280:
191:, and uses a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt. It can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Effective prescriptive analytics utilises hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities. Basu suggests that without hybrid data input, the benefits of prescriptive analytics are limited.
216:, and signal processing. The correct application of all these methods and the verification of their results implies the need for resources on a massive scale including human, computational and temporal for every Prescriptive Analytic project. In order to spare the expense of dozens of people, high performance machines and weeks of work one must consider the reduction of resources and therefore a reduction in the accuracy or reliability of the outcome. The preferable route is a reduction that produces a probabilistic result within acceptable limits.
288:
5,000 - 35,000 feet below the surface and to describe activities around the wells themselves, such as depositional characteristics, machinery performance, oil flow rates, reservoir temperatures and pressures. Prescriptive analytics software can help with both locating and producing hydrocarbons by taking in seismic data, well log data, production data, and other related data sets to prescribe specific recipes for how and where to drill, complete, and produce wells in order to optimize recovery, minimize cost, and reduce environmental footprint.
25:
227:
164:
297:
309:
analytics software can also provide decision options and show the impact of each decision option so the operations managers can proactively take appropriate actions, on time, to guarantee future exploration and production performance, and maximize the economic value of assets at every point over the course of their serviceable lifetimes.
183:
anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics incorporates both
219:
All three phases of analytics can be performed through professional services or technology or a combination. In order to scale, prescriptive analytics technologies need to be adaptive to take into account the growing volume, velocity, and variety of data that most mission critical processes and their
398:
Prescriptive analytics can help pharmaceutical companies to expedite their drug development by identifying patient cohorts that are most suitable for the clinical trials worldwide - patients who are expected to be compliant and will not drop out of the trial due to complications. Analytics can tell
390:
Prescriptive analytics can help providers improve effectiveness of their clinical care delivery to the population they manage and in the process achieve better patient satisfaction and retention. Providers can do better population health management by identifying appropriate intervention models for
353:
have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive analytics software can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the
287:
Energy is the largest industry in the world ($ 6 trillion in size). The processes and decisions related to oil and natural gas exploration, development and production generate large amounts of data. Many types of captured data are used to create models and images of the Earth’s structure and layers
308:
Prescriptive
Analytics software can accurately predict production and prescribe optimal configurations of controllable drilling, completion, and production variables by modeling numerous internal and external variables simultaneously, regardless of source, structure, size, or format. Prescriptive
143:
The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure.
394:
Prescriptive analytics can also benefit healthcare providers in their capacity planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for
182:
Prescriptive analytics uses algorithms and machine learning models to simulate various scenarios and predict the likely outcomes of different decisions. It then suggests the best course of action based on the desired outcome and the constraints of the situation. Prescriptive analytics not only
386:
industry embarks on the necessary migration from a largely fee-for service, volume-based system to a fee-for-performance, value-based system. Prescriptive analytics is playing a key role to help improve the performance in a number of areas involving various stakeholders: payers, providers and
304:
With the value of the end product determined by global commodity economics, the basis of competition for operators in upstream E&P is the ability to effectively deploy capital to locate and extract resources more efficiently, effectively, predictably, and safely than their peers. In
305:
unconventional resource plays, operational efficiency and effectiveness is diminished by reservoir inconsistencies, and decision-making impaired by high degrees of uncertainty. These challenges manifest themselves in the form of low recovery factors and wide performance variations.
322:, there are more than 130,000 electric submersible pumps (ESP's) installed globally, accounting for 60% of the world's oil production. Prescriptive Analytics has been deployed to predict when and why an ESP will fail, and recommend the necessary actions to prevent the failure.
199:
and include objectives constraints, preferences, policies, best practices, and boundaries. Mathematical models and computational models are techniques derived from mathematical sciences, computer science and related disciplines such as applied statistics, machine learning,
114:
which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk, and shows the implication of each decision option. It enables an enterprise to consider "the best course of action to take" in the light of information derived from
131:
Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics. Referred to as the "final frontier of analytic capabilities", prescriptive analytics entails the application of
259:. The data inputs to prescriptive analytics may come from multiple sources: internal, such as inside a corporation; and external, also known as environmental data. The data may be structured, which includes numbers and categories, as well as
317:
In the realm of oilfield equipment maintenance, Prescriptive
Analytics can optimize configuration, anticipate and prevent unplanned downtime, optimize field scheduling, and improve maintenance planning. According to
267:
in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. More than 80% of the world's data today is unstructured, according to IBM.
