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Blanchardì$d1925 215 $aXIII, 591 p.$cill.$d24 cm 610 0 $aElettrotecnica 676 $a621.3 700 1$aCurchod,$bAdrien$0554 702 1$aMauduit,$bA. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000066890403321 952 $a13 L 13 11$b5812$fFINBC 952 $a223-G-8$b2365$fMA1 959 $aFINBC 959 $aMA1 996 $aProblémes d'électrotechnique avec solutions dévelopées et applications numériques$9111115 997 $aUNINA LEADER 09780nam 22006613 450 001 9911008443803321 005 20221204090255.0 010 $a9781803232355 010 $a1803232358 035 $a(MiAaPQ)EBC30279148 035 $a(Au-PeEL)EBL30279148 035 $a(CKB)25509865600041 035 $a(OCoLC)1352967644 035 $a(OCoLC-P)1352967644 035 $a(PPN)267235690 035 $a(FR-PaCSA)88938070 035 $a(CaSebORM)9781803246758 035 $a(DE-B1597)691772 035 $a(DE-B1597)9781803232355 035 $a(FRCYB88938070)88938070 035 $a(EXLCZ)9925509865600041 100 $a20221204d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Literacy in Practice $eA Complete Guide to Data Literacy and Making Smarter Decisions with Data Through Intelligent Actions 205 $a1st ed. 210 1$aBirmingham :$cPackt Publishing, Limited,$d2022. 210 4$d©2022. 215 $a1 online resource (396 pages) 300 $aDescription based upon print version of record. 300 $aApplying Goodhart's law to KPIs 311 08$a9781803246758 311 08$a1803246758 327 $aCover -- Copyright -- Contributors -- Table of Contents -- Preface -- Part 1: Understanding the Data Literacy Conceptss -- Chapter 1: The Beginning - The Flow of Data -- Understanding data in our daily lives -- Analyzing data -- Searching and finding information -- An introduction to data literacy -- The COVID-19 pandemic -- The organizational data flow -- The DIDM journey -- The success story of The Oakland A's -- Summary -- Chapter 2: Unfolding Your Data Journey -- Growing toward data and analytics maturity -- Descriptive analyses and the data path to maturity -- Understanding descriptive analysis -- Identifying qualitative or quantitative data -- Understanding diagnostic analysis -- Understanding predictive analytics -- Understanding prescriptive analytics -- AI -- Can data save lives? A success story -- Summary -- Chapter 3: Understanding the Four-Pillar Model -- Gaining an understanding of the various aspects of data literacy -- Introducing the four fundamental pillars -- Becoming acquainted with organizational data literacy -- Discussing the significance of data management -- Defining a data and analytics approach -- The rapid growth of our data world -- Tools -- The rise of ML and AI -- Moving to the cloud -- Data literacy is a key aspect of data and analytics -- Understanding the education pillar -- Mixing the pillars -- Summary -- Chapter 4: Implementing Organizational Data Literacy -- Implementing organizational data literacy -- Planning the data literacy vision -- Communicating the data literacy vision -- Focusing on desired outcomes -- Adopting a systemic perspective -- Getting everyone involved in the whole process -- Developing a data-literate culture -- Managing change -- Driving resilience -- Managing the organization's skills and knowledge -- Creating a data literacy educational program -- Identifying employee roles. 327 $aLearning levels -- Covering all moments of need -- Learning methodologies -- Including all knowledge types -- Learning elements -- Organizing content -- Searching for content -- Measuring success -- Celebrating successes -- Summary -- Further reading -- Chapter 5: Managing Your Data Environment -- Introducing data management -- Understanding your data quality -- Intermezzo - Starting to improve data quality in a small-scaled healthcare environment -- Delivering a data management future -- Data strategy -- Taking care of your data strategy -- Creating a data vision -- Identifying your data -- Discovering where your data is stored -- Retrieving your data -- Combining and enriching data -- Setting the standard -- Processes -- Control -- IT -- Summary -- Part 2: Understanding How to Measure the Why, What, and How -- Chapter 6: Aligning with Organizational Goals -- Understanding the types of indicators -- Identifying KPIs -- Characteristics of KPIs -- Leading and lagging indicators -- Reviewing for unintended consequences -- Applying Goodhart's law to KPIs -- Defining what to track -- Activity system maps -- Logic models -- Summary -- References -- Chapter 7: Designing Dashboards and Reports -- The importance of visualizing data -- Deceiving with bad visualizations -- Using our eyes and the usage of colors -- Introducing the DAR(S) principle -- Defining your dashboard -- Choosing the right visualization -- Understanding some basic visualizations -- Bar chart (or column chart or bar graph) -- Line chart -- Pie chart -- Heatmap -- Radar chart -- Geospatial charts -- KPIs in various ways -- Tables -- Presenting some advanced visualizations -- Bullet charts -- Addressing contextual analysis -- Summary -- Chapter 8: Questioning the Data -- Being curious and critical by asking questions -- Starting with the problem - not the data. 327 $aIdentifying the right key performance indicators (KPIs) ahead of time -- Questioning not just the data, but also assumptions -- Using a questioning framework -- Questioning based on the decision-making stage -- Questioning data and information -- Questioning analytic interpretations and insights -- Summary -- References -- Chapter 9: Handling Data Responsibly -- Introducing the potential risks of data and analytics -- Identifying data security concerns -- Intermezzo - a data leak at an airplane carrier -- Identifying data privacy concerns -- Identifying data ethical concerns -- Intermezzo - tax office profiles ethnically -- Summary -- Part 3: Understanding the Change and How to Assess Activities -- Chapter 10: Turning Insights into Decisions -- Data-informed decision-making process -- Ask - Identifying problems and interpreting requirements -- Acquire - Understanding, acquiring, and preparing relevant data -- Analyze - Transforming data into insights -- Apply - Validating the insights -- Act - Transforming insights into decisions -- Announce - Communicating decisions with data -- Assess - Evaluating outcomes of a decision -- Making a data-Informed decision in action -- Using a data-informed decision checklist -- Why data-informed over data-driven? -- Storytelling -- Why is communicating with data so hard? -- Three key elements of communication -- Why include a narrative? -- The process -- Summary -- Further reading -- Chapter 11: Defining a Data Literacy Competency Framework -- Data literacy competency framework -- Identifying problems and interpreting requirements -- Understanding, acquiring, and preparing relevant data -- Turning data into insights -- Validating the insights -- Transforming insights into decisions -- Communicating decisions with data -- Evaluating the outcome of a decision -- Understanding data -- Data literacy skills. 327 $aIdentifying data literacy technical skills -- Data literacy soft skills -- Data literacy mindsets -- Summary -- References -- Chapter 12: Assessing Your Data Literacy Maturity -- Assessing individual data literacy -- Assessing organizational data literacy -- Basic organizational data literacy assessment -- Robust organizational data literacy maturity assessment -- Summary -- Chapter 13: Managing Data and Analytics Projects -- Discovering why data and analytics projects fail -- Understanding four typical data and analytics project characteristics -- Understanding data and analytics project blockers -- Pitfalls in data and analytics projects -- Lack of expertise -- The technical architecture -- Time and money -- Unfolding the data and analytics project approach -- Unfolding the data and analytics project framework -- Intermezzo 2 - successfully managing a data and analytics project -- Mitigating typical data and analytics project risks -- Project risks -- Technical risks -- Cultural risks -- Content risks -- Determining roles in data and analytics projects (and teams) -- Managing data and analytics projects -- Writing a successful data and analytics business case -- A chapter layout for your business case -- Finding financial justification for your project -- Argumentation for one-time project costs -- Annual recurring costs -- Argumentation for annual recurring costs -- The quantitative benefits -- ROI -- Conclusion and advice -- Summary -- Chapter 14: Appendix A - Templates -- Project intake form -- STARR TEMPLATE -- Layout for a business case -- Layout for a business case scenario description -- A business case financial analysis -- Layout for a risk assessment -- Layout for a summary business case -- Layout information and measure plan -- Layout for a KPI description -- Table with the Inmon groups and a description of their roles. 327 $aChapter 15: Appendix B - References -- Inspirational books -- Online articles and blogs -- Dutch articles and blogs -- Online tools -- Online sites -- Index -- Other Books You May Enjoy. 330 $aUnderstanding data is a powerful skill. This book will help you build a sound understanding of data literacy basics and build your confidence to work with data every day. Guided by best practices and real-world examples, you'll master the skills to make smarter decisions with data, fast. 606 $aData mining 606 $aInformation literacy 606 $aDecision making 615 0$aData mining. 615 0$aInformation literacy. 615 0$aDecision making. 676 $a006.312 700 $aKlidas$b Angelika$01828087 701 $aHanegan$b Kevin$01828088 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911008443803321 996 $aData Literacy in Practice$94396188 997 $aUNINA