The Challenges of Big Data
Many companies get stuck at the initial phase of Big Data projects. Mainly because they’re not aware of Big Data challenges or simply not equipped to tackle them. Modern technologies and large data tools require skilled data professionals. These professionals include data scientists, data analysts, and data engineers to work with the tools, tackle the challenges, and make sense of these giant data sets.
The data needs to be structured and properly analyzed to enhance and assist in decision-making. The challenges many come across include things like data quality, lack of data science professionals, validating data, accumulating data from different sources, and managing data.
Historically, data sets are lacking necessary entity relationships or contextual information to bridge the gap between unrefined data assets and business processes i.e. the necessary cross-reference assets that make the data "smart". It’s not built around demographic profiles. Demographic information does not have the rich details about exactly who the people are on their lists — from age to income to race and ethnicity — the way that robust panels do. These data sets, because they’re created by machine-to-machine transfers, also increase the possibility of waste and fraud.
Because of that, the level of certainty they can provide about specific segments of people and insights is limited.
Even when you combine that data with other sources, you’re almost guaranteed to have massive gaps and errors in your estimates, due to your inability to correctly identify the relationships between the aggregated data's variables. This data doesn’t provide the accuracy, objectivity, and transparency required to deliver the most optimal results.