Tableau Software

Data visualization plays an important role in a big data environment

Tableau Software is generally thought of as a lightweight piece of software, with fairly simple functionality. But users are increasingly putting it to work in more complicated big data environments, pushing the limits of what the data visualization tool can do.

“Data in itself is pretty meaningless. Insights need to be visual,” said Gaurav Kumar, product lead in the data science engineering team at GoPro, based in San Mateo, Calif., in a presentation at Tableau Conference 2016.

CMO of Sports Authority on How Marketers Can Build A Data-Centric Culture

Leveraging data to achieve a competitive advantage is a priority for most C-level leaders. The benefits are clear. An Economist Intelligence Unit survey of 530 senior executives found that 89% didn’t believe they were significantly better than their peers at leveraging data, indicating just how hard it is to move beyond desire to competency.

Marketing leaders are often at the center of improving data competency as they are the conduit through which firm-level transformation occurs. To better understand how to effectively make the change, I talked with Ron Stoupa, the CMO of Sports Authority and former CMO of Pep Boys, regarding how he was able to champion a data literacy movement. What follows are Stoupa’s insights.

Trump’s Election: Poll Failures Hold Data Lessons For IT

How good is the data we rely on in our lives and businesses? After hundreds of polls were conducted and analyzed to predict who would win the US presidential election, the data scientists, statisticians, and media outlets that analyzed those polls failed to predict that Donald Trump would win against Hillary Clinton on Election Day. It was a shocker to many.
That brings up an important question for IT organizations that are investing in data and tools to analyze it. How good is that data? How good are those insights? Can we trust them? How can we make our predictive models more accurate? What lessons can we learn from the polling data issues that can be applied to our own organizations’ data efforts?

BI Self-Service Analytics: IT’s Next Top Priority

The drive to enable more self-service analytics for business intelligence users is certainly not news. The trend toward enabling self-service has been underway for quite some time. Indeed, Forrester Research said in a blog post that it wasn’t news back in Feb. 2015.
It used to be a lot easier when business intelligence users were only looking at limited data sets such as those from the ERP system or relational databases. Now, with the influx of new data sets, including unstructured data, from social media and email, to name a few, the possibility of gaining greater insights is huge — but so is the challenge. 

Three Types of Goals and KPIs Needed for Social Media Analytics

Good social media analytics are needed for businesses to understand the social consumer.

But which goals and key performance indicators (KPIs) are most needed? And why?

Companies looking to garner better social media results need to start with those two questions — and from my experience, these are the three areas that need addressing when looking to grasp social media analytics.

It’s my opinion that you can’t choose your KPIs until you have chosen your company goals.

Topic Modeling: Deriving Insight From Large Volumes of Unstructured Data

The rise of social networks has led to an increase in unstructured data available for analysis, with a large proportion of this data being in text format such as tweets, blog posts, and Facebook posts. This data has a wide range of applications, for example it is often used in marketing to understand people’s opinions on a new product or campaign, or to learn more about the target market for a particular brand.

When dealing with large volumes of unstructured text data, it can be difficult to extract useful information efficiently and effectively. There is almost always too much data to read through manually, so a method is needed that will extract the relevant information from the data and summarise it in a useful way.
Topic modelling is one method of doing this. Topic modelling is a technique that can automatically identify topics (groups of commonly co-occurring words) within a set of documents (e.g. tweets, blog posts, emails).

An effective topic model should output a number of very distinct groups of related words, which are easily identifiable as belonging to the same subject. For example, if the topic model was trained on thousands of tweets related to diet, one group of words might include “gluten”,”glutenfree”, “coeliac”, “intolerance”, which would correspond to a “gluten free diet” topic. Another group of words might be “vegan”, “dairyfree”, “meatfree”, which would represent a “vegan diet” topic.

IT Decision-Makers Continue to Advance Data & Analytics Strategies

IDG Enterprise — the leading enterprise technology media company, composed of CIO, Computerworld, CSO, InfoWorld, ITworld and Network World — reveals in the 2016 IDG Enterprise Data & Analytics research that 69% of organizations have either implemented data-driven projects or are planning to. Data & analytic strategies and practices stretch beyond storing 247.1 terabytes of data—which most organizations anticipate having to deal with in the next 12-18 months. The focus is on understanding where the data is coming from, how it can be used, security implications and the solutions that vendors offer to alleviate internal burdens brought on by additional data & analysis.
Integrating Data & Analytics into Business
Technology budgets are staying strong as companies continue to harness data for insights and competitive gain. In the coming 12-18 months, 44% of organizations will increase their spending on data-driven initiatives and an additional 35% will have budgets remain consistent with the previous year (Click to Tweet). This translates to enterprise organizations (1,000+ employees) spending an average of $13.9 million on data-driven initiatives and SMBs (<1,000 employees) spending $4.3 million, a drastic jump from the $1.6 million SMBs anticipated spending in 2015.