# (pound sign), 200

[ ] (square brackets), 103

{ } (curly braces), 103

“ (quotation marks), 102

1NF (first normal form), 170

2NF (second normal form), 170

3NF (third normal form), 170–171

4NF (fourth normal form), 170

AAC (Advanced Audio Coding), 77

Abcya, 355

absolute measures of dispersion, 231

acceptable use policies (AUPs), 389–390

access control lists (ACLs), 378

access points (APs), 378

access requirements, 370–374

• ACLs (access control lists), 378

• DUAs (data use agreements), 373–374

• MDM (master data management), 455

• MDM (master data management) for, 456–457

• RBAC (role-based access control), 331

• release approvals, 374

• role-based access control, 372

• user group-based access control, 373

accessibility of data, 417

accuracy, 415

ACID design principle, 25–26

ACLs (access control lists), 378

acquisition, data, 127–135, 419

• APIs (application programming interfaces), 121–125

• data collection methods, 127–135

  • observation, 133–134

  • public databases, 129

  • sampling, 132–133

  • surveys, 129–132

  • web scraping, 128–129

• data integration, 114

• data monetization, 114

• delta load, 121

• ELT (extract, load, and transform) process, 117–119, 120

• ETL (extract, transform, and load) process, 115–117, 120

• improving data quality in, 419

• observation, 133–134

• overview of, 113–114

• public databases, 129

• sampling, 132–133

• sources, 258, 260–261

• surveys, 129–132

• web scraping, 128–129

• web services, 121–125

active data dictionaries, 452

ad hoc reports, 362–363

Adaptive Transform Acoustic Coding (ATRAC), 79

administrative teams, reports for, 294

Advanced Audio Coding (AAC), 77

Advanced Encryption Standard (AES), 377

Advanced Systems Format (ASF), 80

AES (Advanced Encryption Standard), 377

aggregate functions, 177–179

aggregation, data, 167–168

AI (artificial intelligence), 390

AIFF (Audio Interchange File Format), 76

algorithms, encryption, 377, 378

alphanumeric data type, 63

alternative hypotheses, 246

ALUs (arithmetic logical units), 185–186

Amazon Web Services. See AWS (Amazon Web Services)

analogous color schemes, 308

analog-to-digital audio conversion, 73–75

analysis. See data analysis techniques; data analytics tools

Analysis Toolpak (Microsoft Excel), 212

AND function, 175–176

Apex, 220

APIs (application programming interfaces), 121–125

appending data, 160, 164–166

appendixes, in documentation, 319–320

Apple, 407

application programming interfaces. See APIs (application programming interfaces)

approvals, for dashboards, 331

APs (access points), 378

arithmetic logical units (ALUs), 185–186

array data type, 58

array keyword, 58

artificial intelligence (AI), 390

ASF (Advanced Systems Forma), 80

assumption-free methods, 146

asymmetric encryption, 376

Athena (AWS), 381

atomicity, 26

ATRAC (Adaptive Transform Acoustic Coding), 79

attributes, data

• attribute limitations, 417

• in dashboards, 324–326

• definition of, 35

audiences, report, 292–295

audio, 73–79

• analog-to-digital conversion, 73–75

• file formats, 75–79

Audio Interchange File Format (AIFF), 76

Audio Video Interleave (AVI), 79

audits, 433–434

AUPs (acceptable use policies), 389–390

automated validation, 430, 435–437

availability, 375

AVG function, 177–178

AVI (Audio Video Interleave), 79

AWS (Amazon Web Services)

