The big data trend is becoming more prevalent among organizations as the volume of data increases due to social media, mobility and cloud computing. As this abundance of structured and unstructured data grows, companies are grappling to successfully manage and make sense of it.
Specifically, companies are applying big data analytics to determine business trends and insights from the reams of data created. Analyzing big data could result in business growth, cost savings, revenue increases and better marketing for organizations. But it comes with challenges.
In this guide, we look at how big data initiatives can provide a competitive edge. We'll examine big data infrastructure considerations, big data security, how big data impacts master data management (MDM), the transition from big data management to big data analytics and the challenges of supporting big data analytics in small and medium-sized business (SMB) environments.
Embracing the big data trend provides a competitive edge
According to some analysts, companies that don't take advantage of big data business opportunities will fall behind the competition. Companies that embrace big data analytics will make more knowledgeable decisions and see financial gains, analysts say. In the best case scenario, companies will analyze information collected from mobile devices, storage arrays and management tools. The idea is to identify repeatable business patterns to capitalize on successes and avoid failures.
However, big data analytics adoption poses some challenges, such as difficulty using big data technologies like the Apache Hadoop Distributed File System and MapReduce. Some analysts, on the other hand, believe the traditional way of thinking about business through data warehousing and BI reporting technologies doesn't give companies the information they need to make the best use of data.
In the meantime, several companies are developing tools to reduce big data obstacles. With these tools and training, companies will find big data technologies more accessible and be able to deploy, administer, manage and secure them more easily.
Read more on embracing the big data trend.
Infrastructure considerations for a big data initiative
Part of embracing the big data trend is knowing what to consider when choosing a big data infrastructure. The biggest challenge IT administrators face is how to store the structured and unstructured data enterprises produce in a way that is easy to analyze. But IT shops must also consider networking challenges that arise in managing and analyzing big data.
In order to overcome challenges presented by the big data initiative, some characteristics of a big data storage infrastructure to consider include capacity, latency, access to files, security and cost.
Read more on big data infrastructure considerations.
Big data initiatives require a secure framework
While big data analysis and management has its benefits, it is important to recognize the security concerns it can pose. Big data can contain security threats, or toxic data like social security numbers and intellectual property, that could be detrimental to an organization if exposed.
Big data environments that are not secured sufficiently can make that information vulnerable to an attack. To avoid attacks and leaks, take the proper steps to secure a big data environment and create a trustworthy framework. To create this framework, organizations must take three key steps: defining the data classification level based on how toxic it might be, looking for analysis and visibility solutions to intersect with big data for security and using tools like access control to defend the data, and, finally, disposing of data when it's not needed anymore.
Read more on big data security.
The big data trend impacts business workflow management
Once companies implement a big data infrastructure, they're likely to see an impact on business workflow -- specifically master data management (MDM) programs.
MDM programs manage connections between an organization's internal data and the big data that flows externally. Because big data comes from various sources, such as social media and cloud computing, it is important to evaluate the best way to manage all the data coming in. For social media, this could be through linking internal customer master files to external social network profiles. As for cloud computing, companies could use cloud-based MDM capabilities or hybrid environments made up of both cloud and on-premises applications.
One of the biggest challenges organizations face with MDM is proving the business case. Companies must show that investing in these programs provides inventory insight and therefore saves on cost. If done successfully, organizations can use MDM practices to take on big data and improve their business strategies.
Read more on big data's impact on MDM.
From big data management to big data analytics
As the big data trend grows, the idea of "big data" has transitioned from defining the amount, speed and type of big data that businesses must manage to determining what to do with that data for business purposes.
This shift from big data management to big data analytics forces organizations to look beyond just understanding how to control big data and deeper into use cases for analytics. In this video, Colin White, president and founder of BI Research explains that it's important for organizations not to focus entirely on the volume of data to manage but rather to look at how this data can help improve business processes and add business value.
Watch this video on big data analytics.
SMBs use big data analytics with the help of cloud providers
Big data may appear to be for big businesses, but small and medium-sized businesses are taking the necessary steps to use big data analytics too. These SMBs may find the big data help they need from cloud providers.
A number of cloud providers have identified trends in big data analytic for SMBs, and smaller companies should seek out providers that have done this work.
Read more about big data cloud analytics for SMBs.
Overcoming big data initiative challenges in the cloud
While many cloud providers do find opportunities in supporting big data analytics in the cloud, it also brings certain challenges surrounding demands placed on networks, storage and servers. Cloud does offer flexibility to crunch big amounts of unstructured data, but this flexibility can also be the problem for cloud architectures.
In order to overcome capacity, performance and agility challenges, cloud providers must change their architectures to accommodate demand. For storage, data capacity is crucial to ensuring performance. Some cloud providers are also adopting architecture models that allow for a more distributed system and better load balancing.
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