194:
In addition to this variety of data types and growing data volume, incoming data can also evolve with respect to velocity, that is, more data being generated at a faster or a variable pace. Business rules define the
406:
can improve their negotiating position with health insurers by developing a robust understanding of future service utilization. By accurately predicting utilization, providers can also better allocate personnel.
395:
future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.
178:
The final phase is prescriptive analytics, which goes beyond predicting future outcomes but also suggesting actions to benefit from the predictions and showing the implications of each decision option.
300:
Examples of structured and unstructured data sets generated and by the oil and gas companies and their ecosystem of service providers that can be analyzed together using
Prescriptive Analytics software
1102:
619:
167:
Prescriptive
Analytics extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes, and the interrelated effects of each decision.
247:
by Texas-based company Ayata, the underlying concepts have been around for hundreds of years. The technology behind prescriptive analytics synergistically combines hybrid
1091:
962:
707:
Stewart, Thomas. R. & McMillan, Claude Jr. (1987). "Descriptive and
Prescriptive Models for Judgment and Decision Making: Implications for Knowledge Engineering".
1113:
171:
The next phase is predictive analytics. Predictive analytics answers the question of what is likely to happen. This is where historical data is combined with rules,
1073:
279:
54:
1072:, Kalakota, Ravi, Taylor, James, Lampa, Mike, Franks, Bill, Jeremy, Shapiro, Cokins, Gary, Way, Robin, King, Joy, Schafer, Lori, Renfrow, Cyndy and Sittig, Dean,
623:
801:
370:" as one of two main classes of checkable calculations by dedicated numerical tools and algorithms for verifying safety of ship hull construction.
1174:
1082:
1135:
857:
746:
1306:
1125:
873:
329:, prescriptive analytics can predict and preempt incidents that can lead to reputational and financial loss for oil and gas companies.
659:
223:
One criticism of prescriptive analytics is that its distinction from predictive analytics is ill-defined and therefore ill-conceived.
1192:
94:
70:
1156:
175:, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring.
1234:
1213:
1119:
1183:
1005:
1149:
1142:
1165:
1227:
1105:
644:
Davenport, Tom (November 2012). "The three '..tives' of business analytics; predictive, prescriptive and descriptive".
1296:
205:
399:
companies how much time and money they can save if they choose one patient cohort in a specific country vs. another.
46:
562:
Evans, James R. & Lindner, Carl H. (March 2012). "Business
Analytics: The Next Frontier for Decision Sciences".
326:
835:"PRESCRIPTIVE ANALYTICS Trademark - Registration Number 4032907 - Serial Number 85206495 :: Justia Trademarks"
50:
1255:
1241:
733:, Lecture Notes in Computer Science, vol. 9877, Cham: Springer International Publishing, pp. 245–256,
1311:
383:
140:
and suggests decision options for how to take advantage of the results of descriptive and predictive phases.
834:
226:
116:
620:"Gartner terms Prescriptive Analytics as the "Final Frontier" of Analytic Capabilities | Globys.com"
59:
35:
956:
437:
391:
risk stratified population combining data from the in-facility care episodes and home based telehealth.
137:
133:
39:
902:
Basu, Atanu (November 2012). "How
Prescriptive Analytics Can Reshape Fracking in Oil and Gas Fields".
1316:
1204:
452:
403:
120:
725:
Soltanpoor, Reza; Sellis, Timos (2016), Cheema, Muhammad Aamir; Zhang, Wenjie; Chang, Lijun (eds.),
1069:
477:
447:
256:
201:
153:
528:
63:
1033:
689:
601:
585:
544:
432:
422:
338:
252:
213:
111:
1034:"Advanced Analytics in Supply Chain - What is it, and is it Better than Non-Advanced Analytics?"
1051:
Foster, Roger (May 2012). "Big data and public health, part 2: Reducing
Unwarranted Services".
1301:
932:
Mohan, Daniel (August 2014). "Machines
Prescribing Recipes from 'Things,' Earth, and People".
881:
853:
742:
663:
350:
260:
188:
74:
1194:
Predictive
Analytics in Field Service, Practical Ways to Drive Field Service, Looking Forward
1006:
http://www.ge-energy.com/products_and_services/products/electric_submersible_pumping_systems/
1248:
947:
Basu, Mohan, Marshall, & McColpin (December 23, 2014). "The Journey to Designer Wells".