• Athena, 381

• EBS, 385

• Lake Formation, 21, 381

• QuickSight, 221

• S3, 385

Azure

• Blobs, 385

• Database for MySQL Flexible Server, 187–190

• Synapse, 21

balanced tree, 194

bar charts, 346

BCNF (Boyce-Codd normal form), 170–171

best fit, line of, 248

BI (business intelligence) tools, 465

BigQuery, 95

binary data attributes, 325

bitmap (BMP) file format, 83

blending data, 161

Blowfish, 377

BLUF (Bottom Line Up Front) statements, 304

BMP (bitmap) file format, 83

“Bottom Line Up Front” statements, 304

Boyce-Codd normal form (BCNF), 170–171

branding, 313

bring-your-own-device (BYOD), 378

B-tree, 194

BTREE indexes, 194

bubble charts, 344–345

business intelligence (BI) tools, 465

business questions, reviewing, 259

business reports. See reports

business rules, 426

BusinessObjects, 219

BusinessObjects (SAP), 219

BYOD (bring-your-own-device), 378

cache, size of, 187

California Consumer Privacy Act (CCPA), 375

Canva, 353

cardinality, 395–396

categorical data type, 68–69

categorical variables, recoding, 158

CBR (constant bitrate) encoding, 78

CCPA (California Consumer Privacy Act), 375

central processing units (CPUs), 185–186

central tendency, measures of, 228–231

• mean, 228

• median, 229–230

• mode, 230–231

char keyword, 56

character data type, 56–57

Charts command (Insert menu), 343

charts/graphs. See also reports; statistical methods

• bar charts, 346

• bubble charts, 344–345

• creating, 341–342

• geographic maps, 350–351

• heat maps, 348–349

• histograms, 274, 347

• infographics, 353

• line charts, 342–343

• pie charts, 343–344

• in reports, 311–312

• scatter plots, 249, 274, 345–346

• sociograms, 274

• stacked charts, 352–353

• tree maps, 351–352

• waterfall charts, 348

• word clouds, 354–357

cheat sheet for exam preparation, 460–461

check constraints, 395

child nodes, 194

chi-square test, 244–246

choropleth maps. See geographic maps

CIA triad, 375

citations, report, 318–319

classification of data, 401–405

• examples of, 401

• PCI-DSS (Payment Card Industry Data Security Standard), 367, 379, 404–405

• PHI (personal health information), 201, 367, 403–404

• PII (personally identifiable information), 201, 367, 401–403, 417

cleansing data, 139–149

• data outliers, 146–148

• data quality and, 139

• data type validation, 148–149

• definition of, 258

• duplicate data, 140–141

• goals of, 140

• invalid data, 145–146

• missing values, 143–145

• non-parametric data, 146

• redundant data, 141–143

• specification mismatches, 148

C-level executive dashboards, 327

cloud-based storage, 385–386, 392

clustered indexes, 194

clustered sampling, 133

Cognos (IBM), 214

collection methods, 127–135

• observation, 133–134

• public databases, 129

• sampling, 132–133

• surveys, 129–132

  • face-to-face, 131–132

  • factors influencing, 130

  • online, 130–131

  • paper, 131

  • post-survey actions, 132

  • telephone, 131

• web scraping, 128–129

collective outliers, 147

color schemes, report, 306–309

color wheels, 307

Colorado Privacy Act, 375

columnar data stores, 10–11

columns, 92–94

comma-delimited files, 101–102

comma-separated values (CSV) format, 102

comparison

• in data analysis, 270

• of data over time, 268–271

  • comparison, 270

  • composition, 269

  • distribution, 270–271

  • relationships, 268

complementary color schemes, 308

completeness of data, 416

composition, in data analysis, 269

compression of data, 168

CompTIA Data+ exam

• NDA (nondisclosure agreement), 460

• post-exam career tips, 465

• preparation for, 460–461

• question types, 459–460

• registration for, 459

• test-taking tips, 461–465

CONCATENATE function, 162

concatenation, 162–164

confidence intervals, 240–241

confidence levels, 240

confidentiality, 375

conformity, 427–428

connectivity to DBMSs (database management systems), 7

consent, 369

consistency, 26, 149, 414–415

consolidation hubs, 446

consolidation of data fields, 446–448

constant bitrate (CBR) encoding, 78

constraints, data, 394–395

consumers, dashboard, 327–328

contextual outliers, 147

contingency tables, 274

continuous data attributes, 325

continuous data types, 69–72

convenience sampling, 133

conversion data, 420

corporate reporting standards, 312–313

correlation, 250–251

Cosmos DB, 10

COUNT function, 177–178

cover pages, 301–302

CPUs (central processing units), 185–186

crawlers, 129

create, read, update, and delete (CRUD) operations, 199, 221–222

CREATE INDEX statement, 196

CREATE TEMPORARY TABLE statement, 197

CREATE UNIQUE INDEX statement, 196

Crockford, Douglas, 102

cross-validation, 432–433

cryptography, 375–377

CSAT (customer satisfaction), 327

CSV (comma-separated values) format, 102

CURDATE function, 174

curly braces ({ }), 103

currency data type, 64–66

CURTIME function, 174

custodians, data, 368

customer satisfaction (CSAT), 327

dashboards, 321–335

• consumer types for, 327–328

• continuous/live data feeds, 326–327

• data sources and attributes, 324–326

• definition of, 322

• delivery, 332–335

  • interactive dashboards, 333–335

  • scheduled, 332–333

  • subscriptions, 332

• development process for, 328–332

  • approvals, 331

  • deployment to production, 331–332

  • high-level steps of, 328–329

  • mockups/wireframes, 329–331

  • role of designers in, 331

• examples of, 323

• improving data quality in, 421–422

• static data, 326–327

data acquisition/data mining

• APIs (application programming interfaces), 121–125

• data collection methods, 127–135

  • observation, 133–134

  • public databases, 129

  • sampling, 132–133

  • surveys, 129–132

  • web scraping, 128–129

• data integration, 114

• data monetization, 114

• delta load, 121

• ELT (extract, load, and transform) process, 117–119, 120

• ETL (extract, transform, and load) process, 115–117, 120

• improving data quality in, 419

• overview of, 113–114

• sources, 258, 260–261

• web services, 121–125

data analysis techniques, 255–276. See also data analytics tools; reports

• business questions, 259

• comparison of data over time, 268–271

  • comparison, 270

  • composition, 269

  • distribution, 270–271

  • relationships, 268

• connection of data points/pathway, 275–276

• data collection sources, 258, 260–261

• data needs, determining, 257–259

• descriptive statistics for, 273–274

• diagnostic analysis, 266

• exploratory data analysis, 272–273

• gap analysis, 261–263

• importance of, 255–257

• link analysis, 274–275

• performance analysis, 271

• predictive analysis, 266–267

• prescriptive analysis, 267

• projections to achieve goals, 272

• statistical analysis, 266

• text analysis, 266

• tracking of measurements against defined goals, 271–272

• trend analysis, 267–268

data analytics tools, 208–224. See also data analysis techniques

• Apex, 220

• AWS QuickSight, 221

• data lakes, 21

• data warehouses, 17

• Datorama, 220

• Domo, 220–221

• IBM Cognos, 214

• IBM Statistical Package Social Science (SPSS) Modeler, 214–215

