BBBT

[...] CIOs of big data report clear advantages of seriously considering and choosing the cloud for data analysis. These CIOs make a clear distinction between business critical and business enabling systems [...]

[...] Mashing up multiple data sources to generate a single source of truth is an integral part of data analysis. It allows you to compare and cross-reference records stored in different formats and [...]

[...] purposes of this analysis, the term “Big Data” is comprised of the four skill sets of data analysis, data acquisition, data mining and data structures. The WANTED Analytics taxonomy [...]

[...] , along with some common verbiage to describe each skill: Analytics Skills: “Passion for data analysis supported by personal and professional experiences”, “Strong familiarity with statistical [...]

[...] response to those requests. To recap, the overall architecture consists of 3 major components: Data Sources; Core Data Warehousing; and, Core Analytics. Data Sources – This element covers all the [...]

[...] sales force are even more interested in big picture information coming from massaging large data sources. The folks in the new arenas such as Hadoop need to realize that they are complementary to [...]

[...] data such as documents, videos, images, voice records etc. Combining these different data sources and putting it into context in order to get, real-time, insights is a big challenge for [...]

[...] is in an enterprise (and how the culture changes over time) and the volume and variety of data sources (this can be change at the rate of how the enterprise grows and maneuvers), it is very hard [...]

[...] teams, instead of a separate department of data scientists. This is of course only attainable by large organizations, which are able to hire multiple [...]

[...] way of doing it is to “Hir[e] fewer but more skilled personnel such as data analysts and data scientists.” Those are probably the folks in the middle bullet above, who think that a priesthood can [...]

[...] hats really enable such lateral thinking or am I talking through my hat? And where do data scientists come into this argument? An average data-scientist does have to wear multiple hats to do his [...]

[...] need for employees skilled in Big Data. Every application now needs some form of data analytics so it can identify and send the right data to those who need it the most. Organizations are [...]

[...] platform is the one that creates the most value over time and that probably means that the data analytics, those that can use the data to create these data-driven feedback loops, will be the winners. [...]

[...] completed of Big Data hiring trends using WANTED Analytics, the leading provider of data analytics on the workplace.  For purposes of this analysis, the term “Big Data” is comprised of [...]

[...] the reliance on coding will be reduced, so the industry will be open to a wider skillset. A “Data Scientist” with a BA degree? Not out of the question. Real Time Big Data In-memory analytics [...]

[...] weight of need overbalanced to business categories, not the mystically names and overpriced data scientist. The report has a very good summary of how respondents are trying to overcome the challenges [...]

When Edward De Bono spoke about the six thinking hats way back, he definitely did not visualize the emerging field of data science. The hats were mean [...]

[...] operations. Despite the increasing popularity of Executive Dashboards, Infographics, Data Warehousing and Business Intelligence tools in general, there are still far too many businesses whose [...]

[...] first online training video based on my very popular white paper and talk: Agile Methods and Data Warehousing: How to Deliver Faster. Most of you will agree that data warehousing and business [...]

[...] To begin at the beginning Hold this thought: If Data Warehousing was Tesco then Big Data would be the “try something different”. Since the publication of the [...]