“Information is the oil of the 21st century, and Analytics is the combustion engine.”
The Volume, Variety and Velocity of data coming into your organization continue to reach unprecedented levels. This phenomenal growth means that not only must you understand big data in order to decipher the information that truly counts, but you also must understand the possibilities of big data analytics.
Big Data is the biggest game-changing opportunity for IT industry since the Internet went mainstream almost 20 years ago, particularly because of the unprecedented array of insights into customer needs and behaviors it makes possible. But many of my colleagues who agree that this is true aren’t sure how to make the most of it. Instead, they find themselves faced with overwhelming amounts of data and organizational complexity, rapidly changing customer behaviors, and increased competitive pressures.
For years SAS customers have evolved their analytics methods from a reactive view into a proactive approach using predictive and prescriptive analytics. Both reactive and proactive approaches are used by organizations, but let’s look closely at what is best for your organization and task at hand.
Reactive vs. Proactive Approaches: There are four approaches to analytics, and each falls within the reactive or proactive category:
➨ Reactive – business intelligence. In the reactive category, business intelligence (BI) provides standard business reports, ad hoc reports, OLAP and even alerts and notifications based on analytics. This ad hoc analysis looks at the static past, which has its purpose in a limited number of situations.
➨ Reactive – big data BI. When reporting pulls from huge data sets, we can say this is performing big data BI. But decisions based on these two methods are still reactionary.
► Proactive – big analytics. Making forward-looking, proactive decisions requires proactive big analytics like optimization, predictive modeling, text mining, forecasting and statistical analysis. They allow you to identify trends, spot weaknesses or determine conditions for making decisions about the future. But although it’s proactive, big analytics cannot be performed on big data because traditional storage environments and processing times cannot keep up.
► Proactive – big data analytics. By using big data analytics you can extract only the relevant information from terabytes, petabytes and exabytes, and analyze it to transform your business decisions for the future. Becoming proactive with big data analytics isn’t a one-time endeavor; it is more of a culture change – a new way of gaining ground by freeing your analysts and decision makers to meet the future with sound knowledge and insight.
Google Executive Chairman Eric Schmidt and Civis Analytics Chief Executive Officer Dan Wagner discuss the way big data can change everything from corporate strategy to the way people vote. They speak with Trish Regan at Bloomberg’s The Year Ahead: 2014 conference at the Art Institute of Chicago. (Source: Bloomberg)
One research was conducted and online surveys were sent to corporate members of SCM World and MESA International, with respondents from professional services and software sectors excluded from the analysis. Manufacturing & Production (22%), IT Technology (21%), Operations and Engineering (14% each) and General Management (8%) are the most common job functions of survey respondents. Respondents were distributed across Asia & Australia (22%), Europe, Middle East & Africa (40%) and North & South America (38%).
Key take-aways from the study
✔ Mobile technologies and applications (75%), big data analytics (68%) and advanced robotics (64%) are considered the three most disruptive technologies by manufacturers today.
✔ Big Data analytics (42%), advanced robotics (30%), mobile technologies and applications (36%), Internet of things/cyber-physical systems (36%) and digital manufacturing (29%) are the top five technologies manufacturers are relying on to improve agility, responsiveness and reliability of their operations.
✔ 58% of manufacturers are either piloting or planning to invest in mobile technologies and applications, followed by big data analytics (49%).
✔ Comparing the investment priority timeline and level of technology disruption in the following technology investment priority grid further clarifies the impact of each technology on manufacturing.
✔ Real-time factory performance analysis (57%), real-time planning (including MRP and factory scheduling) (53%), real-time supply chain performance analysis (42%) and production quality and yield management (40%) are the four most likely use cases for big data analytics in the digital factory of the future.
The reality is that the tools are still emerging, and the promise of the [Hadoop] platform is not at the level it needs to be for business to rely on it,” says Loconzolo. But the disciplines of big data and analytics are evolving so quickly that businesses need to wade in or risk being left behind. “In the past, emerging technologies might have taken years to mature,” he says. “Now people iterate and drive solutions in a matter of months — or weeks.” So what are the top emerging technologies and trends that should be on your watch list — or in your test lab? Computerworld asked IT leaders, consultants and industry analysts to weigh in. Here’s their list.
- Big data analytics in the cloud
- Hadoop: The new enterprise data operating system
- Big data lakes
- More predictive analytics
- SQL on Hadoop: Faster, better
- More, better NoSQL
- Deep learning
- In-memory analytics
With so many emerging trends around big data and analytics, IT organizations need to create conditions that will allow analysts and data scientists to experiment.
“Data is the new science. Big Data holds the answers.”
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