Machine Learning and AI are dependent on large amounts data. The CompTIA AI Council explained more about how strategic thinking is essential when considering how all aspects of data impact your business outcomes. There have been many statements such as “Data is new oil”, “Data the lifeblood of any company”, etc. for almost a decade now. “Big Data” was the buzzword a few years ago, followed by “IoT”, and now AI/Machine Learning. No matter what the terminology may be, data is the driving force behind these emerging technologies and their business applications.
World Economic Forum predicted that the universe of data would reach 44ZB (or 1 million petabytes) in 2020. The rapid digitization, remote work, and the development of new applications during the pandemic have all contributed to data growth that has now reached 94ZB by 2022. This is more than twice as much data growth as in the last 24 months.
Machine Learning and AI draw on large amounts data to build models and train them for meaningful outcomes. As AI adoption continues to rise, so does the need for more data.
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Data’s many Facets
There was a time when data was structured almost exclusively (i.e., tables and columns, forms, transactions etc.). Many of these data are stored in databases and data warehouses and can be siloed between different functions within an organization and within one branch. Unstructured data, which is raw data that has not been formatted, has become a common feature in many businesses over the past decade and a half. Organizations must now manage multiple data types and manage unstructured data that is highly unpredictable. New privacy laws and regulations (e.g., Europe’s GDPR, California Consumer Protection Act) further restrict the use of any data collected or stored within an organisation, as well as multiple copies of data used in different business applications.
Data also raises strategic, governance, and policy questions about who owns, manages, stores and accesses the data and, most importantly, what purpose or analyses it will be used for. Poor data governance can also make it difficult to comply with regulatory compliance requirements that may not have been anticipated when the data was collected. Most mid to large organizations today have data stewards like CDOs (chief data or digital officers) in place to lead and be accountable for organizational data governance–certainly a far cry from the IT/CIO team that used to own all of this in the past.
Without data, AI is not possible
Artificial intelligence, or AI, is not created in a vacuum. For algorithms to be able to provide meaningful solutions that are more likely than looking into a crystal ball, they need a lot of data. The behavior and outcomes of AI models are directly affected by the foundational data. These data could be manipulated or inadvertently ignored models that are used for credit or loans decision making, security bypass, benefits eligibility and medical imaging and diagnosis, product inspection, fraudulent insurance claims, and other purposes.
The pandemic presented a huge challenge to supply-chain pipelines, as demand exceeded supply. Transportation bottlenecks also caused material shortages, which in turn led to price increases and inflation. Regardless of how much data they had, none of this could be predicted. Organizations have enough data on consumer behavior to reasonably predict demand since then, even though snags at ports led to significant losses at major retailers.
AI is not the solution to all problems. In the above example, multiple datasets were used to track inventory at stores, POC analytics and store footfalls. They also tracked shipment tracking, material and factory capacities, worker availability, seasonal trends, shipment tracking, shipment tracking, shipment tracking, worker availability, and shipment tracking. This could be used to build probabilistic based AI models that can smoothen out the Supply Chain’s hiccups. Companies are looking to expand their use cases beyond supply chain to include sales, finance and customer support.
Data is the new currency?
Data is a key component of business decision making and is increasingly used to generate insights. By definition, currency is fluid and must be monitored throughout its cycles to avoid creating imbalances in the markets it operates. There are guidelines for managing currency in reg
