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Omniscience vs clarity: finding balance in data analytics

“I want to see everything,” is probably the most-often heard request in data analytics and business reporting. Powerful business tools like SAP Data Warehouse Cloud and SAP Analytics Cloud bring us closer to attaining that superpower. The question, however, is whether that should really be our goal, or if there’s a way we can balance hunger for knowledge with optimal performance and clarity.
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Best Practices in ERP

Implementing enterprise resource planning (ERP) in your business is essential, regardless of its size. It not only assists your business in streamlining diverse processes, but also serve as a single source of truth which provides the best report, improved decisions, and greater collaboration overall within your business.

These best practices cover all the stages of ERP implementation, from defining requirements to training and providing support. Think of it as a checklist that can help you achieve your business objectives, minimise the risk of project delays and budget overruns, and enable employees to use the system effectively.

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Connected planning: breaking down the silos

“How can we link our financial plan to supply chain planning?”, “Can you help us make planning more agile?” or “How can we get access to real-time data from across the enterprise?”. These must be the most common questions our finance experts receive these days. It’s crystal clear: as a CFO,  you need more data and forecasts, as well as the ability to change course quickly. Excel no longer do, but what’re the alternatives?
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Engage your business in your data journey with a data catalogue

To become data-driven, organisations need to establish a rock-solid data culture where the value of data is recognised and takes ownership. One of the keys to supporting and enabling this dynamic in the long run is data governance: clear agreements about the way data is organised and managed throughout the organisation. Recently, data governance tools, like the business glossary & data catalogues have proven indispensable parts of a data culture. Here’s why.
 
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