In this
post, let’s read out a business problem differently. Let’s read out the data
visualization problem in a text story board format. This post takes some liberty to take excerpts from blogs and web sites to create a compelling case for data visualization,
the skills around it and the tools that help you attain incredible business
insights.
Chester Liu[1]:
“When
it comes to the topic of Big Data I have to make a public admission. I have a
split personality. On the one hand the geek in me, from years spent as a software
engineer, relishes the challenge of installing my own Hadoop cluster, writing
MapReduce algorithms, and running all sorts of performance tests to see for
myself how amazing the technology is. On the other hand, as a pragmatic product
marketing manager …, I just want to get stuff done and understand my data ASAP,
without writing a single line of code.”
Peter
Wayner[2]: “Understanding the data and finding the
right question to ask is often much more complicated than getting your Hadoop
job to run quickly. That's really saying something because these tools are only
half of the job.”
Ben
Werther[3]:
“Imagine what is possible. Raw data of any kind or type lands in Hadoop with
no friction. Everyday business users can interactively explore, visualize and analyze
any of that data immediately, with no waiting for an IT project. One question
can lead to the next and take them anywhere through the data. And the
connective tissue that makes this possible — bridging between lumbering
batch-processing Hadoop and this interactive experience — are ‘software
defined’ scale-out in-memory data marts that automatically evolve with users
questions and interest...”
Peter
Wayner[2]: “Many of the big data tools are also
working with NoSQL data stores. These are more flexible than traditional
relational databases, but the flexibility isn't as much of a departure from the
past as Hadoop. NoSQL queries can be simpler because the database design
discourages the complicated tabular structure that drives the complexity of
working with SQL...”
Stefan
Groschupf[4]
: “With Hadoop, there was no limitation of storage and compute anymore and
we felt that machine power could be used to overcome the slow, cumbersome,
manual processes like ETL or data modeling that always gets in the way of
finding insights.”
For
instance “Platfora’s core concept is probably the “lens”, which is a
snowflake-schema mini data mart materialized onto Platfora’s servers via a
Hadoop job. A lens is meant to be used in-memory but can certainly be large
enough to spill onto disk, which is why I call Platfora’s data store
“memory-centric ” rather than “in-memory”. A lens is a lot like a materialized
view, including in that it’s incrementally maintained…
- You have data in Hadoop.
- You extract data into a memory-centric data
store.
- You can do drilldowns back into Hadoop.
- …through a browser, thanks to HTML5.*” asserts Curt Monash[5]
Infoactive.us[6] says: “The
cool thing about exploring data is that you can play with numbers and learn by
piecing together different variables. Don’t trap your data in a static image.
Let it loose and help others find their stories in the numbers. Make your data
fun.”
Andy
Cotgreave [7] : “…Each of these visualizations
have common themes: - They were designed to create
change… Whether it's improving Sales or finding cures for diseases, visualizations
help people make decisions based on data.
- The people who created these
visualizations were passionate. In order to make change,
you need passion too.
- The visualization is not the
whole story. A visualization itself can not stand alone. The
change achieved by these visualizations came about because their designers
went out and pushed their views, supported by these visualizations. If you
want to make change, your visualization also needs to be promoted by you.”
Qunb[8] : “It’s not just about big data. It’s also about
the incredible details.”
Peter
Wayner[2]:
“These all bear investigation, but the software is the least of the
challenges.”
Susan Puccinelli[9]: “There’s really no
excuse to not get started working with whatever data you have today.”
No comments:
Post a Comment