In the present world, most organizations are largely involved in tasks that generate huge amounts of data. Data sets are growing exponentially in various domains globally in a way that makes it hard to manage. This clearly indicates that we are in an era of Big Data. It is reasonable that data management is the ultimate approach to extract the valuable information from such massive data sets. Nevertheless, generating accurate outcomes from such large data requires noticeable efforts, time and finances. This undoubtedly necessitates focusing more on Big Data management. Now days IT administrators are striving hard to manage Big Data, making more researchers to focus more on investigating how to manage Big Data. This paper attempts to highlight issues regarding Big Data management with the idea of working smarter as opposed to harder.
Introduction
The world has been deluged with various statistics on rapid growth of data to the extent that the bytes and numbers have become seemingly meaningless. It is clear that data growth is a trend that cannot be avoided. However, the fundamental issue is how businesses can draw meaningful insights from Big Data in a time when resources are shrinking. Despite the perceived data value, resource allocation to leverage and manage data has not matched the growth pace. It is important to employ people with the required skills though it is becoming a challenge for most industries. Finding people with great analytical skills or who can leverage big data to draw useful decisions is a headache for industries. This sparks the need for automation so as to minimize the training and skills needed to manage Big Data. The best approach to address the skills and resource shortage in Big Data is working harder as opposed to working hard. This paper basically looks at how businesses can smartly manage Big Data while at the same time regulating or reducing costs in order for business users to get tangible value from data easily and fast.
Know Your Data
Transforming, moving and making data available to organizations when they need it is a costly process. With the rapid growth of data as well as the level of waste within the current paradigm in data management, it is now time to transform data economics. Many businesses leverage different types of data with huge volumes for projects in data analytics. However, the exorbitant expenses of not managing data that is dormant is not only on storage but rather the CPU capacity. Mainly due to the increasing costs regarding CPU capacity licensing. Often, dormant data slows performance as the process of loading data consumes so much of the CPU.
Through offloading dormant data to the cloud for instance, businesses can be able to dramatically reduce the demand for more CPU capacity, reduce the amount of EDW nodes as well as significantly reduce maintenance costs. Business should look at grouping applications, users or data within the context of the business such that they can start analyzing utilization then assign accountability through show back or charge back. Identifying dormant data in business recovers more storage capacity. It also greatly reduces costs related to transforming and loading data. If a business does not need specific data anymore, they can finally stop loading it, implying that the business eliminates a section of ETL processes which consume more of CPU capacity. The main idea is to obtain EDW visibility so as to learn data that is used and data that is dormant.
When enterprises specify the relative costs of loading and maintaining data as well as demonstrate the amount of Data not being used, a dataset that might have seemed so critical initially might lose its importance or significance. The underlying request may just lose its urgency, specifically if the costs to maintain the data emanates from the departmental budgets and return on investment is not being achieved.
Choose Your Data Platform Wisely
As data grows exponentially, the various platforms that support Big Data need to increase in terms of size due to the various platforms optimizing various workloads. That is the justification why placing data within the right platform is crucial to managing data efficiently as a strategic asset. Businesses could achieve significant benefits through optimizing and modernizing data placement. Not all data in businesses are equal. Some data exhibit higher values and used for sophisticated analytics while some data is primarily kept for purposes of regulation while some data cannot fit anywhere between the two. Businesses should only move datasets to the most ideal platform on the basis of its use case. In addition, information technology departments ought to make sure that such cost savings are effectively maintained through provision of chargeback reports occasionally to business lines. Through highlighting the data being used as well as costs to business users, information technology can justify migrating data to platforms that are cheap or investing more on information technology infrastructure.
Don’t Keep What You Don’t Need
Even as businesses migrate their data to the ideal platform, it behooves them to critically consider if it is necessary to still hold onto particular data sets at all. There is a huge potential to reduce costs through purging all unused data. Most enterprises have scenarios where some data in their warehouses rarely receive any analytical query in months. There is a large lot of data and possible cost savings. For enterprises to ascertain data that is worth retaining, information technology must be able to analyze the usage of data as well as collaborate across work groups to classify business data into various categories. For classification of data, businesses ought to understand what their specific data sets are and what their employees or customers are using them for. It is then inherent to obtain buy-ins from enterprise stakeholders. Highlight business usage patterns and collaborate with the patterns to make vital decisions within an iterative approach. In the long term businesses will be able to receive tangible benefits.
Getting What You Need to Manage Your Data
Effective data management calls for two main capabilities. First, businesses should be able to integrate and migrate data quite easily across all main relational database systems, cloud, enterprise data warehouse as well as Big Data platforms. Second, they should also be able to fine tune performance, reduce costs, optimize data placement with metrics regarding how the enterprise is utilizing data and platform resources.
Further to obtaining tangible benefits out of data, effective data management allows information technology organizations to minimize costs associated with Big Data. With an insight of how data is used, information technology can collaborate with the business to make decisions that are more informed regarding what data should still be retained and how such data should be stored as well as data that can be archived or purged. This norm also enables specific information technology organizations to cap their investments on IT assets within an existing capacity. Being required to achieve more with less is not a new aspect in information technology. Organizations ought to make it a norm to work leaner and smarter while dispensing critical services to the business. The same should be done with Big Data analytics.
This article, presented insights into Big Data management and the idea of working smart as opposed to hard. The paper elucidate various approaches that businesses can use to effectively manage the Data. Furthermore, the paper gave recommendations on each aspect and how businesses can achieve them. Also, data is growing at such an exponential speed in the current business environment. However, the relative information technology to support Big Data operations lags behind significantly. Therefore, there is so much work remaining for researchers regarding Big Data management so that the world could significantly address Data management challenges. Finally, with regard to Big Data management, various notable challenges specifically, maintaining Big Data, Big Data analytics and choosing the right platform are the most evident challenges that demand the efforts of researchers in future.
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