國立虎尾科技大學 |

From data to insights = the strategy of a data analytics team /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	From data to insights/ by John Mackay.
其他題名:	the strategy of a data analytics team /
作者:	Mackay, John.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xiii, 118 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Business Process Management. -
電子資源:	https://doi.org/10.1007/978-981-96-3545-0
ISBN:	9789819635450

From data to insights = the strategy of a data analytics team /
Mackay, John.

From data to insightsthe strategy of a data analytics team /[electronic resource] :by John Mackay. - Singapore :Springer Nature Singapore :2025. - xiii, 118 p. :ill., digital ;24 cm.

Chapter 1. Introduction: Day 1 Strategy -- Chapter 2. Leadership -- Chapter 3. Building a Team -- Chapter 4. Data Sources -- Chapter 5. Data Processing -- Chapter 6. Other Data Considerations -- Chapter 7. Stakeholders -- Chapter 8. Reporting and Insight -- Chapter 9. Strategy -- Chapter 10. Conclusion.

From Data to Insights is a practical guide to building a strategy that addresses the common issues faced by Data Analytics teams. Not enough time, too many requests, unhappy stakeholders, and colleagues with low team morale are just some of the problems caused by poor strategy and design. This guide will help managers avoid these pitfalls and improve team performance by focusing on long-term goals and strategic planning, ensuring your Data Analytics team can effectively support their business and its stakeholders. Practical examples and explanations are provided to help understand how a Data Analytics team's strategy can be implemented and why certain approaches will or won't work.

ISBN: 9789819635450

Standard No.: 10.1007/978-981-96-3545-0doiSubjects--Topical Terms:

1066351
Business Process Management.

LC Class. No.: HF5548.2

Dewey Class. No.: 650.0285

From data to insights = the strategy of a data analytics team /
LDR:01971nam a2200325 a 4500 001 1160670
003 DE-He213
005 20250303115243.0
006 m d
007 cr nn 008maaau
008 251029s2025 si s 0 eng d
020 $a 9789819635450 $q (electronic bk.)
020 $a 9789819635443 $q (paper)
024 7 $a 10.1007/978-981-96-3545-0 $2 doi
035 $a 978-981-96-3545-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HF5548.2
072 7 $a KJQ $2 bicssc
072 7 $a COM005030 $2 bisacsh
072 7 $a KJQ $2 thema
082 0 4 $a 650.0285 $2 23
090 $a HF5548.2 $b .M153 2025
100 1 $a Mackay, John. $3 1487725
245 1 0 $a From data to insights $h [electronic resource] : $b the strategy of a data analytics team / $c by John Mackay.
260 $a Singapore : $c 2025. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xiii, 118 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Introduction: Day 1 Strategy -- Chapter 2. Leadership -- Chapter 3. Building a Team -- Chapter 4. Data Sources -- Chapter 5. Data Processing -- Chapter 6. Other Data Considerations -- Chapter 7. Stakeholders -- Chapter 8. Reporting and Insight -- Chapter 9. Strategy -- Chapter 10. Conclusion.
520 $a From Data to Insights is a practical guide to building a strategy that addresses the common issues faced by Data Analytics teams. Not enough time, too many requests, unhappy stakeholders, and colleagues with low team morale are just some of the problems caused by poor strategy and design. This guide will help managers avoid these pitfalls and improve team performance by focusing on long-term goals and strategic planning, ensuring your Data Analytics team can effectively support their business and its stakeholders. Practical examples and explanations are provided to help understand how a Data Analytics team's strategy can be implemented and why certain approaches will or won't work.
650 2 4 $a Business Process Management. $3 1066351
650 2 4 $a Business Information Systems. $3 669204
650 1 4 $a Business Analytics. $3 1387864
650 0 $a Management information systems. $3 561123
650 0 $a Data mining. $3 528622
650 0 $a Quantitative research. $3 635913
650 0 $a Business $x Data processing. $3 565427
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-96-3545-0
950 $a Computer Science (SpringerNature-11645)