Remove Business Analytics Remove Data Warehouse Remove Finance
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

As data volumes and use cases scale especially with AI and real-time analytics trust must be an architectural principle, not an afterthought. Comparison of modern data architectures : Architecture Definition Strengths Weaknesses Best used when Data warehouse Centralized, structured and curated data repository.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

Decide which are necessary to your business intelligence strategy. This should also include creating a plan for data storage services. Are the data sources going to remain disparate? Or does building a data warehouse make sense for your organization? Define a budget.

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3 Ways Data Engineers Can Deal with Enterprise Data Pipelines

Sisense

The right tools and platforms that are easy to deploy, play well with legacy and modern systems, and can manage a complete end-to-end BI process, are what modern data engineers need in order to fully embrace complex data. You want to make sure you have one place to bring in all your data and do your data modeling.

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5 Signs You’re Using Bad Data to Make Business Decisions

Jet Global

Your month end numbers aren’t adding up and your finance team is forced to manually sift through spreadsheet after spreadsheet looking for inaccuracies and h uman errors . Your resources are spread thin with the additional calculations and system workarounds put in place trying to understand the data your system is delivering .

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9 Distinct Threats to Your BI Implementation

Jet Global

Risk to the business. The mechanical solution is to build a data warehouse. This is because people won’t use BI applications that are founded on irrelevant, incomplete, or questionable data. That’s because nothing will highlight data quality issues like seeing them pop up in a dashboard. guess what?

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Master Your Power BI Environment with Tabular Models

Jet Global

The world of business analytics is evolving rapidly. The size and scope of business databases have grown as ERP functionality has evolved, businesses have increased their adoption of CRM and marketing automation, and collaboration networks have become more common. The first is an OLAP model.

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How Macmillan Publishers authored success using IBM Cognos Analytics

IBM Big Data Hub

Data analytics in the publishing industry With such a widespread global operation, Macmillan Publishers has a long history of investing in technology that can source deep analytical information about sales, inventory and transportation of their titles in the market.