big data

Big Data—The Power of Real-Time Business Analytics

More and more of today’s enterprises are succeeding with big data by capturing real transformative value that just a short time ago seemed unattainable. Companies in nearly every industry across the globe use big data in real time to increase company value and market share. Big data makes it possible to deliver the kind of business intelligence the C-Suite loves—the kind that comes from legitimate experience with real data and analytics in actual business situations.

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20% profitability boost predicted for users of business analytics

From Dan Quirk, SAP National Practice Leader

A new study from Gartner quantifies the benefits of adopting predictive business metrics. According to the study, businesses that invest in analytics that provide predictive tools will see a 20% profitability bump by 2017. However, despite their proven value, many CIOs say their companies aren’t currently investing in these tools.

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  • 31% of IT leaders say they don’t have measureable metrics in place to improve business performance
  • However, 71% of IT leaders know what metrics are most important for their business, highlighting a 40% gap in those who know what to measure but aren’t currently tracking for it
  • Only 48% of IT leaders have identified important metrics, and have tied them to their top KPIs

For those developing these business process suites, they’re in for some big years ahead, with Gartner estimating this sector to grow to $2.8 billion this year.

SAP HANA has been touted for its ability to analyze over a billion data records in 0.04 seconds. Our clients see big results with the power of SAP HANA, and its potential is only just being tapped.

Are you using predictive metrics to guide your business this year? Have you seen benefits from it?

New research highlights the benefits of cloud-based BI solutions

As the business intelligence (BI) market reaches $13 billion, IT leaders searching for new solutions are increasingly choosing cloud-based tools to analyze their operations. For good reason, too—BI solutions with their roots in the cloud consistently receive higher satisfaction scores and often cost less to implement when everything is said and done.



Consider this research:

  • 54% of IT leaders would choose a cloud-based BI solution for a new environment, against just 14% who would prefer on-premise
  • 80% of those who choose cloud BI are satisfied with their service, versus only 51% who are satisfied with their on-premise solution
  • Cloud has a definite speed advantage: 83% of respondents say they can implement cloud BI faster, against only 4% for on-premise
  • Cloud also has a cost edge, with 68% of cloud BI implementations coming in at or below expected costs, while 54% of on-premise solutions met their cost expectations (this means nearly half of on-premise implementations exceeded cost estimates!)
  • 44% said on-premise requires more user training, vs just 5% who think cloud needs more training for employees to learn
  • On-premise solutions also lag behind in frequency of use, with 51% saying that cloud BI is accessed more often by more employees, against just 18% for on-premise
  • Predictably, literally 0 respondents said on-premise was better for mobile accessibility

Do you have any experiences that make you for or against cloud BI? Tell us about it below.

The Information Management Crisis: Are You Ready?

Gartner predicts that one third of Fortune 100 companies will be facing an information management crisis by 2017 due to the large amounts of unmanaged data that has been acquired. Within the walls of these large corporations, data has been gathered and stored with minimal security and data management processes, as well as the technology and hardware to support it.



Corporations need to begin managing the data and what they are doing with it instead of just maintaining it. An Enterprise Information Management (EIM) discipline needs to be built and managed in order for the big data gathering to continue. The power a well-managed, information-sharing system can provide is endless.

Hype or Ripe:  Going forward, will these large organizations be able to continue gather and store the data at the rate, or be able to trust the data quality?

What’s the deal with dirty data?

Guest author: Dan Quirk, SAP National Practice Leader

While it’s widely believed that big data is worth the investment, a 2013 survey revealed 60 percent of IT leaders admitted that their organizations aren’t held accountable for the quality of the data. As big data becomes more relevant, what effect does dirty data have?

Data that’s referred to as “dirty” contains flawed information. Dirty data may include misleading or skewed data, duplicate data, incorrect or inaccurate data and non-integrated data. Dirty data is related to the “garbage in, garbage out (GIGO)” concept, meaning that a program’s quality of output can be no greater than the quality of input.



Dirty data can cost companies time and money. Data quality can affect productivity by up to 20 percent. More than 40 percent of business initiatives fail to meet their goals as a direct result of poor quality data.

Skewed data leads to skewed analytics and performance measurement, and poor data quality leaves analysts less time to actually analyze the data. Approximately 67 percent of business analysts spend too much time manually correcting data, which affects analytic quality and profit.

How can organizations work toward keeping data clean? Here are five tips:

  1. Establish a data management strategy.
  2. Check databases for a baseline of quality, then budget for data maintenance and upkeep.
  3. Plan for regular maintenance and updates.
  4. Prevent duplicate records – and segment old ones.
  5. Establish relationships only with reputable data partners.

Hype or ripe: Is dirty data an inevitable result of big data?

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