780:
734:
679:
593:
536:
363:
319:
196:
163:
264:
209:
184:
726:
1290:
605:
548:
1282:
Global Openlabs for Performance-Enhancement Analytics and Knowledge System (GoPeaks)
1269:
693:
342:
738:
802:"Prescriptive versus Predictive Analytics – A Distinction without a Difference?"
457:
442:
346:
283:
Key Questions Prescriptive Analytics software answers for oil and gas producers
1275:
1185:
Predictive Analytics Practical ways to Drive Customer Service, Looking Forward
917:
Basu, Atanu (December 2013). "How Data Analytics Can Help Frackers Find Oil".
482:
467:
379:
296:
172:
684:
667:
349:, and weather conditions. Gas producers, pipeline transmission companies and
1220:
597:
540:
417:
263:, such as texts, images, sounds, and videos. Unstructured data differs from
244:
149:
382:
providers to dramatically improve business processes and operations as the
785:
768:
976:
Mohan, Daniel (September 2014). "Your Data Already Know What You Don't".
427:
1214:"Underground Analytics- The Value of Predicting When an Oil Pump Fails"
1128:
1096:
157:
1107:
Prescriptive analytics is about enabling smart decisions based on data
462:
1235:"What The Frack: U.S. Energy Prowess with Shale, Big Data Analytics"
767:
Riabacke, Mona; Danielson, Mats; Ekenberg, Love (30 December 2012).
1256:"The Future of Big Data? Three Use Cases of Prescriptive Analytics"
1228:"How Prescriptive Analytics Can Reshape Fracking in Oil & Gas"
1126:"The Difference Between Operations Research and Business Analysis"
362:
Common Structural Rules for Bulk Carriers and Oil Tankers (
295:
278:
225:
162:
145:
1270:
INFORMS' bi-monthly, digital magazine on the analytics profession
1118:, I2SDS and Department of Decision Sciences, School of Business,
472:
248:
1093:
Emerging Role of the Data Scientist and the Art of Data Science
230:
The scientific disciplines that comprise Prescriptive Analytics
1173:
Fischer, Eric, Basu, Atanu, Hubele, Joachim and Levine, Eric,
240:
18:
1281:
1017:
Wheatley, Malcolm (May 29, 2013). "Underground Analytics".
769:"State-of-the-Art Prescriptive Criteria Weight Elicitation"
16:
Form of business analytics offering future decision options
239:
While the term prescriptive analytics was first coined by
1078:
International Institute for Analytics (December 15, 2011)
57:
and tools are available to assist in formatting, such as
1251:
Exploration & Production Magazine, September, 2014.
341:
fluctuate dramatically depending upon supply, demand,
1136:"How Big Data is Changing the Oil & Gas Industry"
874:"IBM100 - TAKMI: Bringing Order to Unstructured Data"
818:
584:
Basu, Atanu; Brown, Scott; Worth, Tim (2019-10-25).
1223:
Exploration & Production Magazine, July 1, 2013
1244:Unconventional Oil & Gas Center, June 2, 2014.
991:Presley, Jennifer (July 1, 2013). "ESP for ESPs".
1242:"Science Fiction Now a Fact in the E&P World"
1090:Laney, Douglas and Kart, Lisa, (March 20, 2012).
1164:Ghosh, Rajib, Basu, Atanu and Bhaduri, Abhijit,
1084:Prescriptive Analytics and Data: Next Big Thing?
529:"Five pillars of prescriptive analytics success"
1209:, SAS Global Forum 2012, Paper 165-2012 (2012).
502:Atanu Basu is the CEO and president of Ayata.
8:
1276:"Why Data Matters: Moving Beyond Prediction"
961:: CS1 maint: multiple names: authors list (
38:, which are uninformative and vulnerable to
1176:TV ads, Wanamaker’s Dilemma & Analytics
53:and maintains a consistent citation style.
1191:Brown, Scott, Basu, Atanu and Worth, Tim,
271:Ayata's trade mark was cancelled in 2018.
160:– uses this type of post-mortem analysis.
