Here’s a new spin on scraping and parsing from Connotate’s blog, Web Data Insider. The recent emphasis on predictive analytics has writer Laura Teller discussing “The Data Supply Chain… and Why You Should Get One.” She reminds us that businesses now do much more with data than they used to. In fact, she asserts, any company that invests in data analytics possesses a critical advantage. Of course, as a prominent web-data extraction firm, Connotate does have a dog in this fight; at the same time, Teller has a point—for many businesses, especially larger ones, data analytics can be an indispensable tool.
Companies put considerable effort into streamlining their supply chains for other resources, so why not data? The article elaborates, and gives us a checklist for investigating our own data-supply needs:
“Once we start conceiving of data as a critical input or a brave new resource, it changes the paradigm of how we think about it, manage it, and leverage it. Data is no longer just an artifact of the ‘real work’ of companies. Rather, it’s something that has to be strategically sourced, managed, and leveraged. Just as companies have supply chains for other raw materials, like sugar, steel, electronic components, etc., they have to think about data in the same way and with the same rigor. They have many decisions to make:
*What to get and where they’ll get it
*How to ensure supply
*How to protect their ability to get it
*Who they’ll source from and how they’ll manage them
*What to pay for it
*How to store it
*How to refine it and add value to it
*How to package it for sale”
Teller notes that her company welcomes this “paradigm shift,” which is no surprise, considering that they are well-positioned to help customers address this burgeoning need. The company’s platform has been named a KMWorld “Trend-Setting Product” a healthy nine times. Based in New Brunswick, New Jersey, Connotate was founded in 2000.
Cynthia Murrell, October 27, 2014
Phi Beta Iota: The craft of intelligence, when performed with integrity, uses the intelligence cycle to convert information into intelligence — to create value. As Robert Steele started saying in the 1990’s, “information costs money, intelligence makes money.” Among the many issues with big data are the burdens of legacy — data resulting from industrial-era processes managed in industrial-era ways, and not as part of an intelligence ecology focused on ephemeralism — doing more with less.