In the contemporary scene dominated by a wide spectrum of application areas, data is now being accumulated and processed at an unprecedented scale. Data-driven mathematical models are fast replacing hand-crafted models of reality and guesswork in strategic decision-making processes. Be it financial services, physical sciences, mobile services, manufacturing, life sciences or retail – Big Data analysis
is the buzzword in all aspects of the society, and how!
Even as Big Data continues to revolutionize the varied aspects of modern-day life—ranging from consumers to enterprises, government to sciences—several research challenges are affecting the different steps that lead to its success. It’s certainly not easy to create value from this multi-step process that encompasses data acquisition, extraction, and cleansing of data, integration, modeling, and analysis of information, as well as its interpretation and deployment. Take a look at the many challenges coming its way.
Multi-steps of Big Data
From issues related to the heterogeneity of data to incompleteness and inconsistency, privacy, visualization, timeliness, and collaborations with eco-systems surrounding Big Data, the research-specific challenges in this field are many. Additionally, with the advent of increased connectivity, improved analytics and the introduction of new technology/ software, there has been a paradigm shift in the ways in which services are being delivered—from preventive and predictive maintenance to reactive approaches alike.
Challenges of Big Data
With consistent growth in the interest created by Big Data and other “third-platform technologies” like social networking, cloud computing, and mobile applications, organizations are now striving hard to utilize the available data
to optimum levels. Even though Big Data is going the big way and creating numerous advantages for gaining competitive advantage, some key challenges are still holding back its forward march.
Time to Overcome Business Data Challenges Outside of IT
It’s crucial for companies to understand that Big Data
is not the responsibility of their IT teams alone. With technology spending outside of the conventionally active IT department taking a front seat, it is time that Big Data became the major driver and started offering clear, definable, and value-added services to other parts of the business as well. For this to take place, organizations will have to educate different teams to extract meaningful insights and benefits from Big Data
and execute the same.
Need for Big Data to Become a Core Part of Business Units
To help Big Data integrate varied business unit functions, it is important for all departments to have IT staff of their own. However, the challenges put forth by the recruitment, training and handling of experienced IT staff are many and are bringing in roadblocks that require further investments of valuable resources.
Realization and Articulation of the Value Added by Big Data
Today, more and more organizations are accumulating data. But then, are they doing so with the intention of articulating the value around the same? In future, to justify their large investments in Big Data solutions, businesses have to build a strong business case with regards to their data collection. This business case, in order to cater to the three USPs of Big Data—velocity, volume and variety—has to be linked to the value created for the organization.
Role of Interactions--other than those on Social Media
In the past, most Big Data activities
were centered on social media and targeted customers with smartly tailored interactions. This is not enough; Big Data’s role has to move beyond social media platforms and include more innovative research options, links with risk management practices, and smarter data analytics; so that it is utilized to its fullest capabilities.
Big Data analytics goes a long way in helping organizations and individuals cope with their data velocity, data variety, and data volume needs. Proper handling of the challenges on hand will help users leverage the many advantages of the same—the right way.