Archive for the ‘Big Data’ Category

 

How can businesses make the most of their data?

Thursday, April 24th, 2014 by

When businesses attempt to harness Big Data, they’re looking to obtain actionable intelligence that can influence key business decisions. A variety of tools to do so are now available, but executives often get lost in the process of selecting which program would best suit their requirements. If a company needs to determine how a specific action will affect a particular industry, predictive analytics is probably the right choice for them. If a merchandiser wants to figure out how a single customer interacts with its brand, then descriptive tools may be the best option.

Organizing a plan to satisfy a customer.

Organizing a plan to satisfy a customer.

Know what you’re working with
Trying to draw conclusions from raw data aggregated onto cloud servers is both inefficient and ineffective. A company could collect all the data it wants, but if there’s no way of managing and segregating the information, then hastily made conclusions could send the company in the wrong direction. In addition, how professionals perceive the intelligence should not be manipulated by how they want to interpret it.

When it comes to understanding data, an open mind is mandatory. If tailored data displays a slight or entirely different angle on a particular situation, it’s better for management to adjust their plans according to the information as opposed to distorting the meaning of the digital information so that it better coincides with an original business strategy.

Interpreting phenomenon
Ultimately, data analytics gives C-suite professionals the ability to navigate through previously undecipherable patterns. ITWeb contributor Goran Dragosavac stated that there are three primary kinds of intelligence scrutiny platforms that draw considerably different conclusions from a single marketplace. Depending on what kind of business a particular company is in, the usefulness of each platform may vary significantly.

  1. Predictive analytics examines the events of the past and present to determine which events will most likely transpire in the future. How can the current actions of a company manipulate the outcome? What should the business do to change the end result?
  2. A descriptive program detects both favorable and undesirable patterns across organizations and regional economies. For example, a health care organization may use such a solution to discern what actions a certain hospital is taking to effectively eradicate diseases. How do its practices differ from other facilities in the same area?
  3. Sequencing software allows enterprises to scrutinize individual aspects of a particular operation, such as warehouse management processes. How will sensors placed on individual packages affect database capacity? Would cloud storage be more preferable?

(more…) «How can businesses make the most of their data?»

How Public Organizations Should Treat Big Data

Tuesday, April 22nd, 2014 by

Though the “only human” argument certainly doesn’t apply to Big Data, enterprises and public organizations often expect too much out of the technology. Some executives are frustrated by results that don’t necessarily correlate with their predetermined business plans, and others consider one-time predictive conclusions to be final. The problem is, there’s no guarantee that analytical results will be “right.”

A government-themed action key

A government-themed action key

Public authorities interested in integrating Big Data into their cloud servers need to understand two things. First, digital information possess no political agenda, lacks emotion, and perceives the world in a completely pragmatic manner. And second, data changes as time progresses. For example, just because a county in Maine experienced a particularly rainy Spring doesn’t mean that farming soil will remain moist — future weather conditions may drastically manipulate the environment.

Benefiting from “incorrect” data
If a data analysis program harvests information from one source over the course of 1 hour and then attempts to develop conclusions, the system’s deductions will be correct to the extent that it accurately translated ones and zeroes into actionable intelligence. However, because the place from which the data was aggregated continues to produce new, variable knowledge, it may eventually contradict the original deduction.

Tim Hartford, a contributor to Financial Times, cited Google’s use of predictive analytics tools to chart how many people would be affected by influenza by using algorithms to scrutinize over 50 million search terms. The problem was, 4 years after the project was underway, the company’s system was disenfranchised by the Center for Disease Control and Prevention’s recent aggregation of data, showing that Google’s estimates of the spread of flu-like illnesses were overstated by a 2:1 ratio.

Taking the good with the bad
Although Hartford exemplified Google’s failure as a way of implying that Big Data isn’t what software developers are claiming it to be, Forbes contributor Adam Ozimek noted that the study displayed one of the advantages of the technology: The ability to reject conclusions due to consistently updated information. Furthermore, it’s important to note that Google only collected intelligence from one source, whereas the CDC was amassing data from numerous resources.

(more…) «How Public Organizations Should Treat Big Data»

Comparing Cloud Infrastructure Options for Running NoSQL Workloads

Friday, April 11th, 2014 by

A walk through in-memory, general compute, and mass storage options for Cassandra, MongoDB, Riak, and HBase workloads

I recently had the pleasure of attending Cassandra Tech Day in San Jose, a developer-focused event where people were learning about various options for deploying Cassandra clusters. As it turns out, there was a lot of buzz surrounding the new in-memory option for Cassandra and the use cases for it. This interest got me thinking about how to map the options customers have for running Big Data across clouds.

