When taking on projects that involve analyzing massive datasets, data scientists encounter several obstacles that can make their work difficult. One of the foremost challenges is simply having to work with the enormous volume of records, files, numbers, text, images or other data points. Big data is called “big” for a reason – it is an immense amount of information that pushes the limitations of storage, memory, and processing power.
With terabytes or petabytes of data, just loading everything into the computing environment for exploration and modeling can tax even the most powerful systems. Moving vast quantities of data between storage and memory consumes time and resources that data scientists no longer have available for actual analysis. This challenge is exacerbated when datasets contain many different file formats, structures, and levels of organization, requiring extra preprocessing work to integrate and standardize everything.
Even when the raw data can be accessed, another major problem is filtering through it all to find the meaningful patterns and insights actually needed for the given problem or research questions. With such abundance, the proverbial needle must be found in a haystack the size of a mountain range. This entails feature selection, data cleaning, and other procedures to focus on only the most informative and error-free subsets. Unfortunately, the process of data reduction introduces its own difficulties like confirmation bias and missing potentially-useful relationships.
Visualization provides another obstacle, as traditional charting and graphing tools max out well below big data scales. When datasets contain many attributes, observations numbering in the millions or billions, or multi-dimensional relationships, mapping and visualizing the key features for human comprehension grows exponentially more problematic. New and more powerful visualization approaches must be adopted or developed specifically for “looking into” immense stores of figures, categories and correlations.
Machine learning algorithms ran into roadblocks as well, since many were created assuming much smaller training datasets to learn from. When provided with humongous data, models may overfit and fail to generalize, or require unreasonable computation time. Advances in distributed computing help address this by parallelizing work across many processors/systems, yet the statistics and programming designs still need reworking. Deep learning shines in some big data use cases but not all, and training really deep neural networks with enormous inputs can hit technical ceilings.
For larger companies and research teams, infrastructure hurdles also factor in. Not all groups have generous budgets to construct industrial-scale data lakes, cloud services, or high-performance computer clusters. On-premise solutions reach capacity constraints without costly expansion. Even for those able to invest, setting up and evolving such systems requires specialized skills that may be lacking in the organization. Licenses for advanced analytics software can further deepen expenses.
The speed of data also introduces challenges, as real-time streaming data places heightened demands on computational infrastructure, algorithms, and pipelines that formerly worked on static datasets. Keeping pace with high-velocity sources necessitates rethinking batch-oriented techniques. Similarly, distributed and decentralized information poses coordination difficulties that centralized data did not.
All of these obstacles combine to substantially increase the time, resources, and expertise needed for tackling big data problems. While enriching fields across sciences, healthcare, commerce and more, the very magnitude of available information presents its own set of difficulties for modern data sleuths to overcome. Innovation constantly improves matters, yet big data analysis will undoubtedly continue challenging scientists and organizations for the foreseeable future. With diligence, focus, cross-disciplinary collaboration, and adaptive methodologies, each new technical barrier can be eventually surmounted.