784:
683:
212:, pattern recognition, image processing,
95:Learn how and when to remove this message
1249:"Your Data Already Know What You Don't"
1150:"The Technology Behind Image Analytics"
848:Inmon, Bill; Nesavich, Anthony (2007).
586:"Predictive analytics in field service"
514:
495:
1158:Analytics and the Future of Healthcare
1152:Analytics. (November / December 2012).
1145:Analytics. (November / December 2012).
1138:Analytics. (November / December 2012).
954:
522:
520:
518:
1206:Bringing Optimization to the Business
1161:Analytics. (January / February 2012).
1143:"Images & Videos: Reall Big Data"
1032:Watson, Michael (November 13, 2012).
829:
827:
727:"Prescriptive Analytics for Big Data"
668:"Data is Dead…Without What-If Models"
7:
1115:Business Analytics Information Event
720:
718:
579:
577:
144:Most management reporting – such as
292:Unconventional Resource Development
45:Please consider converting them to
1230:, DataInformed, December 10, 2013.
1103:McCormick Northwestern Engineering
1087:InformationWeek. (April 15, 2013).
711:. NATO AS1 Subseries F35: 314–318.
709:Expert Judgment and Expert Systems
366:) intensively utilizes the term "
337:Pricing is another area of focus.
14:
1167:From ‘Sick’ Care to ‘Health’ Care
1075:Predictions for Analytics in 2012
731:Databases Theory and Applications
672:Proceedings of the VLDB Endowment
358:Applications in maritime industry
1188:Analytics. (July / August 2010).
1170:Analytics. (July / August 2011).
1148:Venter, Fritz and Stein, Andrew
1141:Venter, Fritz and Stein, Andrew
1120:The George Washington University
402:In provider-payer negotiations,
354:impact of each decision option.
23:
1179:Analytics. (March / April 2011)
1155:Horner, Peter and Basu, Atanu,
850:Tapping Into Unstructured Data
800:Bill Vorhies (November 2014).
327:health, safety and environment
313:Oilfield Equipment Maintenance
49:to ensure the article remains
1:
1200:. (November / December 2010).
773:Advances in Decision Sciences
378:Multiple factors are driving
1258:Datafloq, December 29, 2014.
1182:Basu, Atanu and Worth, Tim,
993:Exploration & Production
880:. 2012-03-07. Archived from
739:10.1007/978-3-319-46922-5_19
364:managed by IACS organisation
1307:Business intelligence terms
1237:WIRED Blog. (January 2014).
1216:DataInformed, May 29, 2013.
275:Applications in Oil and Gas
206:natural language processing
1333:
821:, accessed 4 December 2022
806:Predictive Analytics Times
387:pharmaceutical companies.
374:Applications in healthcare
220:environments may produce.
666:; Tan, Wang-Chie (2011).
368:prescriptive requirements
685:10.14778/3402755.3402802
384:United States healthcare
598:10.1287/lytx.2010.06.03
541:10.1287/LYTX.2013.02.07
949:Oil & Gas Investor
934:Oil & Gas Investor
301:
284:
251:, business rules with
231:
168:
138:computational sciences
108:Prescriptive analytics
1129:OR Exchange / Informs
664:Selinger, Patricia G.
590:The Analytics Journey
533:The Analytics Journey
438:Business Intelligence
299:
282:
229:
166:
1122:(February 10, 2011).
1070:Davenport, Thomas H.
1053:Government Health IT
646:CIO Enterprise Forum
527:Basu, Atanu (2019).
453:Decision Engineering
257:computational models
121:predictive analytics
1254:van Rijmenam, Mark
786:10.1155/2012/276584
662:; Maglio, Paul P.;
478:Operations Research
448:Decision Management
253:mathematical models
202:operations research
1297:Types of analytics
1219:Presley, Jennifer
1212:Wheatley, Malcolm
1081:Bertolucci, Jeff,
433:Business analytics
423:Applied Statistics
339:Natural gas prices
302:
285:
232:
214:speech recognition
169:
112:business analytics
1038:SupplyChainDigest
859:978-0-13-236029-6
852:. Prentice-Hall.
748:978-3-319-46921-8
678:(12): 1486–1489.
