick decisions. Governed and secure, the solution reduces risks and complexity while increasing both IT and analysts’ productivity. The time to go from data to decisions can be dramatically compressed. Here’s how different life can be for Lisa and Ben:


Figure 2: Data-driven business agility

  • Automated Management: With Oracle Autonomous Data Warehouse, Ben can provision a new departmental data warehouse for Lisa in a few minutes only. As matter of fact, he can also let Lisa set up data warehouse instances herself in self-service mode, using a governed, secure cloud solution. Autonomous Data Warehouse intelligently automates data warehouse management, so Ben does not have to spend time doing it manually.

  • Live data: Lisa and her team no longer need to go to Ben for periodic extracts. They can add data themselves, for example uploading spreadsheets, and once the connections to their desired data sources are established, they can get live data whenever needed.

  • Secure access: Data is available only to authorized users, via a shareable and secure workspace allowing secure collaboration within a workgroup.

  • Smart data preparation: Lisa can leverage machine learning recommendations to enrich datasets with a single click.

  • Single source of truth: Analysts no longer need to use spreadsheets to share data and collaborate; they can rely on a single data source, which means a single source of truth. Most importantly, all stakeholders trust the data as they have clear visibility on data lineage.

  • Fast insights: When analyzing the data, Lisa can leverage the interactive self-service discovery capabilities of Oracle Analytics Cloud powered by machine learning. She doesn’t start with a blank canvas but with auto-created visuals based on her data. She can ask the system to explain it for her automatically, which gives her great insights and answers to questions she perhaps didn’t even think of asking.

  • Accelerated business decisions: Lisa can securely present results to executives with visual stories and natural language generation, and she can iterate very quickly to address any further demand.

This agile approach presents multiple benefits for both IT and business teams:




New projects started in minutes

Governed and secure solution

Single source of truth

Simple and rapid implementation

ML-powered self-service analytics

Automated management

Deep data-driven insights fast

Reduced complexity and cost

Consistent high performance

More time spent on business needs


Oracle’s all-in-one solution can rapidly be deployed via Terraform. No need to integrate various tools from different providers. Additionally, IT teams can rely on a simple, reliable, and repeatable approach for all data analytics requests from business departments, greatly improving productivity and data governance. With automated management, there is no more compromise between self-service analytics, governance and security!


Bottom line: Lisa can figure out the “why” beyond the “what” and rapidly deliver insights to help guide business strategy. Data can be trusted, and fact-based decisions rapidly taken. And Ben, well he is now on Lisa’s Christmas cards list... and viewed by business leaders as delivering an agile, responsive, and resilient infrastructure that can support fast-moving business requirements.



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