Fall Season Marketing
Leaves change, the weather changes, people change…but the IBM i marketplace and its customers, in general, seem to remain the same.
Thanks to their frequency and the difficulty of getting your message through to your intended audience, Webinar marketing is a challenge in 2017. I hear from partners who have been disappointed with attendance at recent events, but regardless of the attendance numbers, the more important concern is getting those who do attend to take the next step. Webinars remain a low cost way to share product and technology information, but you need a compelling subject, not a sales pitch, that raises issues your audience will want to discuss further after the event is over.
What does continue to work in the IBM i market is good old-fashioned personal contact. Calls and in-person meetings have returned as the main way to spur IBM i customers to action. It seems that what is old is new again.
Many IBM i IT people know there are additional products or services which can improve operations, but they are hesitant to sell them internally. Having an external subject matter expert come into the office both lends an air of credibility to their internal argument and gives the IT person a reason to involve decision makers in the discussion.
We can work together to create a marketing campaign where the call to action results in lead qualification and is closely followed by a face-to-face appointment with one of our subject matter experts who will travel to your area.
Watson and IBM i
If you are reselling Watson into IBM i using accounts, I would love to talk with you.
Despite several years of announcements and advertising, Watson is still in the early-adoption stage, particularly once you step outside specific industries like healthcare. NGS has created demos and business case discussions for how to integrate Watson-produced data into IBM i-based reports. They work and they make sense to customers, but very few IBM i customers are moving forward at this point.
We get the usual excuses at first – no time, other priorities, and so on. However, with just a little probing we can attain the true reasons for most small and midsize companies’ reluctance – management doesn't know how it might gain a tangible business benefit from Watson, and the IT department isn't sure how to frame the conversation needed to initiate a project. Small, focused projects targeting a narrow business use seem to have the best chance of gaining interest.
What is working for you in selling Watson services to midsize companies using IBM i?
Many companies seeking to change their ERP application weigh the pros and cons, comparing the cost of change against the benefits of a new system. There are lots of hard costs in everyone’s numbers — hardware, software license, implementation, and customization charges add up quickly.
For many on IBM i, the cost is too high to justify the change, especially when they can stay with a proven system that continues working just as it has done for years. But for some, the benefits of a new system are worth the cost.
Those who do opt for a new system often choose a server system other than the IBM i. In that case, the experienced IBM i technician is usually relieved of his services to the company.
Inevitably, and sooner rather than later, the company realizes it has lost a special breed of IT person. IBM i people are typically more experienced and have worked more closely with the actual line of business departments than the new IT people brought in for the new system. Basically, all that valuable knowledge in translating true business needs into IT processes is gone.
That is a huge cost only understood too late.
We continue to add customers for our Qport Office utility. It is a software application which takes Query/400 produced output and automates the process of delivering the data to the business user’s Excel, Word and other applications.
With the increasing rejection of DB2 Web Query as a viable option for IBM i based reporting, Qport Office is making more sense to people as the next step for easier access to IBM i data without needing an IT person to “walk” the data to the business user’s application. It is especially valuable as a tool since many IT people at small and midsize companies spend more and more of their time NOT working on the IBM i system.
Since so many queries are written in Query/400, companies who depend on the system can use the long existing queries in new ways by letting business users get the data directly from the Query/400 report. This ability can be incredibly useful for companies that lose their veteran IBM i IT person. Business users can sample the existing queries to find reports the IT person had run, formatted and then distributed.
NGS customers who are current on maintenance can take advantage of a variety of valuable, free, and educational offerings. These offerings include online tutoring sessions, share and learn Webinars, on demand videos, and even onsite product reviews with NGS product specialists traveling in your area. Most of these offerings only require an hour or two of your time. I hope you or your coworkers have taken advantage of some of these services and found them very helpful.
But if your company is planning to change to a new ERP system or computing platform, there is probably a multi-year timeline attached to that project. Meanwhile, dozens or even hundreds of employees in accounting, operations, human resources, marketing, logistics, and other departments still need to use your existing software applications and tools every day to help your company meet its near-term goals.
