Drowning in Data? AI can Help


Think about any modern convenience or improvement in how the world works, and it’s easy to miss the mostly invisible streams of data generated and stored, seemingly forever. We live in the age of data overflow, with mobile smartphones, Internet of things (IoT) devices, and increased sensorization of business assets adding to a gigantic pool of digital information that grows by the second.


For what purpose? That is the question. There is inherent or implied use for all of the data created in our modern world. But in business settings, it’s easy for relevant, useful information to get lost in the weeds quickly. Workers unable to put their eyes and cursor on the exact dashboard, spreadsheet, report, etc. they can suffer a loss of productivity and miss out on opportunities to collaborate at key moments.


Some data about data:

  • By 2025 humankind’s accumulated data is expected to total 175 zettabytes (for context, that’s just under 175 trillion gigabytes).
  • By comparison, in 2016, Gartner estimated that just over four zettabytes of data were available. The number is expected to reach 2,142 zettabytes by 2035.
  • Seagate says 68% of data in an organization goes untouched
  • For more than a decade, a majority of U.S. workers have reported feeling that the quality of their work suffers because of inadequate time or methodology to properly review their data.


With the pace of data generation only expected to increase as computing becomes more prevalent and powerful, there is a growing need for artificial intelligence (AI) tools to help manage the enormous data pools that can otherwise overwhelm businesses, governments, and organizations of all kinds. SparkCognition’s AI modeling work has proven its worth in manufacturing and other settings for collecting and analyzing the many thousands of sensor outputs generated by industrial machine assets, with the benefit of eliminating unneeded warnings created by OEM systems and reducing the alert fatigue that can cause critical problems to go unnoticed. With subject matter experts no longer tied to control systems to determine which of the dozens of data outputs at a given time need their attention and action, SparkCognition helps them focus on more dynamic aspects of their jobs with potential equipment failures or other issues under the management of powerful AI technology.


How AI helps businesses organize and access data

In business settings, the explosion of asset-generated data has grown into a big enough concern that startups and some leading productivity giants are turning to AI tools to help tame the beast. But there are other connected issues, as well, with the potential for AI to make a difference, from the cybersecurity threats posed by vast stores of customer, business, or government data stored on servers or connected in the cloud.


And all those servers managing data are growing into their own infrastructure issue, currently accounting for 1% of total global energy consumption, with that number on pace to grow unless AI and other resources can be used to handle power consumption better.


One answer to the data problem comes from AI’s ability to identify and eradicate records and data that are no longer relevant. AI’s ability to learn how to “forget” redundant data from an organization’s systems can prevent data overload from becoming a resource issue while making it easier for workers to find and use the documents and assets that are most important for their daily duties.


Another solution focuses on teaching AI to break through silos and bottlenecks by accessing non-sensitive data from anywhere within a company to improve search and conclusions about problems being faced, thus creating a centralized knowledge base that organizes and tags queries to find the most relevant topics and decision-makers. By eliminating irrelevant data and better contextualizing the remaining information, AI can turn an overwhelming problem into a positive opportunity that can improve how a company operates and become more competitive.


AI makes a difference for data at the edge

With the rapid growth of IoT technologies and 5G telecommunications networks, AI is becoming increasingly important in controlling the flow of data to an from so-called “edge devices” that process data locally rather than moving data back and forth to cloud computing networks. Edge computing can occur in retail environments, hospitals, or autonomous vehicles that need instant decision-making to operate smoothly and safely in the real world.


The goal of edge computing is to eliminate computational latency, as well as to improve security for all of the data being utilized in those off-cloud settings. Where AI comes into the picture is in its ability to use advanced neural networks to make decisions at a near-human level quickly without becoming stuck when traditional patterns and rhythms in data analysis take place.


In addition to providing better data security, AI’s use in edge computing reduces bandwidth and cost for all operations. Also, AI’s ability to operate offline makes 5G and other technologies more robust, with the models utilized for computing and decision-making growing more intelligent and delivering better results as they continue to work and train on new, unfamiliar data.


Put your data to work for you

For good reason, there is a growing belief among business leaders that data is the new oil; that insights of every kind about customers, suppliers and the market in general are the essential fuel that will keep businesses running. But there is a pronounced chasm today between accumulated data languishing in hard drives and servers rather than being put to its most productive use.


AI tools are answering this challenge—from integrating into existing workflows and producing insights that can streamline processes to finding improvements for automation that make it possible to direct limited resources most efficiently. SparkCognition enables enterprise organizations to put AI to use quickly and effectively and overcome the growing data challenge: our webinar The Digital Transformation Is Failing: Learn How To Fix It provides insights for maximizing the value of the vast and growing data sources available to business leaders today.

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