Archives: May 2017
Good quality data is never a bad thing. For fueling analytic processes, it is a must. In order to maximize return on the investment in machine learning and predictive analytics, companies need clean data as a foundation for analysis. (My use of “green” in the title refers to making money for those outside the US.)
Facing some real facts, no one is going to do machine learning on IBM i — not going to happen.
However, for many companies IBM i holds important data which is needed for creating meaningful processes based on machine learning. Getting that data to a machine learning environment seems like a no-brainer; just extract the data and send it over. In the real world, many data fields in databases on IBM i need a little massaging to use effectively in other applications.
Big picture problems include multi-member files. Those are almost impossible for non-IBM i based tools to deal with. I have seen companies where the analysts didn’t know about a file being multi-member, so when they wrote an SQL statement to retrieve the data, only data from the first member was pulled. As a result, they wasted precious time trying to figure out the problem before they were forced to throw in the towel and talk to the IBM i people. Another common challenge is dates stored in non-date fields, or worse yet, stored in multiple fields — with one field for the century and year, another for the month, and another for the day.There are a few other pointers I will elaborate on in the next few weeks.
Descriptive Analytics on IBM i
While the term “descriptive analytics” is not brand new, it is unfamiliar to most people in the IBM i ecosystem. Despite all the hoopla surrounding advanced analytics and hot new technologies of analyzing data, old-fashioned reporting still dominates how companies interact with their IBM i databases. However, descriptive analytics is simply another way of saying query and reporting.
Traditional reporting methodologies are not sexy, but they are useful. That level of practicality is embraced by the vast majority of IBM i professionals. Relating to these people by using both new and old terms simutaneously is a quick way we have found to build trust. Being able to equate the new with old concepts is comforting to IBM i professionals who have seen the marketing spin change throughout their careers.
Supplying Watson with Data
You could not be blamed for being a little confused as to what Watson is - it is so multifaceted that narrowing on one area means you completely ignore other possible uses for it. IBM, trying its best, is messaging everything simultaneously.
Regarding our customers, a few are dabbling in some of the advanced analytic functions, but most are playing with Watson more than meaningfully using it.
Many of those who have experimented with these advanced functions have concluded that good data going in to advanced analytics is crucial (long known) and that NGS-IQ is great for getting that good data together from IBM i ERP databases.
Keep that nugget of information in mind. Getting clean, targeted data together using NGS-IQ on IBM i is easier than uploading a lot of random data and then using code on Watson to filter it.
Unlike past years, NGS will extend its travel plans into the summer for visiting customers and prospects as well as attending conferences and trade shows. We have had customers tell us that summer is a great time for refreshers and skills update sessions.
Regions we will be traveling to include the mid-Atlantic, Southern California, and the Great Lakes region.
We are actively looking for additional areas we can visit in order to start some prospect evaluations. If you would like to organize an event or some customer meetings, contact me and we can probably arrange some time for joint sales calls.
There is a lot of confusion as to what companies can do with IBM Watson. At least Watson is architected so that any system can access it by processing via program calls over the Internet. Programmers need only to communicate input and receive output to make use of Watson-based analytics.
That is the main message you hear from IBM, but it is only part of the story.
Watson is not a magic box which mystically does whatever you tell it to do. Someone needs to create procedures and analytical models that produce a result from a future input. It is not much different than creating a formula in an Excel cell that uses the value in a second cell in a calculation. In this example, Watson is equivalent to the first cell — consuming the value in the second cell to create an output. In operation, your program from IBM i supplies the value in the second cell so that Watson can process the formula. The missing key in the marketing is that someone needs to create the formula in the first place.
Certainly, the analysis is going to be done on a platform other than the IBM i, but for businesses with critical data on the i, knowledge of the existing database and the historical information are vital in making that data useful for creating analytic models in Watson.
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