SharePoint started in 2001 with the modest goal of tying the Office Suite (with a Word-like GUI, but prophetically including much of Excel) to the internet. The "second" goal was to evolve inTRAnet capabilities and prepare for web based document management ala XML.
Fast forward 12 years to Hadoop, DAX - Pivot, and an amazing array of Excel tools for big data mining and analysis. Microsoft was truly smart or lucky-- Sharepoint is now the Excel/office suite/ SQL integrator, with the newest two versions specifically targeting many other big data interfaces. And yes, this fine text fully covers the hottest 2 new topics in BI-- apps and clouds (even though there are many separate volumes on apps, eg: Microsoft SharePoint 2013 App Development).
Keywords for those two aspects: Microsoft NAPA (IDE), Windows Azure, Office 365, REST API's, YAMMER. (BTW these authors are pro Microsoft, so they don't get into the .net/.com environmental nightmares, the lack of VBA updates and support, or the impossible snafu of macros in hosted Office/Sharepoint. However, NAPA IDE solves almost ALL those issues PLUS smooths your cloud/apps hosting issues-- it is a MUST with these technologies, as much as Excel).
If you read the literature about new grads unable to find jobs except at McD's, the one exception is a new category of job and profession called "Data Scientist" which started being used as late as 2009/2010. There now even is a CDSO-- Chief Data Science Officer-- in some data intensive firms! IF your team doesn't yet have a CDSO, tell them to create the position and give it to you, especially if you're reading this review and considering purchasing this book. Entry data science: $90K range; Manager: $165K; CDSO: $220K including options, etc. (Source: DS assn.).
The problem isn't day one when you get that trophy job, it's day two. Maybe the CEO likes your work for the board, or the CFO for financial reporting dashboards, but what about the other top managers? SharePoint is the answer! Although the newer servers and warehouses certainly require professional IT skills, the data scientist can use the (still) extremely friendly interfaces, as well as team-familiar and friendly Word, Visio and Excel. IOW, you can be a HERO day 2 by creating tools and dashboards that show the "value" of a data scientist to the organization at EVERY level. One cool, very current emphasis is on "self service" BI with Visio and Excel-- your customers will LOVE it!
So, since every data scientist and most IT folks know that the premier text in this field is Young and Klindt (Professional SharePoint 2013 Administration), why do you need 400 more pages? Specialization! Excel, SQL, big data, Pivot/DAX, Hadoop deserve way more than a chapter, and this fine text fills both a learning and reference need in that deep and specialized application, which BTW IS the major reason "Data Science" has evolved into a separate profession.
If you're confused about the title "Business Intelligence" -- don't worry, data science is even now struggling to define the term with 3 bears taxonomy-- not too big or too small, but just right. In olden days it referred to what your competitors are doing and maybe a little about what your customers are doing. With web x.y it is now all about the myriad aspects of big data, from machine learning to data mining, analysis and presentation, with a BIG TECHNICAL piece filled by statistics, math and probability. DAX, Hadoop, Excel, Powerpivot, Sharepoint all fit in both the "behind the scenes" back room servers (and indeed now Sharepoint "farms") and the visible dashboards, functions, Excel/ Visio BI features and plug ins and other organizational user's daily life-- the tip of the iceberg of BI above the waterline that will make or break your DS/IT career!
The only real question in considering purchase of this unique and current text is the amount of overlap with Klindt-- the ONE you MUST have if you're in DS. After carefully comparing both, we're advising our technical, university, grad and private corporate libraries that they do indeed need both. The reason: about a 22% overlap. Yes, that seems high, but the remaining material has so much reference detail, specifically about interfaces, that every data scientist will need this. This volume covers the majority of client/server options right down to the IDE and NAPA level. These are crucial for the cloud and apps regardless of how you're configuring Share vs. Pivot vs. SQL big data tools, for example. Of course this volume is 80% about Excel - Visio BI tools, as that's SharePoint's sweet spot. Not knocking that-- it is what your DIY users will like about it!
If you are a corporate librarian and your major specialty is document management, you MIGHT get away with just Klindt, but I will predict that your job will evolve into a data scientist either because you're smart and you make it do so, or due to inevitable trends, at which point you'll need the "rest of the story" as given here in fine fashion. Perhaps most importantly, the authors take the time to parse the similarities and differences between the exponentially growing tool options. With just the Microsoft "competing" options in sql, pivot, excel, not to mention dozens others in SAP/IBM etc., knowing which tool to choose for which application is key, and the authors here give an objective trade-off view of similarities and differences by application and solution/need.
If you're on a strict budget, can you get away with a 2012, much cheaper substitute? If it were just for Sharepoint, pivot, DAX etc., possibly, but Excel itself has so many new 2012/2013 hooks in SharePoint that you really do need the updates both here and in Klindt. I write some of the questions for the Microsoft exams, and ahem, you need this, enough said!!! Speaking of budget, Microsoft is not going to advertise that you can avoid a $15,000 feature upgrade with a $500 third party plug in for SP -- say, for example, if you only need some custom filters. Just a thought.
Tip of the day: did you "mistakenly" go into a field like math, stats, Monte Carlo, probability, Bayesian analysis, actuarial science, etc. that now seems like a dead end? CELEBRATE-- reinvent yourself as a data scientist, because these fields are integral to what "lies beneath" this new field! Go for it. Imagine the differing needs between a research organization using R and Google using Hadoop-- the common thread is YOU, the data scientist! If you mastered a field above, olap hypercubes will seem simple by comparison (hey, you'll be the only one on the team who knows how to model them with Minkowski sums!).
EMAILERS: OK, I KNOW a lot of you are peeved at Microsoft for legacy, VBA support, Pivot vs. Share vs. SQL, out of date libraries, and other issues. But if you are truly going the DS route consider this: with that hat on, you also have to consider COST! I mean you might be asked to design a BI system ground up ON a budget. You KNOW that the MS solutions are the biggest bang for the buck if your "customers" (internal users) need a friendly mining and analysis app like Excel! And, believe me, I've been there, even the multi million dollar "SAP" type enterprise solutions have a plethora of issues too, you just PAY MORE for them. You'd be better off with R / Python, as the pharma companies are finding. Microsoft: get a clue, and get back to supporting and updating VBA you goofballs!!!! C# or not, Excel itself depends on it. If not for NAPA, .net would have brought the whole thing down by now! (OK, not many can afford even run time Oracle, so you get points for SQL). That IS the point.
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