One of the best things about doing our annual Top 10 Trends presentation is the discussion we have amongst ourselves here at Tableau. It's a good chance to sit down and take stock of the market.

As anyone working with data these days knows, there is a massive wave of innovation in analytics today. Hundreds of companies from startups to Amazon and Google are making breakthroughs. In the best spirit of innovation they are building on each other's work to make astounding progress on new concepts like NoSQL databases and cloud data warehouses.

But technology itself is never the whole story. People and organizations must absorb innovation to make it relevant. We see that happening at a mass level with some concepts that have been around for years, such as Agile Business Intelligence.

So after much discussion, here are our Top 10 Trends in Business Intelligence for 2014.

Here's the list:

  1. The end of data scientists.
  2. Cloud business intelligence goes mainstream.
  3. Big data finally goes to the sky.
  4. Agile business intelligence extends its lead.
  5. Predictive analytics, once the realm of advanced and specialized systems, will move into the mainstream.
  6. Embedded business intelligence begins to emerge in an attempt to put analytics in the path of everyday business activities.
  7. Storytelling becomes a priority.
  8. Mobile business intelligence becomes the primary experience for leading-edge organizations.
  9. Organizations begin to analyze social data in earnest.
  10. NoSQL is the new Hadoop.

Click through the slides to learn more about each trend. This year each trend includes a link to a report or a video where you can find more information.

With which predictions to you agree? with which do you disagree? Let us know in the comments.


There will always be data scientists; whether using database queries or being power users of tools like Tableau - you need to know real math to do Predictive Analytics even with great software. My guess is at least two or three years before the end user predictions really start to take hold; lots of barriers including lower price points, wider scale adoption, and a main street corporate culture that encourages and rewards adding this kind of value.

Big Data will be the fashion statement; Organisations will invest in BD and figure out that the ROI is disproportional to the cost. This however does not mean a decline of BD. BD will start maturing well and real tangible implementations will be identified and executed

Nice agree with most of it but gotta disagree with #1 and #3. I don't see data scientists going anywhere, especially with the increased need for analytics with regards to big data. Speaking of big data... It can be useful if you have it and if you need it, I don't think most organizations a) have big data or b) have need of the big data they have - certainly there are some that do and that can but big data is far too over hyped as it is.

Nice summary Ellie.

Here's my opinion on "data scientists".


Please, please, stop confusing data analysts with data scientists. Two different beasts, and the latter definitely have a stronger statistical skill set. If more people are going to venture into data analysis, from my experience of the ways things can go horribly wrong without any statistical background, then you've just argued why there are going to have to be MORE data scientists, not less.

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Thanks all for the commentary on Data Scientists. @Crystal, I agree with you that Data Scientists tend to have much more specialized skills. The point is a bit exaggerated, by design. I don't believe the need for advanced data knowledge will go away-- in contrast, we see more an more people adopting sophisticated techniques as well as tools like R. The point is more that general data skills will be necessary throughout the ranks of business people.

@Paul, thanks for the link on the mindset of data scientists, it's a good read.

I strongly disagree with point 1. Data scientists (whether called that in name or not) are absolutely critical to supporting Accidental Analysts and Data Enthusiasts in a successful business. While Ellie softened the position in the comments, my fear is that executives will be miseducated from the headlines and press releases, likely missing the comments here.

As someone who has trained numerous people in Tableau since 2009, there have been very few that would not have benefited from ongoing support of advanced analysts and statisticians.

There should be a natural synergy between Accidental Analysts (line of business subject matter experts) and data scientists. The key is data scientists that are well focused on high-value business problems where optimization and automation will yield high returns that won't be achieved by ad-hoc work that is familiar to Accidental Analysts. Now that Tableau has added initial R integration, this yet another venue where working closely with data scientists could yield great synergy.

In short, to maximize ROI from data in a company, I advise my clients that you need a healthly ecosystem of data scientists, BI teams, Accidental Analysts, data warehouse teams all working together with leadership from the CIO/CDO and line of business execs.

Best regards,
Stephen McDaniel

I like Stephen Few's take on the 'data scientist' - it's just varying degrees of skills for data sensemakers:
"...There is no general agreement about the meaning of the term. Despite the good intentions of some who use it, such as William Cleveland when he first introduced it in 2001, it has led to confusion through freewheeling, self-serving use. When all is said and done, deriving value from data comes down to effective data sensemaking, resulting in greater understanding and better decisions. Regarding those who do the work, it only matters that they’re qualified to do it well. If you’re a hiring manager looking for someone to glean meaningful insights from your data, keep in mind that the title “data scientist” on a resume means nothing. IT titles are notoriously inflated.
...Most of all, never forget that only people can make sense of data. At best, tools can augment the abilities of talented people. There is indeed a science to data sensemaking, but data science by any other name (and there are many) would smell as sweet."

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The end of data scientists.

Seriously don't see this happening, not even close. I think this is more of a "Hey Tableau helps keep things simple, and there isn't a huge need for Data Scientists with Tableau." While that might be true, yo simply cannot get to the solutions and maturity needed in mathematical models that business need with Tableau. I think this #1 here is simply trying to downplay their importance by substitution them for a ease-of-use tool.

Big data finally goes to the sky.

BIG Maybe. A very large amount of organizations are still wrestling with a large multitude of complexity issues around Big Data, and that is even after they've got the tools and data to start analyzing.

Agile business intelligence extends its lead.

While this might be true to a generic extent, it is worthless unless the lead time actually allows a business to make more agile decisions that ultimately allow for agile implementation of strategies that really help a business. Simply slapping dashboards together more quickly so IT can claim it is working a bit faster is a shallow win compared to taking it all the way through a corporate strategy that allows a big win.

Predictive analytics, once the realm of advanced and specialized systems, will move into the mainstream.

And it will show it's truest and most capable colors when used by people that understand statistics, not 1 click forecasting.

Storytelling becomes a priority.

This looks like a Tableau feature being pushed. Frankly, if content of any kind is presented well (or a "story being told") you've lost your audience anyway. This god back to the dawn of mankind lol!

Organizations begin to analyze social data in earnest.

Every single CPG, product, automotive, and pharma company I've ever dealt with in the past 18 months is already there, so this is kind of late. They're well past beginning. FB wouldn't have much revenue if smart companies that know how to analyze the social data didn't pay for ads.

I guess this was interesting, not sure I could do much better though :)

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