C*Nect Delivers ‘Self-service Data Quality’ to Transparent Healthcare

C*Nect’s Data Management Platform has enabled Transparent Healthcare to easily identify and correct data quality problems, thereby greatly improving the accuracy and quality of its data for improved Business Intelligence, ultimately resulting in improved efficiency, growth, and member service.

C*Nect has partnered with Transparent Healthcare to address two priorities. “With C*Nect’s Data Management Platform, we were able to quickly view our customer and financial data in a single view. We immediately realized we had numerous data quality issues. Using C*Nect, we could address and fix data quality issues ourselves directly in a controlled and audited manner”, says Transparent Healthcare President and Co-Founder, Andy Rieger. “We have greatly improved both the accuracy and completeness of our customer data that better positions our company for accelerated growth and improved customer service.”

[ PRWeb – July 23, 2015 – Full Story ]

How Microsoft is using big data to predict traffic jams up to an hour in advance

Microsoft has partnered with the Federal University of Minas Gerais, one of Brazil’s largest universities, to undertake research that could help predict traffic jams up to an hour in advance.

The Traffic Prediction Project is setting out to crunch all traffic data, including historical numbers where available, from transport departments, road cameras, Microsoft’s Bing traffic maps, and even drivers’ social networks, to see if established patterns can help foresee traffic jams from 15-60 minutes before they happen.

Big data is increasingly being used to analyze global problems to find solutions; this extends into the medical realm too, where it’s being used to discover new drugs and even combat health care fraud. So beating traffic jams is just one of many real-world issues that can be tackled by combining lots of data from multiple sources.

 

[ Full Story – venture beat.com – April 3, 2015 ]

Data Quality Is Lacking

A new study shows that a majority of organizations are wasting revenue and realizing poor customer insights by failing to optimize their data management strategy.

Experian Data Quality‘s “Create Your Ideal Data Quality Strategy” study, which examines respondents’ data-quality levels and issues, finds that 78% of U.S. companies could stand to improve their level of data management sophistication. Furthermore, the research shows that companies with a more sophisticated data management approach are rewarded with more accurate information and higher profits.

In short: The right data strategy can lead to greater success.

[ Full Story – dmnews.com – April 4, 2015 ]

Adapting Big Data Governance: Business Buy-In and Technology

Governance is one of those issues frequently mentioned with Big Data, but few pieces go into depth about what that means. So this week, I’ve focused on what several expert sources say about Big Data governance.

First, I shared four new governance challenges Big Data creates. The next day’s post focused on how to prioritize, simplify and adapt for two of those challenges _— data roles and a broader, enterprise-approach to the data.

Today, I’m wrapping up with a look at what experts say about the next two problem areas: business buy-in and technical challenges.

Business Buy-In

In my previous post, I shared how Big Data creates broader business involvement and explained the problems that this can create. Involvement isn’t enough, though. For success, you’ll also need to ensure that business users understand, appreciate and support Big Data governance.

Prioritize: Middle managers are often the cause of slow adoption and maturity, writes Avi Kalderon,  NewVantage Partners’ practice leader for Big Data and Analytics, in “10 Steps to Big Data Success.” That’s because they tend to be the skeptics, so be sure to prioritize winning over LOB middle managers.

Contradictions in BI?

Over the past few weeks I have read several posts and Tweets covering a number of ideas regarding business intelligence (BI), many of which seemed to contradict each other. For example, one article claimed BI will solve business challenges simply by installing a BI tool on top of data, while another claimed BI solutions require a new process and skilled people to analyze and interpret the data regardless of BI tool. A third article claimed the ability to automate the generation of analytical information via BI tools, while a fourth identified various challenges in managing the quality of the input data and relationships thus making automation impossible. To complicate this further, additional articles challenged the use of Excel as a BI tool while others defended it.

[ Full Story – Linkedin.com – by William Gardner – January 7, 2014 ]

Who Owns Big Data?

Aggregate data and decision making are being hoarded by a few technology companies with powerful data infrastructure. Does it have to be this way? Or could we create a future in which this data infrastructure is available for use by anyone in the world?

Since the early days of modern computing, science-fiction authors and other visionaries have been fantasizing about a database that could contain all of the world’s knowledge. This idea is now moving out of the realm of fantasy. A small number of technology companies are engaged in serious efforts to build databases that really will contain much of human knowledge. Facebook, for example, has mapped out the social connections among more than a billion people, and Google aspires to digitize all the books in the world.

It has become profitable to build a database containing the entire world’s knowledge. The few for-profit companies that own the data and the tools to mine it – the data infrastructure – possess great power to understand and predict the world. But could we create a similarly powerful public data infrastructure, a Big Data for the masses, that anyone in the world could access?

[ Full Story – MIT Technology Review – By Michael Nielsen – January 5, 2015 ]

Big Data Knows When You’re Going to Quit Your Job Before You Do

Good bosses have an uncanny ability to sense when employees are unhappy and work with them to fix problems in the office before it’s too late. At VMware in Silicon Valley, they let the machines figure it out.

VMware has been testing a new prediction technology from Workday, which makes software for human resources departments. The system delivers notifications about when employees might be getting ready to quit, and allows managers to intervene before it’s too late. It looks for trends within employee activity, when promotions were last handed out, regional factors, changes in the industry and other data to make its predictions. The recommendations can improve over time as employers train the system.

[ Full Story – by Jack Clark: Bloomberg.com – December 30, 2014 ]

Six Business Intelligence Predictions For 2015

Being a data-driven organization requires a combination of analysis and predictions. As we look at the last several years, big data has been the dominant force, moving from a consumer-based requirement to a must-have strategy for enterprises to stay competitive. This data revolution is seeing accelerated technology adoption curves and is forcing job roles to change, strategic investments in technologies like Hadoop and data quality/governance, threats to incumbent BI vendors, just to name a few. 2015 will see big changes in enterprise information management, as organizations transform every employee into being data-driven, as analytic use-cases become more complex, and as vendors try to predict the next need of the market.

[ Full Story – Forbes.com – Prakash Nanduri, December 19, 2014 ]