Key Factors to Consider for Effective Big Data Initiative
Big data has enabled organizations to analyze huge volume of data and gain insights that can help themptimize their business strategy.. That said there are many factors to look at before jumping into the big data landscape. While only few organizations have implemented a fully-fledged big data infrastructure. It is because of various reasons pertaining to security, infrastructure constraint, and lack of big data analytics framework in dealing with smaller data sets to name few. There are certain myths as well that has lead organizations to think about big data differently. Some of them are detailed below:
Big Data is Not Mission Critical
Most enterprises tend to believe that big data are only good for one time usage. While looking at the importance of big data in today’s era, it is certain that it is not just an experimental entity, rather a mission-critical IT asset. By using Hadoop, big data can prove to be instrumental in storing and processing huge volumes of data and deliver trustworthy results.
Hadoop is Free
It is true that all the necessary elements of Hadoop can be downloaded from the Apache website free of cost. Alternatively, Hadoop as a Service (HaaS) also provide many public clouds for with a meager cost, at least initially. Why then Hadoop is not free?
Firstly, to run Hadoop based systems and application, a separate set of skillset is required, which is expensive. Organizations can hire data scientists, Hadoop specialists at several hundreds of dollars per hour, or engage in-house business analysts and engineers into this, or a combination of both.
Infrastructure Required is Inexpensive
Consider an enterprise that has already installed cloud-based Microsoft Exchange implementation and therefore have access to large number of generic servers that are already paid for. The enterprise might think of implementing big data on the installed set up, making the infrastructure cost look inexpensive. However, for enterprise to embrace big data as a mission-critical asset, a robust infrastructure is necessary. If an organization plans to see big data as part of their business model in the long run, then ‘purpose built’ hardware is mandatory.
Big data technologies leverages large chunk of structured and unstructured data: by capturing the large data sets, organizations can optimize their business strategiesby using the insights gleaned through massive data. Hadoop systems and NoSQL databases also play major role in this landscape and needs to be deployed. However, enterprises have to bolster their infrastructure to meet the scalability and complexity of big data landscape.
Irrespective of the type of big data technology that a company wants to deploy, there are certain considerations that they must look into and are detailed below:
Data quality has always been one of the major concerns in the analytics arena. Many BI and analytics teams find it difficult to ensure validity of data and convince business users to trust the reliability and accuracy of information. The ability to store, use, and manipulate data in Excel creates self-service analysis capabilities; however it might not provide a resilient platform for analysis. Data integration and quality tools can improve the quality of data accuracy and provide standardized process for BI and data analytics. However, big data poses a greater challenge by bringing in large data that include structured and unstructured data sets.
One of the foremost requirement of a data warehouse is its ability to store large sets of data. However, all data warehouses are not created equally, some can process complex queries, while others cannot. Big data makes things even more complicated with the variety and velocity at which data is created and collected. In the light of these challenges, it has become an absolute necessity to augment data warehouses with Hadoop systems or NoSQL databases. For organizations looking to capture and analyze big data, storage space is never going to be enough; the important aspect to consider is where to put the data so that it can be transformed into useful information and made available to data scientists and other users.
Big data analytics largely depends on the speed at which data set query is performed. Organizations of varied sizes will deploy analytics system in accordance with their query-processing requirement.
With the growing demand of processing and analyzing data, a big data strategy cannot be prepared without having future in mind. It is crucial to think if the big data infrastructure can scale up to the levels that will be required going forward. Organizations will have to think beyond just storage issues, going ahead, performance of the big data infrastructure will also be an important factor.
For big data to deliver beneficial outcomes for organizations, it will require an enterprise-class facility. Big data entails requirement of networking, hardware, and software. It is therefore important that an organization ensure that these requirements are met.