Decades of empowering efficient data decisions
Updated: Jan 11
How IBM has helped businesses organize, store, and leverage their data from the 1920s until today.
Data is everywhere. It grows exponentially year by year, and it is our duty to keep up with its overwhelming volume and complexity. The thing is, we’re so focused on conquering our data that we often forget this battle to understand it has been one we’ve been fighting since the beginning of time. However, we’ve always overcome this and been able to synthesize and communicate our data findings throughout the years.
Data complexity simplified by the digitization of data storage
One of the most prevalent times in data evolution was the Information Explosion of 1961, in which there were tremendous economic and technological innovations due to a rapid increase in the production rate of new information. This sudden overload of information was overwhelming to the masses, which resulted in numerous companies being unable to make clear and accurate decisions due to the newfound complexity and volume of their data.
IBM’s contributions to developing digital data storing technology set the precedent for the standardized method of data storage for years to come. This method was later justified in 1996 when digital data storage was proven to be more cost-effective than storing information on paper, as stated in the 2003 IBM Systems Journal paper, “The Evolution of Storage Systems.”
How has IBM helped businesses organize, store, and leverage their data from the 1920s until today?
“We are a business with a single mission – to help our customers solve their particular problems through the applications of data processing and other information handling equipment.” – Thomas Watson Jr., Former Chairman and CEO of IBM, 1970
IBM has long been a data leader throughout history. As a company, we have been entrusted with organizing data on a national scale, made revolutionary progress in data storing technology and have exponentially advanced trustworthy AI using aggregated structured and unstructured data from both internal and external sources.
Here are some of the key moments in IBM’s data and AI journey that showcase the evolutions of organizing, storing and leveraging data.
Did you know IBM helped with organizing data at a national scale?
1928 Punch Card & U.S. Census:
IBM punch cards became the industry standard for the next 50 years, helped organize U.S. data at a national scale, and enabled other large-scale projects like the US Census.
1936 Social Security made possible by IBM:
IBM worked with the U.S. government on the U.S. Social Security Act of 1935 and tabulated employment records for 26 million Americans. This was the largest accounting project of its time and really helped demonstrate IBM’s bandwidth for organizing data.
What does the evolution of data storing technology look like at IBM?
The System/360 ushered in an era of computer compatibility—for the first time, allowing machines across a product line to work with each other.
1970 Relational database:
The relational database called for information stored in a computer to be arranged in easy-to-interpret tables which allowed non-technical users to manage and access large amounts of data. Most databases today still use this structure.
1971 World’s first floppy disc:
Invented by IBM, this is one of the industry’s most influential products ever, making data storage more powerful, affordable, and portable.
1981 The IBM 3380:
The IBM 3380 –a storage solution meant to be used alongside a computer– gave users the ability to store up to 2.52 billion characters of information, while new film head technology allowed data to be read and written at three million characters per second—two-and-a-half times the previous rate.
2021 2-nanometer chip:
With 50 billion transistors on a fingernail-sized chip – the densest to date – this IBM innovation holds the potential for greener data centers & safer autonomous vehicles.
How has IBM leveraged data for AI Advancement?
1956 AI Before AI:
Arthur L. Samuel programed an IBM 704 – a large-scale computer designed for engineering and scientific calculations– to play checkers and learn from its experience. This is considered the first demonstration of artificial intelligence.
1997 Defeating the reigning chess champ:
The IBM Deep Blue supercomputer defeated the best chess player in the world at the time. This led to thinking computers taking a giant leap forward towards the kind of AI that we know and use today, such as current image or speech recognition used on cellphones.
2000 Deep Learning:
Deep learning attempts to mimic the human brain and helps with enabling systems in clustering data and making predictions with incredible accuracy. It has raised the bar for image recognition and even learning patterns for unstructured data.
2022 The Mayflower Autonomous Ship Project:
With no human captain or onboard crew, the Mayflower Autonomous Ship (MAS) used AI and the energy from the sun to travel further into the ocean to uncover more unexplored parts of the sea. The ship has recently docked in Plymouth, Boston on June 30, 2022.
IBM’s most recent moves in Data & AI
The volume of data continues to grow exponentially, and organizations are faced with challenges due to managing the quality of their data, research states that,
Bad data costs companies an average of $15 million.
73% of business executives are unhappy with their data quality.
61% of organizations are unable to harness data to create a sustained competitive advantage.
Thus, why we have made efforts to help companies improve their business practices through data analysis. IBM’s data fabric approach prioritizes helping enterprises elevate the value of their data architecture, and through initiatives such as Customer 360 –which helps to reduce data quality issues in applications and optimizes business’ insights on customers.
Most recently IBM has acquired Databand.ai, the leading provider of data observability software that helps organizations fix issues with their data before it impacts their bottom-line –including errors, pipeline failures and inadequate quality.
This acquisition highlights our dedication to helping companies improve their businesses and highlights our continuous evolution of data and AI innovations.
Ultimately, as a data leader our goal is to help you organize, store and leverage data while deriving insights from complexity. Data is everywhere and will continue to grow exponentially, but the more quality data you have, the clearer you see.
By Alexia Tsimiklis and Catalina Sanchez and Cody Murray-Bruce