• Microsoft Excel, 211–213

• Microsoft Power BI, 217–218

• MicroStrategy, 219

• Minitab, 221–222

• Python, 211

• Qlik, 218–219

• R, 213

• Rapid Miner, 214

• SAP BusinessObjects, 219

• SAS (Statistical Analytical System), 215–216

• SQL (Structured Query Language), 8, 210–211

• Stata, 221

• Tableau, 216–217

data audits, 433–434

data breach reporting, 407–408

data classification, 401–405

• examples of, 401

• PCI-DSS (Payment Card Industry Data Security Standard), 367, 379, 404–405

• PHI (personal health information), 201, 367, 403–404

• PII (personally identifiable information), 201, 367, 401–403, 417

data cleansing. See cleansing data

data collection methods, 127–135

• observation, 133–134

• public databases, 129

• sampling, 132–133

• surveys, 129–132

  • face-to-face, 131–132

  • factors influencing, 130

  • online, 130–131

  • paper, 131

  • post-survey actions, 132

  • telephone, 131

• web scraping, 128–129

data compression, 168

data consent, 369

data consistency, 149

data constraints, 394–395

data custodians, 368

data de-identification, 379–381

data deletion, 123, 179, 391–392

data dictionaries, 451–453

data dimensions, 46–50

• dimension data type, 68–69

• dimension tables, 35

• dimensionality reduction, 168

• overview of, 46

• SCDs (slowly changing dimensions), 46–50

  • definition of, 46–50

  • Type 0, 47

  • Type 1, 48

  • Type 2, 48–49

  • Type 3, 49

  • Type 4, 49–50

data encryption, 375–377, 378

data fields

• consolidation of, 446–448

• names of, 448–451

data governance. See governance

data ingestion layer, 20

data integration, 114

data integrity, 375, 416

data lakes, 20–23

• advantages of, 21–22

• data warehouses compared to, 22–23

• definition of, 20

• examples of, 21

• importance of, 15–16

• structure and components of, 20–21

data loss prevention (DLP), 379

data manipulation, 153–179. See also individual functions

• appending data, 160, 164–166

• blending data, 161

• concatenation, 162–164

• data reconciliation, 156

• definition of, 155

• derived variables, 159

• DML (Data Manipulation Language), 26, 156

• final product and reports, 421–422

• importance of, 155

• improving data quality in, 421

• imputation, 166–167

• merges, 160

• normalization, 170–171

• recoding, 156–159

• reduction of data, 167–168

• string manipulation, 171

  • aggregate functions, 177–179

  • date functions, 174

  • filtering, 171–172

  • logical functions, 174–177

  • sorting, 172–173

  • system functions, 179

• transposition, 168–169

Data Manipulation Language (DML), 26, 156

data marts, 18–19

data masking, 379–381

data mining. See data acquisition/data mining

data modeling, 34

data monetization, 114

data needs, determining, 257–259

data outliers, 146–148

data owners, 368

data partitioning, 202

data points, connection of, 275–276

data processing, 390–391

data profiling. See profiling and cleansing data

data purging, 392

data quality. See quality

data queries. See queries

data ranges, validation of, 149

data reconciliation, 156

data release approvals, 374

data retention, 392–393

data sharding, 202

data sources, 20

• in dashboards, 324–326

• definition of, 17

• documentation references to, 318–319

• improving data quality at, 419

data sovereignty, 406

data stewards, 368

data stores

• columnar, 10–11

• graph, 12

• key/value, 11

data structures

• file formats, 98–109

  • comma-delimited, 101–102

  • DSV, 102

  • HTML (Hypertext Markup Language), 106–109

  • JSON (Java Script Object Notation), 10, 102–103, 109

  • tab-delimited, 100–101

  • text/flat file, 99–100

  • XML (Extensible Markup Language), 104–106, 109

• metadata, 96–97

• overview of, 89–90

• semi-structured data, 96

• structured data, 90–94

  • abstract view of, 91

  • examples of, 91–92

  • key/value pairs, 94

  • rows/columns, 92–94

  • unstructured data compared to, 90–91

• unstructured data

  • abstract view of, 94–95

  • processing and analysis of, 95–96

  • structured data compared to, 90–91

data transformation

• data lakes, 20

• improving data quality in, 419–420

data transmission, 378–379

data transposition, 168–169

data types, 55–71. See also types of data

• alphanumeric, 63

• array, 58

• categorical, 68–69

• character, 56–57

• continuous, 69–72

• currency, 64–66

• date, 61–62

• definition of, 55

• dimension, 68–69

• discrete, 69–70

• double, 57–58

• float, 57

• integer, 57

• length of, 55

• numeric, 63–64

• storage sizes of, 56

• string, 58–59

• text, 64

• validation of, 148–149

data use agreements (DUAs), 373–374

data validation

• conformity/non-conformity, 427–428

• methods of, 430

  • audits, 433–434

  • automated validation, 430, 435–437

  • cross-validation, 432–433

  • data profiling, 430–432

  • sampling/spot checking, 433

• rows passed/rows failed, 429–430

data visualization techniques, 341–364. See also dashboards; documentation

• charts/graphs

  • bar charts, 346

  • bubble charts, 344–345

  • creating, 341–342

  • geographic maps, 350–351

  • heat maps, 348–349

  • histograms, 274, 347

  • infographics, 353

  • line charts, 342–343

  • pie charts, 343–344

  • in reports, 311–312

  • scatter plots, 249, 274, 345–346

  • sociograms, 274

  • stacked charts, 352–353

  • tree maps, 351–352

  • waterfall charts, 348

  • word clouds, 354–357

• importance of, 341–342

• reports, 301–313, 359–364

  • ad hoc/one-time, 362–363

  • business reports, 279–295

  • cover pages, 301–302

  • creating, 359

  • design elements, 306–313

  • documentation, 316–320

  • dynamic, 361–362

  • executive summary, 303, 304–306

  • instructions, 303

  • recurring, 363–364

  • self-service/on-demand, 363

  • static, 359–360

  • tactical/research, 364

data warehouses, 6-

• architecture of, 17–18

• components of, 17

• data lakes compared to, 22–23

• data marts, 18–19

• definition of, 16

• examples of, 17, 18

• importance of, 15–16

• key functions of, 16

databases. See also DBMSs (database management systems)

• data dimensions, 46–50

  • overview of, 46

  • SCDs (slowly changing dimensions), 46–50

• definition of, 3

• importance of, 3–4

• non-relational, 9–13

  • columnar data stores, 10–11

  • definition of, 9

  • document data stores, 10

  • graph data stores, 12

  • key/value data stores, 11

  • relational databases compared to, 12–13

• normalization, 43

• public, 129

• relational, 7–9

  • advantages of, 8–9

  • definition of, 7

  • Excel spreadsheets compared to, 8

  • non-relational databases compared to, 12–13

  • non-relational databases versus, 13

  • simple example of, 7

• schemas, 32–43

  • importance of, 34–35

  • logical, 33

  • physical, 33

  • snowflake, 34, 41–44

  • star, 34, 38–40, 43–44

  • view, 33

• simple example of, 4

• views, 33

date data type, 61–62

date functions, 174

date ranges, in reports, 288–290

DATE_FORMAT function, 174

DATEDIFF function, 174

Datorama, 220

DBMSs (database management systems)