351:utility companies
261:unstructured data
189:unstructured data
105:
104:
97:
55:Several templates
1324:
1057:
1056:
1048:
1042:
1041:
1029:
1023:
1022:
1014:
1008:
1003:
997:
996:
988:
982:
981:
978:E&P Magazine
973:
967:
966:
960:
952:
944:
938:
937:
929:
923:
922:
914:
908:
907:
899:
893:
892:
890:
889:
884:on April 3, 2012
870:
864:
863:
845:
839:
838:
831:
822:
816:
810:
809:
797:
791:
790:
788:
764:
758:
757:
756:
755:
722:
713:
712:
704:
698:
697:
687:
656:
650:
649:
641:
635:
634:
632:
631:
622:. Archived from
616:
610:
609:
581:
572:
571:
559:
553:
552:
524:
503:
500:
320:General Electric
243:, and was later
197:business process
100:
93:
89:
86:
80:
78:
67:
27:
26:
19:
1332:
1331:
1327:
1326:
1325:
1323:
1322:
1321:
1312:Formal sciences
1287:
1286:
1266:
1261:
1065:
1063:Further reading
1060:
1050:
1049:
1045:
1031:
1030:
1026:
1016:
1015:
1011:
1004:
1000:
990:
989:
985:
975:
974:
970:
953:
946:
945:
941:
931:
930:
926:
916:
915:
911:
901:
900:
896:
887:
885:
872:
871:
867:
860:
847:
846:
842:
833:
832:
825:
817:
813:
799:
798:
794:
766:
765:
761:
753:
751:
749:
724:
723:
716:
706:
705:
701:
658:
657:
653:
643:
642:
638:
629:
627:
618:
617:
613:
583:
582:
575:
561:
560:
556:
526:
525:
516:
512:
507:
506:
501:
497:
492:
487:
413:
376:
360:
335:
325:In the area of
315:
294:
277:
265:structured data
237:
210:computer vision
129:
101:
90:
84:
81:
69:
58:
44:
28:
24:
17:
12:
11:
5:
1330:
1328:
1320:
1319:
1314:
1309:
1304:
1299:
1289:
1288:
1285:
1284:
1279:
1272:
1265:
1264:External links
1262:
1260:
1259:
1252:
1247:Mohan, Daniel
1245:
1238:
1231:
1224:
1217:
1210:
1203:Pease, Andrew
1201:
1189:
1180:
1171:
1162:
1153:
1146:
1139:
1134:Farris, Adam,
1132:
1123:
1111:
1100:
1088:
1079:
1066:
1064:
1061:
1059:
1058:
1043:
1024:
1009:
998:
983:
968:
939:
924:
909:
894:
878:www-03.ibm.com
865:
858:
840:
823:
811:
792:
759:
747:
714:
699:
660:Haas, Peter J.
651:
636:
611:
573:
554:
513:
511:
508:
505:
504:
494:
493:
491:
488:
486:
485:
480:
475:
470:
465:
460:
455:
450:
445:
440:
435:
430:
425:
420:
414:
412:
409:
375:
372:
359:
356:
334:
331:
314:
311:
293:
290:
276:
273:
236:
233:
128:
125:
103:
102:
85:September 2022
47:full citations
31:
29:
22:
15:
13:
10:
9:
6:
4:
3:
2:
1329:
1318:
1315:
1313:
1310:
1308:
1305:
1303:
1300:
1298:
1295:
1294:
1292:
1283:
1280:
1277:
1273:
1271:
1268:
1267:
1263:
1257:
1253:
1250:
1246:
1243:
1239:
1236:
1232:
1229:
1226:Basu, Atanu,
1225:
1222:
1221:"ESP for ESPs
1218:
1215:
1211:
1208:
1207:
1202:
1199:
1196:
1195:
1190:
1187:
1186:
1181:
1178:
1177:
1172:
1169:
1168:
1163:
1160:
1159:
1154:
1151:
1147:
1144:
1140:
1137:
1133:
1131:(April 2011).
