AWS prescriptive-guidance documentation change
Summary
Updated document title, improved example formatting in common mistakes section, minor grammatical fixes, updated image path reference, and formatting adjustments in stage descriptions
Security assessment
Changes are editorial improvements and formatting adjustments without any mention of security controls, vulnerabilities, or security-related features. The modifications focus on content clarity and documentation structure rather than security aspects.
Diff
diff --git a/prescriptive-guidance/latest/strategy-aws-data/alignment.md b/prescriptive-guidance/latest/strategy-aws-data/alignment.md index c48819f7a..a7f8fdc41 100644 --- a//prescriptive-guidance/latest/strategy-aws-data/alignment.md +++ b//prescriptive-guidance/latest/strategy-aws-data/alignment.md @@ -5 +5 @@ -[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Creating a data strategy on AWS](introduction.html) +[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Creating a data strategy on AWS that supports your company's goals](introduction.html) @@ -13 +13 @@ AWS customers tell us that a lack of alignment between data projects and their c -Common mistakes in building a data strategy include focusing too much on technical tools and trends, using edge tools, and missing the chance to accelerate business opportunities by providing business users with data that uses their own terminology, automating manual tasks for key metrics reporting, providing data quality visibility, and giving users autonomy for data exploration. +Common mistakes in building a data strategy include focusing too much on technical tools and trends, using edge tools, and missing the chance to accelerate business opportunities by facilitating data usage: for example, providing business users with data that uses their own terminology, automating manual tasks for key metrics reporting, providing data quality visibility, and giving users autonomy for data exploration. @@ -15 +15 @@ Common mistakes in building a data strategy include focusing too much on technic -Your data strategy should focus on solving your business problems, such as performing better customer segmentation to increase conversion rates, improving customer satisfaction with personalization, reducing customer churn by anticipating retention actions, testing new products and new features faster with A/B tests to improve the customer experience, and any other strategies that can improve business or branding impact. +Your data strategy should focus on solving your business problems, such as performing better customer segmentation to increase conversion rate, improving customer satisfaction with personalization, reducing customer churn by anticipating retention actions, testing new products and new features faster with A/B tests to improve the customer experience, and any other strategies that can improve business or branding impact. @@ -23 +23 @@ Moving a company from an entry stage of data usage maturity to a data-driven sta - + @@ -27 +27 @@ Moving a company from an entry stage of data usage maturity to a data-driven sta -**Stage 2 (informed by data).** In stage 2, companies use data to monitor their business health in terms of operational, financial, and departmental data that is analyzed inside each department in a siloed manner. Most enterprises that are at this stage have on-premises, proprietary systems, where sharing the data can be complex and expensive. +**Stage 2 (informed by data).** In stage 2,**** companies use data to monitor their business health in terms of operational, financial, and departmental data that is analyzed inside each department in a siloed manner. Most enterprises that are at this stage have on-premises, proprietary systems, where sharing the data can be complex and expensive. @@ -42 +42 @@ Moving stage 2 companies to AWS usually involves enabling them to extract, catal -However, although the companies in stage 3 use data extensively, they require manual data analysis to take these actions. +However, although the companies in**** stage 3 use data extensively, they require manual data analysis to take these actions. @@ -54 +54 @@ Fraud detection is another example of a two-way, data-driven action. Companies m -However, some actions aren't easily reversible and require further discussion and approval by a board of executives. These are called _one-way door_ decisions. For example, actions that involve the construction of facilities or significant money investments are usually hard to reverse. These aren't good candidates for automatic data-driven actions. +However, some actions aren't easily reversible and require further discussion and approval by a board of executives. Those are called _one-way door_ decisions. For example, actions that involve the construction of facilities or a significant money investments are usually hard to reverse. These aren't good candidates for automatic data-driven actions. @@ -56 +56 @@ However, some actions aren't easily reversible and require further discussion an -A data-driven action should be evaluated for the visibility of its impact through constant measurement. These measurements help you decide to roll back a feature or to test and engage a team for deeper analysis of distinct behavior. +A data-driven action**** should be evaluated for the**** visibility of its impact**** with constant measurements. These measurements help you follow up with decisions to roll back a feature or to test and engage a team for deeper analysis of distinct behavior.