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Quality of datasets defines the quality of AI projects

Data professionals need vast amounts of data to explore and test new ideas and hypotheses. Internal datasets available in an organisation – whether commercial, industrial, or academic – are not varied or big enough to define new findings. They provide limited information. This is where public datasets fit in. Public datasets help bring in real-world insight into studies. Combining internal data and external datasets can benefit anyone involved in decision making. Today there are many curated datasets available for artificial intelligence (AI) projects, whether it involves machine learning (ML) or deep learning. On the other hand, there are an equal number, or even more, of datasets which are not curated. 

Data is undoubtedly the backbone of any AI project. Deep learning models and systems require especially large quantities of data. Datasets consist of texts, numerical data, audio, images, videos, and various other types for solving and analysing AI challenges. Both the quality and the quantity of data are important. Even the best algorithms fail if the quality of data is bad.

In fact, preparing and understanding data is perhaps the most time-consuming aspect of any AI project. AI developers, data scientists, and other data professionals spend most of their time analysing datasets. Today, there is an abundance of open-source data sets that can motivate and inspire researchers to carry out state-of-the-art research on AI projects. However, not all of them are curated.


Open data and public datasets

Although open-source datasets, or open data, and public datasets are not the same, the terms are frequently used interchangeably. Both data types allow free access, sharing, usage, and modification. Open data includes data collected and released by academic institutions, reputed independent agencies, and governments, and hence it is highly reliable. Such datasets are sourced carefully in adherence with privacy laws. On the other hand, public datasets - if defined precisely - include all other data. They are often unstructured and need varying amounts of sorting before they can be used.

A few popular, curated and data-rich dataset platforms include:

  1. Google Dataset Search
  2. Kaggle
  3. AWS Public Datasets
  4. UCI Machine Learning Repository
  5. Quandl

Benefits

The most obvious benefit of open data is that it doesn’t require time and effort to collect. Due to this, there are cost savings as well, in addition to other benefits such as agility*. Open data leverages AI outside the realm of academia and business; hence, it can provide a wider understanding of issues and trends worldwide. Since the data sources are varied, they cover a wide range of products or population, depending about study. 

Unrestricted availability and access of open-source data and open-source tools allows data professionals and organisations, even small ones, to analyse data and train algorithms as needed. The access to so much data makes it easy to train machine learning algorithms at scale, which is otherwise not easy. In fact, there are many successful applications that have been developed using public datasets.

With widespread access to data, more people can work and contribute to ML, and this can give a great boost to ML development. When participants from varied industries or interests participate, there is increased innovation and progress all around.


Challenges and solutions

However, like everything else, there are always a few hurdles. Prominent challenges faced by data professionals include:

  1. Lack of data samples: Not having large enough data samples to run ML algorithms. Very niche areas of research can make datasets rare as well. In such cases, data professionals must dig deep, look for correlations between available datasets, and define new datasets. 
  2. Biased and inaccurate datasets: If there are inherent errors and biases in the tools used to collect data, the datasets can be biased and inaccurate. Data professionals must know how the data was collected and ensure that it is relevant and reliable. Inaccurate and biased datasets would negatively affect the accuracy of the work.   
  3. Need to cleanse non-curated data: Curated datasets are usually of high quality and refined. However, non-curated open-source datasets need a lot of cleaning before they can be used. Open data is not aligned with any outlined standard yet, so certain datasets may be unusable because of the way data is stored. Such data can slow down a process or, if used, limit functionality. Since organisations do not really gain from the release of data, they are not frequently motivated to spend resources to clean the data.
  4. Licensing of the dataset: Before using a dataset, data professionals must make sure that the dataset is licensed to be released in an open platform. Any compliance and privacy issues in the dataset can complicate matters and render the work irrelevant.
  5. Privacy concerns: Privacy in open data is always a grey area. It is possible to integrate several datasets and find confidential information. While the dataset may be complete, it may breach privacy laws making the ground of analysis quite shaky. To overcome such situations, it is important to use encryption and other security measures as per standards.
  6. Risks of data tampering: Open-source datasets can be modified by any user and to eliminate the risk of using tampered data, it is best to procure datasets from the original sources. Tampering of a dataset could involve changed values, or addition of malicious code, or some other kind of processing that would degrade data quality. Data integrity is critical for accurate and productive results.

Undoubtedly, careful usage of open-source data can have a significant impact on AI/ML. Data professionals need to remember that any data is only as good as its source and the methods of collection.

Data science is no longer solely focused on what is required; it is shifting to the realms of unlimited possibilities. Data is valuable, and high-quality data is abundantly available. Intuition, curiosity, and imagination are now the forces driving the use of big data. The democratisation of data is playing a pivotal role in this development. 

* For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.