• capabilities of, 4–7

• connectivity to, 7

• RDBMSs (relational database management systems), 9

• simple example of, 6

deep redundancy, 142

default value constraints, 395

de-identification, 379–381

DELETE function, 179

Delete method, 123

deletion of data, 123, 179, 391–392

delimiters, 100–101

delimiter-separated values (DSV) file format, 102

delivery, of dashboards, 332–335

• interactive dashboards, 333–335

• scheduled, 332–333

• subscriptions, 332

delta load, 121

dependent data marts, 19

deployment, dashboards, 331–332

derived variables, 159

descriptive statistics, 273–274

• central tendency, measures of, 228–231

  • mean, 228

  • median, 229–230

  • mode, 230–231

• definition of, 227

• dispersion, measures of, 231–234

  • absolute versus relative, 231

  • mean deviation, 232–233

  • quartile deviation, 232

  • range, 231–232

  • relative measures, 234

  • standard deviation, 233–234

  • variance, 233

• frequencies, 234–235

• percent change, 235–236

• percent difference, 235–236

design elements, report, 306–313

• charts/graphs, 311–312

• color schemes, 306–309

• corporate reporting standards, 312–313

• font size/style, 310–311

• importance of, 306

• report layout, 309–310

• style guides, 312–313

designers, dashboard, 331

developers, reports for, 294

development process, dashboards, 328–332

• approvals, 331

• deployment to production, 331–332

• high-level steps of, 328–329

• mockups/wireframes, 329–331

• role of designers in, 331

deviation

• mean, 232–233

• quartile, 232

• standard, 233–234

diagnostic analysis, 266

dictionaries, data, 451–453

digital rights management (DRM), 394

dimension data type, 68–69

dimension tables, 35

dimensionality reduction, 168

dimensions, data. See data dimensions

discrete data attributes, 325

discrete data types, 69–70

disk space, 185–186

dispersion, measures of, 231–234

• absolute versus relative, 231

• mean deviation, 232–233

• quartile deviation, 232

• range, 231–232

• relative measures, 234

• standard deviation, 233–234

• variance, 233

distribution

• in data analysis, 270–271

• frequency, 234–235

distribution-free methods, 146

DLP (data loss prevention), 379

DML (Data Manipulation Language), 26, 156

document data stores, 10

documentation, 316–320

• appendixes, 319–320

• FAQs, 319–320

• reference data sources and dates, 318–319

• version numbers, 317

DocumentDB, 10

Domo, 220–221

DOUBLE data type, 57–58

double quotes (“), 102

drill-down, 333

drives

• local, 384–385

• shared, 384

DRM (digital rights management), 394

DROP TABLE statement, 199

DSV (delimiter-separated values) file format, 102

DUAs (data use agreements), 373–374

duplicate data, 140–141

durability, 26

dynamic data masking, 379–381

dynamic reports, 361–362

ECC (Elliptic Curve Cryptography), 377

ecosystem, API (application programming interface), 122

electronic protected health information (ePHI), 403

Elliptic Curve Cryptography (ECC), 377

ELT (extract, load, and transform) process, 419–420

• ETL process compared to, 120

• stages of, 117–119

Encapsulated Postscript (EPS), 84

encryption, 375–377, 378

end-to-end encryption, 378

entity relationship requirements, 393–396

• cardinality, 395–396

• data constraints, 394–395

• importance of, 393–394

• record link restrictions, 394

ePHI (electronic protected health information), 403

EPS (Encapsulated Postscript), 84

ER (entity relationship) requirements, 393–396

• cardinality, 395–396

• data constraints, 394–395

• importance of, 393–394

• record link restrictions, 394

errors

• type I, 247

• type II, 247

escalation of data breach reporting, 407–408

ETL (extract, transform, and load) process, 140, 419–420

• data warehouses, 17

• ELT process compared to, 120

• stages of, 115–117

exam, CompTIA Data+. See CompTIA Data+ exam

Excel. See Microsoft Excel

execution plans, query, 186, 187–190

executive summaries, 303

executives, reports for, 293–294

EXPLAIN statement, 188

exploratory data analysis, 272–273

Extensible Markup Language (XML), 104–106, 109

external customers, 327, 328

external data sources, 261

extract, load, and transform process. See ELT (extract, load, and transform) process

extract, transform, and load process. See ETL (extract, transform, and load) process