1130:
1127:
1124:
1121:
1117:
1116:
1112:
1109:
1108:
1104:
1101:
1098:
1095:
1094:
1089:
1086:
1085:
1080:
1077:
1076:
1071:
1068:
1067:
1062:
1054:
1047:
1044:
1039:
1035:
1028:
1025:
1020:
1013:
1010:
1007:
1002:
999:
994:
987:
984:
979:
972:
969:
964:
958:
950:
943:
940:
935:
928:
925:
920:
913:
910:
905:
904:Data-Informed
898:
895:
883:
879:
875:
869:
866:
861:
855:
851:
844:
841:
836:
830:
828:
824:
820:
815:
812:
807:
803:
796:
793:
787:
782:
778:
774:
770:
763:
760:
750:
744:
740:
736:
732:
728:
721:
719:
715:
710:
703:
700:
695:
691:
686:
681:
677:
673:
669:
665:
661:
655:
652:
647:
640:
637:
626:on 2016-04-02
625:
621:
615:
612:
607:
603:
599:
595:
591:
587:
580:
578:
574:
569:
565:
564:Decision Line
558:
555:
550:
546:
542:
538:
534:
530:
523:
521:
519:
515:
509:
499:
496:
489:
484:
481:
479:
476:
474:
471:
469:
466:
464:
461:
459:
456:
454:
451:
449:
446:
444:
441:
439:
436:
434:
431:
429:
426:
424:
421:
419:
416:
415:
410:
408:
405:
400:
396:
392:
388:
385:
381:
373:
371:
369:
365:
357:
355:
352:
348:
344:
340:
332:
330:
328:
323:
321:
312:
310:
306:
298:
291:
289:
281:
274:
272:
269:
266:
262:
258:
254:
250:
246:
242:
234:
228:
224:
221:
217:
215:
211:
207:
203:
198:
192:
190:
186:
180:
176:
174:
165:
161:
159:
155:
151:
147:
141:
139:
135:
126:
124:
122:
118:
113:
110:is a form of
109:
99:
96:
88:
76:
75:documentation
72:
65:
64:documentation
61:
56:
52:
48:
43:
41:
37:
32:This article
30:
21:
20:
1233:Basu, Atanu
1205:
1197:
1193:
1184:
1175:
1166:
1157:
1114:
1106:
1092:
1083:
1074:
1052:
1046:
1037:
1027:
1019:DataInformed
1018:
1012:
1001:
992:
986:
977:
971:
957:cite journal
948:
942:
933:
927:
918:
912:
903:
897:
886:. Retrieved
882:the original
877:
868:
849:
843:
814:
805:
795:
776:
772:
762:
752:, retrieved
730:
708:
702:
675:
671:
654:
645:
639:
628:. Retrieved
624:the original
614:
589:
567:
563:
557:
532:
498:
401:
397:
393:
389:
377:
367:
361:
343:econometrics
336:
324:
316:
307:
303:
286:
270:
238:
222:
218:
193:
181:
177:
170:
142:
134:mathematical
130:
107:
106:
91:
82:
71:Citation bot
33:
1317:Health care
1274:Menon, Jai
1240:Logan, Amy
458:Forecasting
443:Data mining
347:geopolitics
245:trademarked
117:descriptive
1291:Categories
888:2023-05-01
754:2023-05-01
630:2014-10-29
510:References
483:Statistics
380:healthcare
185:structured
173:algorithms
154:operations
51:verifiable
1198:Analytics
606:242347282
549:240957300
468:MapReduce
418:Analytics
404:providers
150:marketing
36:bare URLs
1302:Big data
919:Datanami
779:: 1–24.
428:Big Data
411:See also
127:Overview
40:link rot
1097:Gartner
694:6239043
333:Pricing
235:History
158:finance
856:
745:
692:
604:
547:
463:Hadoop
156:, and
60:reFill
819:Ayata
690:S2CID
602:S2CID
545:S2CID
490:Notes
146:sales
34:uses
963:link
854:ISBN
777:2012
743:ISBN
570:(2).
473:OLTP
255:and
249:data
187:and
136:and
119:and
68:and
1278:IBM
781:doi
735:doi
680:doi
594:doi
537:doi
241:IBM
1293::
1036:.
959:}}
955:{{
876:.
826:^
804:.
775:.
771:.
741:,
729:,
717:^
688:.
674:.
670:.
600:.
592:.
588:.
576:^
568:43
566:.
543:.
535:.
531:.
517:^
345:,
208:,
204:,
152:,
148:,
123:.
1110:.
1099:.
1055:.
1040:.
1021:.
995:.
980:.
965:)
951:.
936:.
921:.
906:.
891:.
862:.
837:.
808:.
789:.
783::
737::
696:.
682::
676:4
648:.
633:.
608:.
596::
551:.
539::
98:)
92:(
87:)
83:(
79:.
77:)
73:(
66:)
62:(
42:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
