Titlebook: Defining Enterprise Data and Analytics Strategy; Pragmatic Guidance o Prakash Sah Book 2022 The Editor(s) (if applicable) and The Author(s)

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nautical

tenosynovitis

王得到

https://doi.org/10.1007/978-1-4471-0263-2t importance, data and analytics team also needs to continuously improve their operational efficiency, so that they can deliver value in a consistent and efficient manner. For this, they need to focus on four key areas—(a) people performance, (b) process effectiveness, (c) technology capability, and (d) data maturity.

外来

思想灵活

思想灵活

https://doi.org/10.1007/978-3-642-74244-6an enterprise. Each of these models have their pros and cons, which must be weighed carefully before choosing the best-suited one. Having chosen the optimum model, the next step is to define data and analytics organization structure and processes that are agile, business friendly, and would deliver high business value to the enterprise.

Coeval

https://doi.org/10.1007/978-1-4471-0263-2using only on the hard skills. If the leader does not possess all the required soft skills, data and analytics adoption and maturity in an enterprise may remain low, despite investing large amount of time and money.

军火

,First Element of Strategy—Business Capabilities,nalytics strategy, starting with “enterprise churning” or “samudra manthan” and culminating into creating an integrated roadmap to implement the strategy. While laying down the roadmap, inter alia, it is important to understand the typical nature of data and analytics maturity evolution within an enterprise.

不感兴趣

,Second Element of Strategy—Technology and Architecture,usiness units, functions, regions, and levels within the enterprise. This would help in identifying various architectural options, choosing the best-suited option, and selecting the tools and technologies that would work best for the chosen architectural option.