I've used Sunbursts to do the same with keyword portfolios. No better way to optimize for all of search behavior, rather than the absolutely silly obsession with a few keywords (it is fatal when apply to single session conversion scenarios!). The sunburst visual of our oil consumption is nice. But you can see how much more powerful Sunbursts can. Learn how to use them from this tutorial, which is linked off my most beloved data visualization source d3js. One last nice visual from our friends at Stats Monkey.
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Additionally you can click on any country and just look at that one. Better than the table, but perhaps less optimal than the Treemap. I want to use the above visual to share with you how much i adore the sunburst visualization. I believe it is best at describing sequences of events. It is best demonstrated in the example below, which illustrates the path followed by a group of people on a website. You get a confusing little thing, but the visualization is interactive. You simply move your mouse and it illuminates the journey and how many people follow a particular path. For example you can configure what the end is, in my case the end is people who converted. Now I can quite grey literally follow the path to every conversion. I can find the biggest pools of customers who share a behavior and go back and optimize my campaign strategy, my content strategy and indeed my overall digital strategy.
If you are interested in any particular channel, miscellaneous as an example, you can click on it and boom! You see the big ones named, the hidden mysterious ones, you can unmask using a mouse hover. It would be nice to see all the sites named, but it is kind of nice that it forces you to internalize the big ones, likely where you can have the biggest impact, and then look at the small ones. A delightful way to take your 198 row table and present it in a manner that aids stronger understanding of performance. Let's go back to our table, and global oil consumption. Stats Monkey also presents that table using the sunburst visualization Perhaps compared to the Treemap, this visual shows fewer countries and fewer actual numbers of oil consumption due to space limitation. You can still hover your mouse and get the details eksempel of each country.
Three cool benefits:. Treemaps are a great way to report visualize a lot of information. They are really good at showing the differences in the big head and the long tail. They can form the foundation of allowing data consumers to drilldown into the represented segments. One of my favourite implementations of Treemaps is in the competitive intelligence tool Compete. It shows all the incoming traffic to a site as a treemap. At a glance you can see all the big clusters of sources (close to the channels view in google Analytics). You can hover over each box to get a sense of the key metrics. Number of visits, percentage of share of total visits and the percentage change (which you can discern from the color of each box, in that sense the compete Treemap does not use color just for decoration).
Usa!) are more clearly visible, and you get a much stronger sense of proportions. Yes, you could see in the table that the us was bigger than China, but the Treemap really brings the comparison home. You start to see weird things like russia and India are the same. Yes, it was in the table. But for a visual person like me, this is the ah-ha moment. While you can't see the smaller consumers all that easily, you can hover your mouse and see the details. Additionally, you can go down to the little ones, now that you have the ability to easily do that, and point and hover.
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You do you best to understand what is going. Yes you see the numbers, there are lots of them. You scroll up and down. Some countries use a lot of oil. That's just the top 12 rows, there are another 196 rows of data.
But how can you be expected to understand it all? How can you understand enough to at least pick directions you want summary to go down? The table above is from Stats Monkey. Their approach is to actually present the data using a treemap (they call it a squaretree for some reason). It is so much better! You can suddenly see the forest and the trees. (Get it?) The few dominating countries (USA!
You are welcome to read them all at once (warning: once you start you won't be able to stop! or you can consume them one at a time. For four of the examples, i'll also share how the visualization inspired me to apply the lessons to my web analytics data. In the other two, i'll ask for your help in how you might connect the inspiration to your work as a marketer/Analyst. Short story 1: Treemaps, sunbursts, packed Trees, Oh My! Short story 2: Predictive modeling, quantifying Cost of Inaction.
Short story 3: Streamgraphs, data Trends diving Made simple! Short story 4: Multi-dimensional Slicing and Dicing! Short story 5: Segmented Stacked, square, charts. Short story 6: Conditional Formatting, simple Strategies to a drive big Focus! Six stories, a total of eleven different data visualization techniques to inspire you to think different at work when you play with data. Our lives are dominated by columns and rows. And sometimes they are indeed optimal: 7 Data Presentation Tips: Think, simplify, calibrate, visualize. You'll also see examples below. So a table like this one is par for the course for you.
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Diseases that kill. Data visualized is data understood. It delivers world peace! I'm exaggerating a tiny bit. (As is clear from the discussion on the preference of guns over knowledge in 37 us states. But at least we're talking.). In this post, i want to share some examples of data visualization I was excited about recently. In each case the creator did something interesting that made me wonder how I can use their strategy in my daily efforts in service of digital marketing and analytics. We will look at six short stories.
It spun off into an independent organization in 2015 with the solutions goal of bringing Hyper to industry and shipping a commercial version of the technology. Hyper was acquired by tableau in 2016; the core technology now powers the tableau data engine. Like a vast majority on planet Earth, i love data visualizations. Ok, so perhaps as the author of two bestselling books on analytics I love it a little bit more! There is something magical about taking an incredible amount of complexity and presenting it as simply as we possibly can with the goal of letting the cogently presented insight drive action. A day-to-day manifestation of this love is. Google or, facebook profiles where 75 of my posts are related to my quick analysis and learnings from a visualization. Be it looking.1 million fcc net neutrality comments, things people around the world identify as their biggest threat, water consumption of a burger patty. Daily cooking, the religious gap on spanking children, or a simple graph that rises profound questions about where we donate.
query, it creates a tree, logically optimizes the tree, and then uses it as a blueprint to create a unique program, which is then executed. The end result is better utilization of modern hardware for faster query execution. Leveraging more of your hardware: morsel-driven parallelization. We designed Hyper from the ground up with large, multi-core environments in mind. Our parallelization model is based on very small units of work (morsels.) These morsels are assigned efficiently across all available cores, allowing Hyper to more efficiently account for differences in core speed. This translates into a more efficient hardware utilization and faster performance. Hyper began as an academic research project at the technical University of Munich (TUM) in 2010.
In short, hyper delivers fresh data, faster—so you can analyze a larger, more complete view of your data. Rethinking system architecture: one state for transactions and analytical queries. With fruit Hyper, transactions and analytical queries are processed on the same column store, with no post-processing needed after data ingestion. This reduces stale data and minimizes the connection gap between specialized systems. Hyper's unique approach allows a true combination of read-and write-heavy workloads in a single system. This means you can have fast extract creation without sacrificing fast query performance. (We call that a win-win.). A new approach to query execution: dynamic code generation. Hyper uses a novel just-in-time compilation execution model.
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Hyper is a high-performance in-memory data engine technology that helps customers analyze large or complex data sets faster, by efficiently evaluating analytical queries directly in the transactional database. A core tableau platform technology, hyper uses proprietary dynamic code generation and cutting-edge parallelism techniques to achieve fast performance for extract creation and query execution. Hyper's unique design, over the past decade, in-memory data engines and analytical database technologies have delivered incredible query performance improvements through techniques such as sampling and summarization. These performance improvements come at a cost, however. Many systems sacrifice write performance—critical for fast extract creation and refresh performance—in favor of optimizing analytical workload performance. Poor write speeds lead to stale and disconnected data. A lag between people and the data they want to analyze. Our eksempel mission with Hyper is to bring people closer to their data by giving you fast write speed and fast analytical workload performance.