extract stage

• ELT (extract, load, and transform) process, 118

• ETL (extract, transform, and load) process, 115–116

extrapolation, 145

Facebook, 213

face-to-face surveys, 131–132

facts

• definition of, 34

• fact tables, 35

false negatives, 247

false positives, 247

Family Educational Rights and Privacy Act (FERPA), 379

FAQs (frequently asked questions), 319–320

FERPA (Family Educational Rights and Privacy Act), 379

field separators, 100–101

fields

• JSON (Java Script Object Notation), 103

• standardization of, 448–451

• tab-delimited files, 100

file formats

• AAC, 77

• AIFF, 76

• ASF, 80

• ATRAC, 79

• AVI, 79

• BMP, 83

• CBR, 78

• comma-delimited, 101–102

• CSV, 102

• data, 98–109

• DSV, 102

• EPS, 84

• FLAC, 77–78

• GIF, 84

• GSM, 78

• HTML, 106–109

• JPEG, 83

• JSON, 10, 102–103, 109

• MOV, 80

• MP3, 76

• MPEG, 80

• OGG, 78, 81

• PDF, 84–85

• PNG, 84

• PSP, 79

• QT, 80

• RAW, 78

• SVG, 84

• tab-delimited, 100–101

• text/flat file, 99–100

• TSV, 100–101

• validation of, 149

• W3C, 84

• WAV, 75

• WebM, 81

• WebP, 84

• WMA, 78

• WMV, 80

• XML, 104–106, 109

Filled Maps command (Maps menu), 351

filters, 171–172, 285–287

final product, improving data quality in, 421–422

first normal form (1NF), 170

FLAC (Free Lossless Audio Codec), 77–78

flat dimensions, 39

flat files, 99–100

FLOAT data type, 57

float keyword, 57

floating point numbers, 57–58

fonts, report, 310–311

foreign keys, 395

formal reports, 282

fourth normal form (4NF), 170

Free Lossless Audio Codec (FLAC), 77–78

frequencies, 234–235

frequency of reports, 291

frequently asked questions (FAQs), 319–320

functions

• aggregate, 177–179

• AND, 175–176

• AVG, 177–178

• CONCATENATE, 162

• COUNT, 177–178

• CURDATE, 174

• CURTIME, 174

• date, 174

• DATE_FORMAT, 174

• DATEDIFF, 174

• DELETE, 179

• IF, 177

• INSERT, 179

• logical, 174–177

• MAX, 177–179

• MIN, 177

• NOT, 176

• NOW, 174

• OR, 175–176

• SUM, 177

• system, 179

• TRANSPOSE, 169

• UPDATE, 179

gap analysis, 261–263

GCP (Google Cloud Platform), 95, 211, 392

GDPR (General Data Protection Regulation), 201, 374–375, 379, 406

general public dashboards, 328

geographic maps, 350–351

Get method, 122

GIF (Graphics Interchange Format), 84

GLB (Gramm-Leach-Bliley) Act, 393

global outliers, 147

Global System for Mobile, 78

global temporary tables, 199

goals

• projections to achieve, 272

• tracking of measurements against, 271

goodness-of-fit, chi-square test of, 244

Google, 213

• BigQuery, 21

• GCP (Google Cloud Platform), 95, 392

• Looker, 211, 333

governance, 367–408

• access requirements, 370–374

  • DUAs (data use agreements), 373–374

  • RBAC (role-based access control), 331, 372

  • release approvals, 374

  • user group-based access control, 373

• benefits of, 368–369

• data breach reporting, 407–408

• data classification, 401–405

  • examples of, 401

  • PCI-DSS (Payment Card Industry Data Security Standard), 367, 379, 404–405

  • PHI (personal health information), 201, 367, 403–404

  • PII (personally identifiable information), 201, 367, 401–403, 417

• entity relationship requirements, 393–396

  • cardinality, 395–396

  • data constraints, 394–395

  • importance of, 393–394

  • record link restrictions, 394

• jurisdiction requirements, 406–407

• roles in, 368

• security requirements, 374–381

  • CIA triad, 375

  • data de-identification/masking, 379–381

  • data encryption, 375–377, 378

  • data transmission, 378–379

  • GDPR (General Data Protection Regulation), 201, 374–375

• storage environment requirements, 383–386

  • cloud-based storage, 385–386

  • local storage, 384–385

  • shared drives, 384

• use requirements, 389–393

  • AUPs (acceptable use policies), 389–390

  • data deletion, 391–392

  • data processing, 390–391

  • data retention, 392–393

GPUs (graphics processing units), 185–186

Gramm-Leach-Bliley (GLB) Act, 393

graph data stores, 12

Graphics Interchange Format (GIF), 84

graphics processing units (GPUs), 185–186

graphs/charts. See also reports; statistical methods

• bar charts, 346

• bubble charts, 344–345

• creating, 341–342

• geographic maps, 350–351

• heat maps, 348–349

• histograms, 347

• infographics, 353

• line charts, 342–343

• pie charts, 343–344

• scatter plots, 345–346

• stacked charts, 352–353

• tree maps, 351–352

• waterfall charts, 348

• word clouds, 354–357

GSM file format, 78

Health Insurance Portability and Accountability Act (HIPAA), 379, 393, 403

heat maps, 348–349

HIPAA (Health Insurance Portability and Accountability Act), 379, 393, 403

histograms, 274, 347

HTML (Hypertext Markup Language), 104, 106–109

hubs, 446

hybrid data marts, 19

Hypertext Markup Language (HTML), 104, 106–109

hypothesis testing, 239, 246–247

IBM

• Cognos, 214

• SPSS (Statistical Package Social Science), 157, 214–215

• Watson, 214

IETF (Internet Engineering Task Force), 102

IF function, 177

images, 81–85

• file formats, 83–85

• raster versus vector, 81–85

imputation, 145, 166–167

independence, chi-square test of, 244

independent data marts, 19

indexing, 187, 193–196

inferential statistics, 238–251

• chi-square test, 244–246

• confidence intervals, 240–241

• correlation, 250–251

• definition of, 227

• goals of, 239

• hypothesis testing, 239, 246–247

• p-values, 243–244

• simple linear regression, 248–249

• t-tests, 242–243

• Z-scores, 241–242

infographics, 353

informal reports, 282

Informatica, 447

informational reports, 283

INSERT function, 179

Insert menu commands, Charts, 343

instructions, report, 303

INT data type, 57

integer data type, 57

integration data, 114

integrity, data, 375, 416

IntelliClean, 141

interactive dashboards, 333–335

internal customers, 327

internal data sources, 261

Internet Engineering Task Force (IETF), 102

Internet of Things (IoT), 95, 104

interpolation, 145

interval data attributes, 325

intrahops, improving data quality in, 419–420

invalid data, 145–146

IoT (Internet of Things), 95, 104

IS NOT NULL statement, 96

IS NULL statement, 96

isolation, 26

IT team dashboards, 328

IT/operations teams, 294

Jason Davies, 355

Java Script Object Notation (JSON), 10, 102–103

JPEG (Joint Photographic Experts Group), 83

JSON (Java Script Object Notation), 10, 102–103, 109

jurisdiction requirements, 406–407

key performance indicators. See KPIs (key performance indicators)

keys

• foreign, 395

• key/value data stores, 11

• key/value pairs, 94

• primary, 395

keywords. See also functions; statements

• array, 58

• char, 56

• float, 57

K-fold cross-validation, 432

Kimball, Ralph, 34

know your customer (KYC), 456

KPIs (key performance indicators). See also dashboards

• recurring reports, 363–364

• selection of, 284

• tracking against defined goals, 271–272

KYC (know your customer), 456

Lake Formation (AWS), 381

lakes, data. See data lakes

languages

• DML (Data Manipulation Language), 26, 156

• HTML (Hypertext Markup Language), 104, 106–109

• Python, 211

• R, 213

• SGML (Standard Generalized Markup Language), 104

• SQL (Structured Query Language), 8, 210–211

• VQL (Visual Query Language), 217

• XML (Extensible Markup Language), 104–106

layout, reports, 309–310

leadership, reports for, 294

leadership dashboards, 327–328

leaf nodes, 194

legislation

• CCPA (California Consumer Privacy Act), 375

• Colorado Privacy Act, 375

• FERPA (Family Educational Rights and Privacy Act), 379

• GDPR (General Data Protection Regulation), 201, 374–375, 379, 406

• GLB (Gramm-Leach-Bliley) Act, 393

• HIPAA (Health Insurance Portability and Accountability Act), 379, 393, 403

• impact of, 406

• Patriot Act, 406

• Virginia Consumer Data Privacy Act, 375

life cycle, data quality and

• data acquisition/data source, 419

• data manipulation, 421

• data transformation/intrahops, 419–420

• final product and reports, 421–422

• overview of, 418

line charts, 342–343

line of best fit, 248

linear regression, simple, 248–249

links

• definition of, 274

• link analysis, 274–275

lists, 39

live data feeds, 326–327

load stage

• ELT (extract, load, and transform) process, 119

• ETL (extract, transform, and load) process, 116

local storage, 384–385

local temporary tables, 200

logical functions, 174–177

• AND, 175–176

• IF, 177

• NOT, 176

• OR, 175–176

logical rules, imputation based on, 167

logical schemas, 33

logos, 313

LONGTEXT data type, 64

Looker (Google), 211, 333

management dashboards, 327–328

managers, reports for, 294

manipulation of data. See data manipulation

manpower gaps, 263

maps. See also charts/graphs

• geographic, 350–351

• heat, 348–349

• tree, 351–352

Maps menu commands, Filled Maps, 351

MAR (missing at random), 143–144

MariaDB, 10

markers, 343

market gaps, 263

marketing teams, reports for, 294

masking data, 379–381

master data management. See MDM (master data management)

