The AIIM Blog
Keep your finger on the pulse of Intelligent Information Management with industry news, trends, and best practices.
Data Management | Enterprise Content Management (ECM) | Intelligent Information Management (IIM)
This is an article about names and whether or not they matter. My first intuition was to open with the quote, “What's in a name? That which we call a rose by any other name would smell just as sweet,” but I thought better of it. Not because it was too cliche, which it was, but because it immediately transported me back to my sophomore year of high school, when the greatest achievement of my life up to that point was winning the role of Romeo in the drama club's production of Romeo and Juliet. Please spare me the reminder of my nerdy teenage years! Anyway, onto the matter at hand. The purpose of this article is to begin a conversation about data and content and whether or not the long-held distinction between the two is still important. I'm admitting to you upfront that I don't have the answer. Instead, I only aim to share what I know.
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Companies are dealing with more data than ever before. In a complex IT environment, the challenge of maximizing the value of that data can be daunting. Everyone in an organization wants to excel, but many do not have holistic approaches for measuring the ongoing status and enhancement of their business data. Not doing so can result in lower overall company performance, incomplete or incorrect data to drive decisions, and undue stress, driving lower overall employee satisfaction.
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Making an ECM implementation successful requires planning and attention to detail. The best way to create the right solution is to identify organizational goals and priorities. Learn how to manage a successful implementation in our free guide.
Blockchain is one of the most important new technologies that has impacted the business world in the last decade. Along with cloud computing, artificial intelligence (AI), and robotic process automation (RPA), blockchain is transforming business operations and challenging traditional methods the world over. For those who are considering adoption for one of its many applications, blockchain can hold great promise.
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In 1989, I took my first decision sciences course and started coding in SAS at the age of 20. I greatly enjoyed pulling discoveries buried within mounds of data, although and even small datasets had many discoveries back then. At the root of every model I’ve built, even the simplest, was a solid understanding and foundational rigor of statistical theory. When computing simple statistics or developing descriptive models, I thought through the math behind the model and how this would impact the formation, application, and interpretation.
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Data Management | Document Management | Enterprise Content Management (ECM)
Since the 1980s, relational databases have been used to store business information. They were a huge step forward over hierarchical databases, which organized data into rigid tree-like structures with connections between data elements defined by the links in the structures.
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In many cases, hundreds of millions of records can be handled with split-second response times, especially when dealing with aggregate queries on modern hardware. But today, a considerable number of organizations are clinging to 90s ETL technology. ETL seems to be an addiction for IT organizations because they still consider millions of records a lot of data. But having more memory than data may allow for these organizations to have their cake and eat it too. And today, getting to this point is dirt-cheap.
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