Remove Business Objectives Remove Data Collection Remove Data Quality Remove Measurement
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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

The primary goal of any data governance program is to deliver against prioritized business objectives and unlock the value of your data across your organization. Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes.

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Bringing an AI Product to Market

O'Reilly on Data

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. This is aligned to the five pillars we discuss in this post.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

A business intelligence strategy refers to the process of implementing a BI system in your company. This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. Clean data in, clean analytics out. It’s that simple.

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Building a Data Governance Strategy in 7 Steps

Alation

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. A data governance strategy provides a framework that connects people to processes and technology.

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What Is Data Intelligence?

Alation

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. Data lineage features.