Over the past few years, data has exploded, but getting the correct data is difficult.
AI and ML are the most disruptive technology of our lifetime. According to IDC’s AI Strategies View 2021 Survey— AI disrupters report a 39% improvement in customer experience and 33% improvement in employee efficiency and accelerated innovation with the rollout of AI solutions. Clearly, enterprises are embracing AI technology. However, the access to leverage various data types can pose a roadblock to many AI and ML initiatives. Let’s look at data’s role in AI initiatives and how enterprises can streamline access to this data.
AI and ML require vast volumes of data to train models, and ensuring unbiased results requires diverse data sets. Models must be continuously trained with the most current information to maintain predictive performance, particularly in dynamic business environments.
Not only is data at the core of an AI model, but it is also a key challenge. Organizations lack the volumes and data quality needed to implement an AI solution. But it’s not only a question of supply. While developing a model, enterprises struggle to get the data into the platform. Difficulties arise, especially when the data isn’t in the desired format. Let’s explore some data formats and their characteristics.
|Structured Data||Unstructured Data||Semi-structured Data|
|– Easy to Search|
– Predefined data models
– Text Based
|– No predefined data models|
– Difficult to search
– Text, PDF, images, video
|– Loosely organized|
– Meta-level structure that can contain unstructured data
– HTML, XML, JSON
The data characteristics listed create challenges and result in enterprises spending more time on tasks related to AI initiatives that are not actual data science. According to a recent Forbes survey, data scientists spend 60% of their time on cleaning and organizing data, with collecting data sets being 19% of their time, resulting in data scientists spending around 80% of their time preparing and managing data for analysis.
To harness the full power of data with AI/ML, data scientists and machine learning engineers need models with the most relevant data, which may not be readily available inside an organization. The heaps of unstructured data do not allow data scientists to get a genuinely comprehensive view of data.
Additionally, most companies have levels of mass data fragmentation, with most of the data not well-cataloged, redundant, and hard to access. Organizations must seek out vendors that offer pre-processing engines to turn unstructured data into structured well, organized data. With the right data transformation tools and plan in place, businesses can gain true power from their already existing data.
With advanced technologies and automation, organizations can automate and streamline their data discovery process and turn unstructured data into structured data. Mantium makes it seamless to gain valuable insights from data that seemed previously unattainable. Mantium’s end-to-end AI automation platform allows businesses to gain the insight they need from files of any type with the most innovative pre-processing engine available today.
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