For a specific workload, NoSQL customers may want to have the following:

1. Access to mass storage servers for files and objects (not to be confused with block storage). Instead, we’re talking on-demand access to terabytes of raw spinning disk volumes for running a large storage array (think storage hub for Hadoop/HBase, Cassandra, or MongoDB).

2. Access to High RAM options for running in-memory with the fastest possible response times—the same times you’d need when running the in-memory version of Cassandra or even running Riak or Redis in-memory.

3. Access to high-performance SSDs to run balanced workloads. Think about what happens after you run a batch operation. If you’re relating information back to a product schema, you may want to push that data into something like PostgrSQL, SQL, or even MySQL and have access to block storage.

4. Access to general-purpose instances for dev and test or for workloads that don’t have specific performance SLAs. This ability is particularly important when you’re trialing and evaluating a variety of applications. GoGrid’s customer’s, for example, leverage our 1-Button Deploy™ technology to quickly spin up dev clusters of common NoSQL solutions from MongoDB to Cassandra, Riak, and HBase.

(more…) «Comparing Cloud Infrastructure Options for Running NoSQL Workloads»

Be Prepared with a Solid Cloud Infrastructure

Thursday, April 10th, 2014 by

The more Big Data enterprises continue to amass, the more potential risk is involved. It would be one matter if it was simply raw material without any clearly defined meaning; however data analytics tools—combined with the professionalism of tech-savvy employees—allow businesses to harvest profit-driving, actionable digital information.

Recovery disks shattering

Compared to on-premise data centers, cloud computing offers multiple disaster recovery models.

Whether the risk is from a a cyber-criminal who gains access to a database or a storm that cuts power, it’s essential for enterprises to have a solid disaster recovery plan in place. Because on-premise data centers are prone to outages in the event of a catastrophic natural event, cloud servers provide a more stable option for companies requiring constant access to their data. Numerous deployment models exist for these systems, and most of them are constructed based on how users interact with them.

How the cloud can promote disaster recovery 
According to a report conducted by InformationWeek, only 41 percent of respondents to the magazine’s 2014 State of Enterprise Storage Survey stated they have a disaster recovery (DR) and business continuity protocol and regularly test it. Although this finding expresses a lack of preparedness by the remaining 59 percent, the study showed that business leaders were beginning to see the big picture and placing their confidence in cloud applications.

The source noted that cloud infrastructure and Software-as-a-Service (SaaS) automation software let organizations  deploy optimal DR without the hassle associated with a conventional plan. Traditionally, companies backed up their data on physical disks and shipped them to storage facilities. This method is no longer workable because many enterprises are constantly amassing and refining new data points. For example, Netflix collects an incredible amount of specific digital information on its subscribers through its rating system and then uses it to recommend new viewing options.

The news source also acknowledged that the issue isn’t just about recovering data lost during the outage, but about being able to run the programs that process and interact with that information. In fact, due to the complexity of these infrastructures, many cloud hosts offer DR-as-a-Service.

(more…) «Be Prepared with a Solid Cloud Infrastructure»

Infographic: 2014 – The Year of Open Source?

Tuesday, April 8th, 2014 by

If you’re a software developer, you’ve probably already used open-source code in some of your projects. Until recently, however, people who aren’t software developers probably thought “open source” referred to a new type of bottled water. But all that’s beginning to change. Now you can find open-source versions of everything from Shakespeare to geospatial tools. In fact, the first laptop built almost entirely on open source hardware just hit the market. In the article announcing the new device, Wired noted that, “Open source hardware is beginning to find its own place in the world, not only among hobbyists but inside big companies such as Facebook.”

GoGrid_OpenSource200_blog

Why now?

Open source technology has moved from experiment to mainstream partly because the concept itself has matured. Companies that used to zealously guard their proprietary software or hardware may now be building some or all of it on open-source code and even giving back to the relevant communities. Plus repositories like GitHub, Bitbucket, and SourceForge make access to open-source code easy.

In its annual “Future of Open Source Survey,” North Bridge Venture Partners summarized 3 reasons support for open source is broadening:

1. Quality: Thanks to strong community support, the quality of open-source offerings has improved dramatically. They now compete with proprietary or commercial equivalents on features–and can usually be deployed more quickly. Goodbye vendor “lock-in.”

(more…) «Infographic: 2014 – The Year of Open Source?»