Unfortunately, and all too often, once a major software or platform change is planned, even low and no-cost education related to current applications is deemed unnecessary or a distraction. While that education may not be required anymore for application developers engaged in learning about the future system, it could still hold a lot of value for the business users who will continue to work with your existing systems for one, two, or even several more years.
Take full advantage of your educational opportunities until your company is ready to roll out its new system. Even a few hours saved each month over a year or two in multiple departments can yield a tremendous return on investment. And, let’s face it, enterprise software migration projects routinely take much longer than planned, and you could still be using your current system years from now.
In machine learning, decision trees are a great algorithm family to work with business information. They are not the most precise nor are they considered cutting edge, but they are a first pass algorithm for many data scientists. Maybe in version two of a project, another algorithm family might create a better model for delivering a reliable model, but over most types of transaction or ERP data, decision trees as a class are where most data scientists start.
One of the great things for business use is that decision trees can be deciphered and understood by people. That capability lends them an air of credibility if managers and executives can look at the logic of the tree and follow how the final answer is made by tracking the tree at each branch.
It also lets IBM i programmers code the decision tree splits in familiar programming languages. Realistically, this is the only way decision trees are going to work with IBM i programs natively on the box without making calls out to other servers.
In reality, you'll want to make those calls out from your programs rather than code the decision tree. There are many reasons that go beyond just the simple work of coding hundreds or even thousands of decision points into a program. The easiest way to explain is to ask the question, “What happens when they change the model?”
It will happen. It always happens.
Many machine learning and predictive processes struggle when they encounter missing data; entire records are bypassed if one field value is missing in the algorithm. For example, in a decision tree, if no value exists for the field where the tree splits, that record is useless because the algorithm cannot say what tree branch the record needs to follow.
Most software implementations of machine learning processes get around this problem by offering the data scientist the option of ignoring missing value records or imputing a value. Often, the imputed value is used so as not to waste what is otherwise a good record. Most of the time an average, median, or similar generic value is used in place of the missing value. Null often looks like a missing value, too, and usually receives the same treatment by data scientists.
Most IBM i IT professionals are close enough to operations to know that average values across the entire database are unlikely to be good substitutes. Using domain knowledge, IBM i professionals can easily create levels or classes based on experience that better substitute for the missing values. This work is best done on IBM i before it gets to the data scientist.
Someone messaged me to point out my title of the “Green” Revolution last week might also refer to IBM i and its heritage with green screen terminal interfaces.
I think that idea is a valid and reasonable path of thought to go down this week. Machine learning and advanced analytics need real data to create meaningful and useful models. IBM i is at the heart of your real data – as in data that is really useful. IBM i contains transaction records, customer records, payment records, and other concrete data points for businesses.
While your IBM DB2 on i database probably does not store production machine sensor data, product environmental condition information, or other larger volume data flows, those same data flows almost always need to be tied to the transaction, product, and customer data from IBM DB2 on i to create useful machine learning models, advanced analytic visualizations, and so on.
IBM DB2 on i data is critical to the success of many commercial analytics projects. I wish IBM would give a nod to that heritage in its current marketing.
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.
In a recently completed survey of non-customers, we found that analysts who used Query/400 reported spending an average of 1.625 hours per day extracting, manipulating, and distributing data. We know from previous studies that people who move to NGS-IQ typically cut the time they spend on these tasks by approximately 50%.
That reduction in time is due to NGS-IQ having many more features which let analysts and business users write and run fewer queries and automate data transfers, spreadsheet updates, and report distribution. The math works out to 0.8125 hours per day in labor savings or about 10% of an eight-hour work day. Using a national average of $70,000 annual salary for a business analyst, the financial savings equate to $7,000 per year.
This productivity savings doesn't include the intangible business value and impression you make on your customers when staff members regularly have meaningful, accurate, timely data at hand.
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