Big Data is fast emerging as a major component of the information management and analytics infrastructure of many enterprises and companies across the world. As more and more organizations adopt Big Data in their fold of necessary business tools and technologies, the reach of Big Data continues to widen in the business world.
However, along with the increasing opportunities of Big Data analytics in various business spheres, a host of obstacles are also emerging in the scenario. These obstacles are the challenges that can prevent organizations and enterprises from realising the full potential of Big Data and analytics.
Outlined below are the five big challenges that must be addressed for the success of Big Data and analytics in business:
Challenge 1: Selection of Proper Data Management Technology
Big Data involves the use of numerous data management frameworks that are meant to be helpful for operational as well as analytical processing. These innovative frameworks differ from the traditional database management systems in that they are mostly designed to cater to the performance demands for Big Data applications. Such data management frameworks are referred to as NoSQL (“not only SQL”).
Today there are a huge number of NoSQL tools available in the market. Each approach of NoSQL tool development deals with different performance issues such as flexibility, functionality, etc. Selection of the right data management framework is of utmost importance for an organization. A wrong choice in this case can prove to be an expensive error.
As such, the challenge in front of any organization wishing to adopt Big Data is to make the best choice for data management technology. The organization needs to identify NoSQL alternatives that will be able to handle the technology risks of Big Data adoption and provide the desired results.
Challenge 2: Reduction of Talent Gap
As Big Data is making long strides into the business world, the number of Big Data applications and tools are also increasing at an equal rate, if not more. These applications and tools require the expertise of skilled people who are well-versed about how to implement these applications and run them smoothly. However, there is a dearth of talent as far as the evolving field of Big Data applications is concerned.
Today the need is of experts with deep analytical skills and the ability to make effective decisions based on their analysis. The scarcity of such skilled analysts in adequate numbers can impact the adoption of Big Data by the organizations. Even in organizations that have already adopted Big Data as a part of their business strategy, the requirement of skilled analysts cannot be undermined.
With time this gap of talent will diminish due to the entry of more data analysts and practitioners into the analytics field. However, today the challenge is to find skilled analysts who can unleash the potential of Big Data thoroughly to attain the desired objectives.
Challenge 3: Positioning of Data into the Big Data Platform
It is common knowledge that Big Data involves the analysis of massive volume of data. Many people are of the idea that analysis of huge volume of data through a Big Data platform is a simple job that can be easily done by a data analyst. But the fact is that there are a lot of complexities involved in the process of accumulating data from numerous sources, transmitting and delivering the data and then loading the data into the Big Data platform.
Apart from the practical steps of accessing, moving and loading data, an analyst also needs to take care about the response time for loading of data into the Big Data platform. As such, an organization that wishes to adopt Big Data must face the challenge of having the tools and technologies required for universal data accessibility and the infrastructural means needed to deal with massive volumes of data transfers in a timely way.
Challenge 4: Harmonization of Data Sources
Harmonization of data sets is an essential part of Big Data analysis. As we are aware, data is sourced from various sources. These data sets are generally sourced at different schedules and at different rates. This means, there is a need to synchronize or harmonize this data. This synchronization must take place at different levels. For instance, there must be synchronization between data definitions, metadata, etc.
Conventional data warehouses generally carry the risks for data to become unsynchronized. The lack of synchronized data can result in the use of inconsistent information. This is not at all desirable since it will affect the result of analysis. So, organizations using Big Data must face the challenge of achieving data synchronization or harmonization in order to eliminate inconsistencies in analysis and inaccurate results.
Challenge 5: Derivation of Useful Information
Big Data helps organizations to meet several purposes, such as, enhancement in the performance of analytics and increase in data storage. However, to achieve such objectives data availability must be possible. This means that the information obtained must be made available to other sections of the information architecture in the organization.
Data consumers need to access data in order to reduce the necessity of custom coding. But the ever-increasing number of data consumers means that data must be made available to them at the right time. This need to make data available and accessible to various applications in a transparent manner while supporting demand is another challenge faced by the organizations dealing with Big Data.
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