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Common Business Intelligence Challenges Facing Entrepreneurs

datapine

In addition to increasing the price of deployment, setting up these data warehouses and processors also impacted expensive IT labor resources. Robust dashboards can be easily implemented, allowing potential savings and profits to be quickly highlighted with simple slicing and dicing of the data.

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Blending Art and Science: Using Data to Forecast and Manage Your Sales Pipeline

Sisense

Nowadays, sales is both science and art. Best practice blends the application of advanced data models with the experience, intuition and knowledge of sales management, to deeply understand the sales pipeline. Why sales and analysts should work together. Why sales and analysts should work together.

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13 power tips for Microsoft Power BI

CIO Business Intelligence

If you use Xero for accounting, or K2 Cloud to build business processes, or Adobe Marketing Cloud, SAP HANA, Salesforce, MailChimp, Marketo, or Google Analytics, you can use Power BI to visualize the data you have in those services, perform calculations, create reports, and bring them together in a custom dashboard.

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How Newcomp Analytics partners with IBM to advance clients’ supply chain insights

IBM Big Data Hub

Lindt has used Cognos Analytics for more than 20 years as an analytics solution for its sales and marketing functions. Left to their own devices, they had resorted to using legacy reporting tools such as Excel that required manual gathering, slicing and dicing of data.

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Business Intelligence vs. Reporting: Finding Your Bread and Butter

Jet Global

Reports tend to narrowly focus on a specific operation or dataset for a period (monthly sales, daily customer orders, weekly open AP, etc.). In addition, reporting typically draws and refreshes data in real-time from the live production database. First, you should never perform analysis for large volumes of data.

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The Data Journey: From Raw Data to Insights

Sisense

Generating and storing data in its raw state. Every organization generates and gathers data, both internally and from external sources. The data takes many formats and covers all areas of the organization’s business (sales, marketing, payroll, production, logistics, etc.) Data modeling: Create relationships between data.

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What AI Means to a Retailer Dedicated to Customer Experience

Birst BI

When the data sets are large, with numerous attributes, users spend a lot of time slicing and dicing for newer insights or apply their original hypotheses to a subset of data. Figure 1: Specialty’s Café and Bakery — Catering Sales Dashboard using Birst Networked BI and Analytics Platform.