MAX function, 177–179

MCAR (missing completely at random), 143–144

MDM (master data management), 324, 441–458

• capabilities of, 443

• data consolidation, 446–448

• data dictionaries, 451–453

• data standardization, 448–451

• definition of, 441, 443

• example of, 444–446

• interaction with business functions, 444–446

• mergers and acquisitions, 455

• policy compliance, 456

• streamlined data access, 456–457

mean

• calculation of, 228

• imputation with, 145

mean deviation

• calculation of, 232–233

• mean, 232–233

measurements, tracking against defined goals, 271

measures

• of central tendency, 228–231

  • mean, 228

  • median, 229–230

  • mode, 230–231

• of dispersion, 231–234

  • absolute versus relative, 231

  • mean deviation, 232–233

  • quartile deviation, 232

  • range, 231–232

  • relative measures, 234

  • standard deviation, 233–234

  • variance, 233

• of position, 274

median

• calculation of, 229–230

• imputation with, 145

MEDIUMTEXT data type, 64

mergers and acquisitions, MDM (master data management) for, 455

merging data, 160

metadata, 6, 96–97, 451–453

metrics, quality, 413–414

• accessibility, 417

• accuracy, 415

• attribute limitations, 417

• completeness, 416

• consistency, 414–415

• integrity, 416

• validity, 416

Microsoft Azure Synapse, 95

Microsoft Excel, 211–213

• charts/graphs, 342. See also reports

  • bar charts, 346

  • bubble charts, 344–345

  • creating, 341–342

  • geographic maps, 350–351

  • heat maps, 348–349

  • histograms, 347

  • infographics, 353

  • line charts, 342–343

  • pie charts, 343–344

  • scatter plots, 345–346

  • stacked charts, 352–353

  • tree maps, 351–352

  • waterfall charts, 348

  • word clouds, 354–357

• data manipulation, 153–179

  • appending, 160, 164–166

  • blending, 161

  • concatenation, 162–164

  • data reconciliation, 156

  • definition of, 155

  • derived variables, 159

  • importance of, 155

  • imputation, 166–167

  • merging, 160

  • normalization, 170–171

  • recoding, 156–159

  • reduction of data, 167–168

  • string manipulation, 171

  • transposition, 168–169

• data validation in, 435–437

• functions. See functions

• Power Query Editor, 165

• rows/columns, 92–94

• spreadsheets, 8, 211–213

Microsoft Power BI, 217–218, 302

• measuring data quality with, 421

• mockups/wireframes, 329

• scheduled deliveries in, 333

Microsoft PowerPoint

• report options in, 302

• versioning tools, 317

Microsoft Word

• citation options, 318–319

• versioning tools, 317

MicroStrategy, 219

MIN function, 177

mining data. See data mining

Minitab, 221–222

mismatches, specification, 148

missing values, 143–145

• imputation of, 166–167

• MAR (missing at random), 143–144

• MCAR ( missing completely at random), 143–144

mobile device management, 378

mockups, 329–331

mode, 230–231

modeling, data, 34

monetization, data, 114

MongoDB, 10

monochromatic color schemes, 307

most frequent values, imputation with, 145

MOV file format, 80

Moving Picture Experts Group. See MPEG (Moving Picture Experts Group)

MPEG (Moving Picture Experts Group), 76, 80

MPLS (Multiprotocol Label Switching), 378

multimedia

• audio, 73–79

• images, 81–85

• video, 79–81

multiple-choice questions, 460

Multiprotocol Label Switching (MPLS), 378

MySQL, 170. See also data types

• functions

  • aggregate, 177–179

  • AVG, 177–178

  • COUNT, 177–178

  • CURDATE, 174

  • CURTIME, 174

  • date, 174

  • DATE_FORMAT, 174

  • DATEDIFF, 174

  • DELETE, 179

  • INSERT, 179

  • logical, 174–177

  • MAX, 177–179

  • MIN, 177

  • NOW, 174

  • SUM, 177

  • system, 177–179

  • UPDATE, 179

• MySQL Workbench execution plans, 187–190

• statements

  • CREATE INDEX, 193–196

  • CREATE TEMPORARY TABLE, 197

  • CREATE UNIQUE INDEX, 196

  • DROP TABLE, 199

  • IS NOT NULL, 96

  • IS NULL, 96

  • SHOW TABLES, 198

names, field, 448–451

name/value pairs, 103

NDA (nondisclosure agreement), for CompTIA Data+ exam, 460

negative correlation, 250–251

networks, in link analysis, 274

NF (normal forms), 143, 170

nodes

• B-tree, 194

• in link analysis, 274

nominal data attributes, 325

non-clustered indexes, 194

non-conformity, 427–428

nondisclosure agreement (NDA), for CompTIA Data+ exam, 460

non-parametric data, elimination of, 146

non-parametric methods, 146

non-random missing values, 144

non-relational databases, 9–13

• columnar data stores, 10–11

• definition of, 9

• document data stores, 10

• graph data stores, 12

• key/value data stores, 11

• relational databases compared to, 12–13

non-sensitive PII information, 402

normal forms (NF), 143, 170

normalization, 143, 170–171

NoSQL databases. See non-relational databases

NOT function, 176

NOW function, 174

null hypothesis, 246

null values, 96

Nullsoft Winamp, 76, 79

numbers

• double data type, 57–58

• float data type, 57–58

• integer data type, 57

• interval data attributes, 325

• numeric data attributes, 325

• numeric data type, 63–64

• numerical values, recoding, 158–159

numeric data attributes, 325

numeric data type, 63–64

Numerical Python (NumPy), 211

numerical values, recoding, 158–159

numerosity reduction, 168

observation, 133–134, 167

OGG format, 78, 81

OLAP (Online Analytical Processing), 26–29

• definition of, 26–27

• key tenets of, 27

• OLTP (Online Transactional Processing) integration with, 29–30

OLTP (Online Transactional Processing), 24–26

• ACID design principle, 25–26

• definition of, 25

• OLAP (Online Analytical Processing) integration with, 29–30

Omni calculator, 241

on-demand reports, 363

one-sample t-tests, 243

one-time reporting, 362–363

Online Analytical Processing. See OLAP (Online Analytical Processing)

online surveys, 130–131

Online Transactional Processing. See OLTP (Online Transactional Processing)

on-the-fly data masking, 379

open standard, 75

optimization, query, 184–202

• cache size, 187

• considerations for, 186–187

• definition of, 186

• execution plans, 186, 187–190

• indexing, 187, 193–196

• parameterization, 190–192

• subqueries, 187

• temporary tables, 197–200

• wildcard searches, 186

OR function, 175–176

Oracle, 333, 447

ordinal data attributes, 325

outliers data, 146–148

paired t-tests, 243

paper surveys, 131

parameterization, query, 187–190

parent nodes, 194

parsing data, 171

participant-based observation, 133–134

partitioning, data, 202

partner APIs (application programming interfaces), 122

passive data dictionaries, 452

pass-through data, 420

Patriot Act, 406

PAYG (pay-as-you-go), 446

PCI-DSS (Payment Card Industry Data Security Standard), 367, 379, 404–405

PDF (Portable Document Format), 84–85

Pearson VUE, registration with, 459

percent change, 235–236

percent difference, 235–236

performance analysis, 271

performance gaps, 263

performance-based questions, 460

personal health information. See PHI (personal health information)

personally identifiable information. See PII (personally identifiable information)

