Page 21 - Demo

Computing Complexity Challenges
Traditional options to share data also require scaling complex computing platforms to share even small slices of data. Complex- ity adds burdens and requires extra resources, including infra- structure­costs —­internally­and­externally.
The­goal­should­be­effortless­sharing­of­limitless­amounts­of­data­ with internal and external organizations, including your business partners, for collaboration and business planning. If your busi- ness­ model­ is­ focused­ on­ monetizing­ your­ data,­ you’ll­ want­ the­ same­level­of­effortless­sharing­to­distribute­data­to­as­many­data­ consumers as possible, with individualized, self-service access and security as needed.
If you think cloud storage is the answer, think again. Sharing data using­a­basic­cloud­storage­service­is­inefficient.­It­won’t­provide­ the ability for you or your data consumers to query the data in a high-performance manner or ensure data consistency. A Hadoop computing platform is not the answer either because of its inher- ent complexities and complications.
Conventional Data Sharing: Business Pain Points
Cumbersome and complex data sharing methods combined with costly­and­inflexible­computing­platforms­produce­headaches­for­ organizations that need to collaborate on data. In addition, the processing overhead required to extract data from a traditional data warehouse and transfer that data to other organiza- tions delays the value shared data provides. Additionally, every time data changes, data extraction and transfer processes must be repeated because shared data is always a static version and becomes stale immediately.
Within an organization
Data sharing scenarios within an organization include:
» Sales groups share data with finance groups to track sales and revenue to forecast an organization’s performance.
CHAPTER 2 Understanding Traditional Data Sharing Challenges 15 These materials are © 2018 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.