The term Data Visualization loosely refers to the techniques used to communicate data or information by creating visual objects that are contained in graphics. The end goal is to communicate information clearly and efficiently to users via the information graphics selected, such as tables and charts. In his 1983 book “The Visual Display of Quantitative Information”, Professor Edward Tufte defines ‘graphical displays’ and principles for effective graphical display in the following passage: “Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency. Graphical displays should: Continue reading this post>>
As someone who interacts day-to-day with BI developers, consumers and the IT folks who make the whole BI infrastructure function, I have firsthand, in-depth knowledge of the range of logistics that’s required to successfully bring an application from server to client user, regardless of whether the user is sitting in a corporate cubicle or perched on a coffee house stool, somewhere downtown.
I like to break these logistical things down into 2 categories. Continue reading this post>>
Data analysis is considered to be a core component in business intelligence systems. The importance of data analysis pushes some company leadership to opt for outsourced data analysis while other business leadership prefers to stick with in-house data analysis. Let’s first take a look at the role of data analysis in business intelligence. Data analysis converts raw data gathered using different tools into meaningful data, which is usually presented to managers through reporting tools, and will aid managers in decision making. Ultimately, good data analysis leads to good decision making and successful business practices.
Every so often I overhear interesting conversations while standing in line at a store or waiting to board a flight. Recently, I heard this one:
Person 1: I literally put on 5 pounds between Thanksgiving and New Year’s.
Person 2: Don’t get me started…I’ll be working all year to knock off the 10 pounds I picked up over the holidays, just to be back where I was before Thanksgiving.
Person 1: Yeah I’ll exercise full-force through January but by February, let’s be honest – I’m tired of it. One step forward, two steps back…
This scenario is true for many of us; we take a step in the right direction toward our goal, but then get distracted and fall behind. Now that 2013 is underway, it’s time to make some data management resolutions and stick to them.
Data management is an overarching term that includes all the disciplines related to creating, housing, delivering, maintaining and retiring data, with the goal of valuing data as a corporate asset. And it’s not just an enterprise issue anymore. SMBs also find themselves struggling with growing data volumes and subpar data quality. Organizations of all sizes and industries are implementing business intelligence software to glean insight from their data, but the thing no one wants to talk about is this: how many BI projects get delayed due to issues with that data. Whether data or their definitions vary across systems or there are rows that violate relationship rules (many-to-one, one-to-many), data integrity issues must be resolved before you can expect great results from your BI software.
Here are some practical steps you can take to get your data back in shape this year:
Musings on the challenges of big data in a year of serious hype
There’s a reason you haven’t heard more than a handful of big data success stories in 2012. Handling big data correctly is hard, requires huge infrastructure and resource investments, and may not be worth it…yet. According to one survey in November 2012, 60% of businesses said it’s too early to tell if their big data project was successful and produced a proper ROI. It seems that so much of the hype around big data is focused on the technologies you need to buy and the talent you need to acquire (data scientist is the latest fad title), and not on what’s most important: what you can do with the data, what value you can extract, what business decisions you can speed up or improve with all that data.
With companies jumping on the big data bandwagon to the tune of $28 billion this year, it’s time to discuss why it might be best to ignore the hype for now and focus on reaping insight from the data you have already. Here’s why I’m not impressed with your big data:
You don’t actually have big data.
The marketing hype can lead you to believe you have a “big data problem” when you really don’t. Using the terminology incorrectly has the potential to harm your business, causing you to invest in unnecessary infrastructure when you may be able to leverage what you already have in place. Even Microsoft and Yahoo! have made this mistake…