personas, audience, 293–294

PHI (personal health information), 201, 367, 403–404

physical schemas, 33

pie charts, 343–344

PII (personally identifiable information), 201, 367, 401–403, 417

plans, query execution, 186, 187–190

PlayStation Portable (PSP), 79

PNG (Portable Network Graphics), 84

point-of-sale (PoS) data, 95

policies

• AUPs (acceptable use policies), 389–390

• data deletion, 391–392

• data retention, 392–393

• policy compliance, 456

Portable Document Format (PDF), 84–85

Portable Network Graphics (PNG), 84

PoS (point-of-sale) data, 95

position, measures of, 274

positive correlation, 250–251

Post method, 123

pound sign (#), 200

Power BI, 217–218, 302

• measuring data quality with, 421

• mockups/wireframes, 329

• scheduled deliveries in, 333

Power Query Editor, 165

PowerPoint

• report options in, 302

• versioning tools, 317

practice exams, 462

predictive analysis, 266–267

preparation for CompTIA Data+ exam

• cheat sheet, 460–461

• NDA (nondisclosure agreement), 460

• post-exam career tips, 465

• question types, 459–460

• registration for, 459, 463

• test-taking tips, 461–465

prescriptive analysis, 267

primary audience, 293

primary colors, in reports, 307

primary data sources, 260

primary keys, 395

privacy and security requirements. See security requirements

private APIs (application programming interfaces), 122

private information, 402

product gaps, 263

production, deploying dashboards to, 331–332

profiling, data, 426

profiling and cleansing data, 139–149, 430–432

• data outliers, 146–148

• data quality and, 139

• data type validation, 148–149

• definition of, 258

• duplicate data, 140–141

• goals of, 140

• invalid data, 145–146

• missing values, 143–145

• non-parametric data, 146

• redundant data, 141–143

• specification mismatches, 148

profit gaps, 263

projections to achieve goals, 272

prolog XML (Extensible Markup Language), 105

proprietary formats, 75

PSP (PlayStation Portable), 79

public APIs (application programming interfaces), 122

public databases, 129

public information, 403

purging data, 392

Put method, 123

p-values, 243–244

Python, 211

Qlik, 218–219

• measuring data quality with, 421

• scheduled deliveries in, 333

QT (QuickTime) file format, 80

quality, 139, 413–437

• business rules, 426

• circumstances to check for

  • data acquisition/data source, 419

  • data manipulation, 421

  • data quality life cycle overview, 418

  • data transformation/intrahops, 419–420

  • final product and reports, 421–422

• data profiling, 426, 430–432

• data validation methods, 430

  • audits, 433–434

  • automated validation, 430, 435–437

  • cross-validation, 432–433

  • data profiling, 430–432

  • sampling/spot checking, 433

• data validation rules, 426–427

  • conformity/non-conformity, 427–428

  • rows passed/rows failed, 429–430

• expectations for, 434

• metrics, 413–414

  • accessibility, 417

  • accuracy, 415

  • attribute limitations, 417

  • completeness, 416

  • consistency, 414–415

  • integrity, 416

  • validity, 416

• validity, 416

quartile deviation, 232

queries

• optimization and testing, 184–202

  • cache size, 187

  • considerations for, 186–187

  • definition of, 186

  • execution plans, 186, 187–190

  • indexing, 187, 193–196

  • parameterization, 187–190

  • subqueries, 187

  • subsets of records, 200–202

  • temporary tables, 197–200

  • wildcard searches, 186

• parameterized, 187–190

• tools

  • data lakes, 21

  • data warehouses, 17

QuickSight (AWS), 221

QuickTime (QT) file format, 80

quotation marks (“), 102

R, 213

RACI matrix, 391

random missing values, 143–144

random sampling, 133

range, 231–232

Rapid Miner, 214

raster images, 81–84

ratio data attributes, 325

RAW audio files, 78

RBAC (role-based access control), 121, 331, 372

RDBMSs (relational database management systems), 9

recoding data, 156–159

reconciliation, data, 156

record link restrictions, 394

records, subsets of, 200–202

recurring reports, 363–364

RedShift, 10

reduction of data, 167–168

redundant data, 141–143

reference citations, 318–319

refresh rates, 79

registration for CompTIA Data+ exam, 459, 463

regression, simple linear, 248–249

regulation-enforced data deletion, 392

regulations. See also legislation

• compliance with, 456

• impact of, 406

relational database management systems (RDBMSs), 9

relational databases, 7–9

• advantages of, 8–9

• definition of, 7

• Excel spreadsheets compared to, 8

• non-relational databases compared to, 12–13

• simple example of, 7

relationship cardinality, 395–396

relationships, in data analysis, 268

relative measures of dispersion, 231, 234

release approvals, 374

Remote Procedure Call (RPC), 122

replacing missing vlaues, 144

reports, 279–295, 301–313, 359–364. See also charts/graphs

• ad hoc/one-time, 362–363

• analytical versus informational, 283

• audience for, 292–295

• content of, 282–284

• cover pages, 301–302

• creating, 359

• data breach reporting, 407–408

• date range for, 288–290

• design elements, 306–313

  • charts/graphs, 311–312

  • color schemes, 306–309

  • corporate reporting standards, 312–313

  • font size and style, 310–311

  • importance of, 306

  • report layout, 309–310

  • style guides, 312–313

• documentation, 316–320

  • appendixes, 319–320

  • FAQs, 319–320

  • reference data sources and dates, 318–319

  • version numbers, 317

• dynamic, 361–362

• executive summaries in, 303

• executive summary, 304–306

• filters in, 285–287

• formal versus informal, 282

• frequency of, 291

• high-level objectives of, 279–282

• improving data quality in, 421–422

• instructions, 303

• KPIs (key performance indicators)

  • selection of, 284

  • tracking against defined goals, 271–272

• recurring, 363–364

• self-service/on-demand, 363

• static, 359–360

• tactical/research, 364

• views of, 287–288

requirements gathering, 257–258

REST (representational state transfer), 122

restricted information, 403

retention, data, 392–393

RFC 8259, 102

ROI (return on investment), 114

role-based access control (RBAC), 121, 331, 372

roll-up, 334

rows, 92–94

rows passed/rows failed rules, 429–430

RPC (Remote Procedure Call), 122

RSA, 377

rules

• business, 426

• validation, 426–427

  • conformity/non-conformity, 427–428

  • rows passed/rows failed, 429–430

S3 (AWS), 385

SaaS (software as a service), 220

• solutions

  • AWS QuickSight, 221

  • Datorama, 220

  • Power BI Service, 218

  • Qlik Sense, 219

Salesforce, 220, 326

sampling, 132–133, 433

SAP, 219, 447

Sarbanes-Oxley Act (SOX), 379

SAS (Statistical Analytical System), 215–216

Scalable Vector Graphics (SVG), 84

scatter plots, 249, 274, 345–346

SCDs (slowly changing dimensions), 46–50

• definition of, 46

• Type 0, 47

• Type 1, 48

• Type 2, 48–49

• Type 3, 49

• Type 4, 49–50

scheduled delivery, 332–333

schemas, database, 32–43

• importance of, 34–35

• logical, 33

• physical, 33

• snowflake, 34, 41–44

  • characteristics of, 43

  • definition of, 41

  • examples of, 41–42

  • structure of, 41

• star, 34, 38–40

  • characteristics of, 40

  • definition of, 38

  • examples of, 39–40

  • snowflake schema versus, 43–44

  • structure of, 38

• view, 33

scrubbing data. See cleansing data

search engine optimization (SEO), 334

second normal form (2NF), 170

secondary audience, 293

secondary colors, in reports, 307

secondary data sources, 260

secure wired access, 378

secure wireless access, 378

security requirements, 374–381

• CIA triad, 375

• dashboards, 335

• data de-identification/masking, 379–381

• data encryption, 375–377, 378

• data transmission, 378–379

• GDPR (General Data Protection Regulation), 201, 374–375

self-service reports, 363

semi-structured data, 96

Sense, measuring data quality with, 421

sensitive PII information, 402

SEO (search engine optimization), 334

SGML (Standard Generalized Markup Language), 104

sharding, data, 202

shared drives, 384

shareholder dashboards, 328

SHOW TABLES statement, 198

signed char data type, 56

simple linear regression, 248–249

Simple Object Access Protocol (SOAP), 122

single-tier architecture, 17

size

• of data types, 56

• of report fonts, 310–311

slowly changing dimensions. See SCDs (slowly changing dimensions)

