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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

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How OLAP and AI can enable better business

IBM Big Data Hub

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. As AI techniques continue to evolve, innovative applications in the OLAP domain are anticipated.

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Understanding Data Entities in Microsoft Dynamics 365

Jet Global

Confusing matters further, Microsoft has also created something called the Data Entity Store, which serves a different purpose and functions independently of data entities. The Data Entity Store is an internal data warehouse that is only available to embedded Power BI reports (not the full version of Power BI).

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Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

First, accounting moved into the digital age and made it possible for data to be processed and summarized more efficiently. Spreadsheets enabled finance professionals to access data faster and to crunch the numbers with much greater ease. Request a free demo and take the first step to leveling-up your organization.

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Prevent Customer Churn: Customer Retention in the Transition to Microsoft D365 F&SCM

Jet Global

You might measure those costs in different ways, including actual dollars and cents, staff time, added complexity, and risk. There are numerous soft costs involving risk and potential business disruption. A non-developer can build a custom data warehouse with Jet Analytics in as little as 30 minutes.

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Business Intelligence vs. Reporting: Finding Your Bread and Butter

Jet Global

The risk of not clearly identifying and defining these: you’ll attempt to use the wrong tools for the job. Not only will this cost you mountains of wasted time, but you’re also in extreme danger of having the wrong data in front of you or giving it to someone else. A good example of this could be Cost of Goods Sold (COGs).

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Closing the breach window, from data to action

IBM Big Data Hub

The list of challenges is long: cloud attack surface sprawl, complex application environments, information overload from disparate tools, noise from false positives and low-risk events, just to name a few. You get near real-time visibility and insights from your ingested data.