Your Data Analysis Takes How Long?

Reference: www.biblogg.no

Av

Andy Cotgreave

– 13. oktober 2013

Publisert i: Forside, Toppsak, Trender, Utvalgte saker

 

Andy Cotgreave, Social Content Manager at Tableau Software, looks at how analytics tools can help to save valuable time

Imagine, for a moment, that you’ve been given a task to analyse a dataset inside sixty minutes and share your results. How far do you think you would get in that time?
It’s a question I had cause to reflect on recently, after running «Fanalytics», a workshop for users of Tableau Public. In the workshop, we gave people a dataset and one hour to do something cool. Their results were astounding.
To understand why they were so impressive, let me provide a little context by considering how many of us work with data.
First, let me dispel a myth. Contrary to popular opinion, if you are using spreadsheets or traditional BI tools, it is quite possible to build beautiful charts. Unfortunately, each view of your data takes considerable time to build, Do you have that time to spare in your working life? What if the chart you take 10 minutes to build doesn’t answer your question? What if it inspires a new question? You have to go back and start again.
What if you could explore your data at the speed of thought instead? What if each mouse click changed the view instantly? This is what we call visual analytics: it allows you to find insight in your data at speeds unimaginable just a few years ago.
You’re probably wondering how all of this relates to the Fanalytics competition I mentioned earlier. Well, during the session, we gave our teams a list of every UK Number one album since 1956, downloaded from Wikipedia. The instructions they were given were to analyse the data and publish something interesting within one hour.
Did they deliver? Oh, boy, yes, and in ways that made my jaw drop. Each entry was different. The winning team analysed albums that had been to number one more than once, revealing perennially popular music, and the effects of sales on a musician’s death. Another team came up with an album explorer that found out which album was number one on your birthday. One team created a visually gorgeous dashboard, sure to engage anyone. A further team came up with a predictive model based around the likelihood of any album title to get to number one. You can see all the entrants on Tableau’s Fanalytics blog post. What was truly amazing was that they did this in one hour. Sixty minutes!
Unfortunately, many people are stuck with tools that are cumbersome or too hard to use. It often takes more than an hour just to connect to data. The simple lesson I’ve learnt from the recent session is that although some tools can make amazing charts, they are often unnecessarily complicated. With some, you need to fill in five steps in a property wizard just to draw a chart. In others, you are required to write custom scripts before you can start drawing anything.
The question we need to ask is whether we are using the right tools to answer questions quickly and in the most efficient way? If not, then perhaps it’s time to ditch these time hogs and focus on analytic tools that save you time instead!

Interview with Jill Dychè on Data Management in the Era of Big Data

Guest blog: www.biblogg.no

 

Av

Jill Dyche

Jill Dyche, Vice President of SAS Best Practices, was keynote speaker at SAS Forum Norway in September, and was interviewed by Lars Rinnan 

Jill, you delivered a strong keynote, and the audience was really attentive.
You talked about big data and how to get the c-suite to listen. It sounds almost impossible. How do you get them to listen?

You need to meet executives where they are. In other words: figure out what’s important to them now, and then map big data as the answer.
Here’s an example: A large cable company sees an uptick in customer complaints in its call center. They have to add expensive headcount to the support staff. But they decide to incent customers to use social media interactions to ask for support or lodge complaints.  By adding social media transactions to customer profiles, the cable company can not only monitor valuable customers who may be at-risk, it can also “score” its brand reputation based on text analytics of social media interactions. They understand that over half of customer feedback comments are actually installation questions and not complaints. They develop customer support videos and post them on YouTube. Both questions and complaints are reduced, and support staff can be redeployed to cross-selling functions.

Data governance is essential, but how do you get the CXO interested?

Find the problem the CXO needs to solve, and explain how data enables the solution. Many senior executives don’t make the link between a business need and data. If you “deconstruct” the business problem into the data necessary to solve it, you can see the lights go on with executives.


How would you start a data governance process at a large company who has no clue of data governance?
We recommend starting with what I call a “small, controlled project.” Take a business problem, scope it down to a level where data can enable it quickly, and then implement a well-bounded process around data rules and policies necessary to address it.


How is data governance related to big data?

Big data is like any other data: It requires policy making and oversight. In that respect big data should be beholden to larger rules. For instance, data from sensors or devices that may be streaming into your company should be handled in a different way. Is it sensitive? Is it defined? Is it targeted to be consumed by a department? An individual? A machine? All of these factors, and others, should inform the policies around that data. And that’s data governance.

In your experience, are businesses giving data governance enough attention in terms of resources, technology and funding?

Only after they feel enough pain. Very few companies new to data governance actually say, “Hey! Let’s make sure we factor data governance into this new initiative.” Most have to experience the pain of not having the right data for the right business purpose. Go back to the days of Customer Relationship Management and remember how everyone thought they could just plug in a new tool? The validity of the data is directly proportional to the value of the resulting application. Data can no longer be considered an afterthought.

Thanks you for sharing these valuable insights with the readers of biblogg, Jill!