Snowflake, 21

snowflake schemas, 34, 41–44

• characteristics of, 43

• definition of, 41

• examples of, 41–42

• structure of, 41

SOAP (Simple Object Access Protocol), 122

sociograms, 274

software as a service. See SaaS (software as a service)

Sony Walkman, 79

sorting data, 172–173

sources, data collection, 258, 260–261

sovereignty, data, 406

SOX (Sarbanes-Oxley Act), 379

Spearman correlation test, 146

specification mismatches, 148

SPICE (Super-fast, Parallel, In-memory Calculation Engine), 221

spiders, 129

split complementary color schemes, 308

spontaneous observation, 133–134

spot checking, 432–433

spreadsheets, 8, 211–213. See also Microsoft Excel

SPSS (Statistical Package for the Social Sciences), 157, 214–215

SQL (Structured Query Language), 8, 188, 210–211. See also MySQL

square brackets ([ ]), 103

stacked charts, 352–353

standard deviation, 233–234

Standard Generalized Markup Language (SGML), 104

standardization of names, 448–451

star schemas, 34, 38–40

• characteristics of, 40

• definition of, 38

• illustration of, 38

• snowflake schema versus, 43–44

• structure of, 39–40

Stata, 221

statements, 188

• CREATE INDEX, 196

• CREATE TEMPORARY TABLE, 197

• CREATE UNIQUE INDEX, 196

• DROP TABLE, 199

• IS NOT NULL, 96

• IS NULL, 96

• SHOW TABLES, 198

static data, 326–327

static data masking, 379–380

static reports, 359–360

statistical analysis, 266

Statistical Analytical System (SAS), 215–216

statistical methods

• descriptive, 273–274

  • central tendency, measures of, 228–231

  • definition of, 227

  • dispersion, measures of, 231–234

  • frequencies, 234–235

  • percent change, 235–236

  • percent difference, 235–236

• inferential, 238–251

  • chi-square test, 244–246

  • confidence intervals, 240–241

  • correlation, 250–251

  • definition of, 227

  • goals of, 239

  • hypothesis testing, 239, 246–247

  • p-values, 243–244

  • simple linear regression, 248–249

  • t-tests, 242–243

  • Z-scores, 241–242

Statistical Package for the Social Sciences (SPSS), 157, 214–215

stewards, data, 368

storage environment requirements, 383–386

• cloud-based storage, 385–386

• local storage, 384–385

• shared drives, 384

• storage sizes, 56

strategy gaps, 263

stratified sampling, 133

streamlined data access, MDM (master data management) for, 456–457

string data type, 58–59

strings

• concatenation, 162–164

• manipulation of, 171

  • aggregate functions, 177–179

  • date functions, 174

  • filtering, 171–172

  • logical functions, 174–177

  • sorting, 172–173

  • system functions, 179

structure observation, 133–134

structured data, 90–94

• abstract view of, 91

• examples of, 91–92

• key/value pairs, 94

• rows/columns, 92–94

• unstructured data compared to, 90–91

Structured Query Language. See MySQL; SQL (Structured Query Language)

style guides, 312–313

subqueries, optimization of, 187

subscriptions, 332

subsets of records, 200–202

SUM function, 177

summary section, reports, 303, 304–306

Super-fast, Parallel, In-memory Calculation Engine (SPICE), 221

superficial redundancy, 142

surveys, 129–132

• face-to-face, 131–132

• factors influencing, 130

• online, 130–131

• paper, 131

• post-survey actions, 132

• telephone, 131

SVG (Scalable Vector Graphics), 84

symmetric encryption, 376

system functions, 179

systematic sampling, 133

tab-delimited files, 100–101

Tableau, 216–217, 302, 465

• measuring data quality with, 421

• mockups/wireframes, 329

• reports. See reports

• scheduled deliveries in, 333

tables

• contingency, 274

• dimension, 35

• fact, 35

• temporary, 197–200

  • creating, 197–198

  • deleting, 199

  • global, 199

  • local, 200

  • showing, 198

tab-separated values (TSV), 100–101

tactical reports, 364

target audience, 293

telephone surveys, 131

temporary tables, 197–200

• creating, 197–198

• deleting, 199

• global, 199

• local, 200

• showing, 198

tertiary audience, 293

tertiary colors, in reports, 307

testing

• hypothesis, 246–247

• queries, 184–202

  • cache size, 187

  • considerations for, 186–187

  • definition of, 186

  • execution plans, 186, 187–190

  • indexing, 187, 193–196

  • parameterization, 187–190

  • subqueries, 187

  • subsets of records, 200–202

  • temporary tables, 197–200

  • wildcard searches, 186

• t-tests, 242–243

test-taking tips, 461–465

tetradic color schemes, 308

text analysis, 266

text data type, 64

text databases, 99–100

text/flat files, 99–100

third normal form (3NF), 170–171

TIBCO, 447

time, comparison of data over, 268–271

• comparison, 270

• composition, 269

• distribution, 270–271

• relationships, 268

timely integration, 455

tracking of measurements against defined goals, 271

transactional processing. See OLTP (Online Transactional Processing)

transform stage

• ELT (extract, load, and transform) process, 119

• ETL (extract, transform, and load) process, 116

transformation, data, 419–420

transmission, data, 378–379

TRANSPOSE function, 169

transposition of data, 168–169

tree maps, 351–352

trend analysis, 267–268

triadic color schemes, 308

TSV (tab-separated values), 100–101

t-tests, 242–243

Twitter, 213

two-sample t-tests, 243

two-tier architecture, 17

Type 0 SCDs (slowly changing dimensions), 47

Type 1 SCDs (slowly changing dimensions), 48

Type 2 SCDs (slowly changing dimensions), 48–49

Type 3 SCDs (slowly changing dimensions), 49

Type 4 SCDs (slowly changing dimensions), 49–50

type I errors, 247

type II errors, 247

types of data. See also data types

• audio, 73–79

• images, 81–85

• video, 79–81

typography, reports, 310–311

U test, 146

Uber, 213

undefined values, 96

uniqueness constraints, 395

unsigned char data type, 56

unstructured data

• abstract view of, 94–95

• processing and analysis of, 95–96

• structured data compared to, 90–91

UPDATE function, 179

use requirements, 389–393

• AUPs (acceptable use policies), 389–390

• data deletion, 391–392

• data processing, 390–391

• data retention, 392–393

user group-based access control, 373

user request-based data deletion, 391

utilization of data, 455

validation

• conformity/non-conformity, 427–428

• data consistency, 149

• data format, 149

• data range, 149

• data type, 148–149

• methods of, 430

  • audits, 433–434

  • automated validation, 430, 435–437

  • cross-validation, 432–433

  • data profiling, 430–432

  • sampling/spot checking, 433

• rows passed/rows failed, 429–430

• rules, 426–427

validity of data, 416

VARCHAR data type, 64