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Emergency change: when something on a system, application, or physical device is changed immediately in order to prevent an incident. It can be the result of an incident or of failed change.
Take my word for it; I’ve seen it first hand. Emergency changes are risky.
They are quick, “on the fly” production changes that usually don’t contain back out plans. For this reason, and for many more I won’t dive into just now, it’s critical to have outlined processes for emergency changes.
Risky or not, many organizations often don’t take pen to paper to write out these necessary processes to approve emergency changes. In such instances, I’ve found that people push through emergency changes as new code and skip the approval process all together. Why? Because the approval process by which to submit and review those emergency changes virtually does not exist.
No red tape and no CAB review bureaucracy… sounds like CAB-utopia, right? Wrong. The consequences are far reaching, and they might just catch up to you.
So what’s the bottom line?
Use your CAB to review each and every emergency change that occurs using an after change review. The CAB should assess whether or not it can work to prevent similar emergency changes in the future. It should strive to discover the root cause through deep analysis and should likewise explore ways to eliminate those moving forward.
And, if a large number of emergency changes occur each week, raise those red flags and dig a little deeper.
Maybe you have no clear policy for Emergency Change.
Perhaps you have not identified the true root cause of an incident.
Or maybe you have yourself a trending Emergency Change that needs addressing.
Whatever the case, whatever the cause, emergency changes come up quite a bit in Change Advisory Board (CAB) meetings. Don’t let them slide by. Are you prepared?
Let’s start a conversation.
(Original image by Perspecsys Photos.)
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Category: Monitoring & Analytics
Why ETL Tools Might Hobble Your Real-Time Reporting & Analytics
Companies report large investments in their data warehousing (DW) or Business Intelligence (BI) systems with a large portion of the software budget spent on extract, transformation and load (ETL) tools that provide ease of populating such warehouses, and the ability to manipulate data to map to the new schemas and data structures.
Companies do need DW or BI for analytics on sales, forecasting, stock management and so much more, and they certainly wouldn’t want to run the additional analytics workloads on top of already taxed core systems, so keeping them isolated is a good idea and a solid architectural decision. Considering, however, the extended infrastructure and support costs involved with managing a new ETL layer, it’s not just the building of them that demands effort. There’s the ongoing scheduling of jobs, on-call support (when jobs fail), the challenge of an ever-shrinking batch window when such jobs are able to run without affecting other production workloads, and other such considerations which make the initial warehouse implementation expense look like penny candy.
So not only do such systems come with extravagant cost, but to make matters worse, the vast majority of jobs run overnight. That means, in most cases, the best possible scenario is that you’re looking at yesterday’s data (not today’s). All your expensive reports and analytics are at least a day later than what you require. What happened to the promised near-realtime information you were expecting?
Contrast the typical BI/DW architecture to the better option of building out your analytics and report processing using realtime routing and transformation with tools such as IBM MQ, DataPower and Integration Bus. Most of the application stacks that process this data in realtime have all the related keys in memory –customer number or ids, order numbers, account details, etc. – and are using them to create or update the core systems. Why duplicate all of that again in your BI/DW ETL layer? If you do, you’re dependent on ETLs going into the core systems to find what happened during that period and extracting all that data again to put it somewhere else.
Alongside this, most organizations are already running application messaging and notifications between applications. lf you have all the data keys in memory, use a DW object, method, function or macro to drop the data as an application message into your messaging layer. The message can then be routed to your DW or BI environment for Transformation and Loading there, no Extraction needed, and you can get rid of your ETL tools.
Simplify your environments and lower the cost of operation. If you have multiple DW or BI environments, use the Pub/Sub capabilities of IBM MQ to distribute the message. You may be exchanging a nominal increase in CPU for eliminating problems, headaches and costs to your DW or BI.
Rethinking your strategy in terms of EAI while removing the whole process and overhead of ETL may indeed bring your whole business analytics to the near-realtime reporting and analytics you expected. Consider that your strategic payoff. Best regards in your architecture endeavors!
Image by Mark Morgan.
Do health-tracking wearables actually make us healthier?
When I’m not writing all about the health IT world, I am a personal trainer, and it never ceases to amaze me how often these two worlds collide. The other day one of my training clients said to me – “I’ve gained almost 10 pounds since I got my <insert name of popular health-tracking device here>. Isn’t it supposed to do the opposite?”
I thought about it for a minute.
“Well,” I began, “Do you wear it every day? Have you forgotten it any?”
The client shook her head. “I only take it off to shower and to charge it.”
I thought a little more.
“How have your behaviors changed since you started wearing it?”
Now she looked at me strangely. She shrugged. I asked her to keep wearing her health-tracking fitness band, but to also go back to keeping a journal where she logs in her activities and her food. In addition, I asked her to also log in how often she consults her wearable.
When she came back to me the next week, she handed over the journal. It didn’t take long to see what the problem was. In the evenings, when my client had consulted her health-tracking device (which she does about a billion times a day), she would then consume the exact amount of calories she had remaining in order to come in right at her daily goal. However, sometimes these snacks consisted of highly processes carbohydrates and sugars. In addition, her fitness band had no way of knowing her muscle mass or the speed at which she metabolizes specific types of food.
Technology plays an enormous and essential role in the detection, diagnosis and treatment of many life-threatening diseases. Digital devices monitor heartbeats and blood pressure, all able to be analyzed by the amazing connectivity of the Internet of Things. We can cure and prevent more illnesses than ever imagined before with digestible sensors, hybrid operating rooms and 3D printed biological materials. However, as a fitness professional, I’m not talking about that kind of technology. I’m talking about the kinds of health-tracking gadgets, like wristbands, apps and trackers, that have become as common place as the timeless Timex. Can these wearables really stop, or even reverse, the American obesity epidemic?
The answer is — it depends. In my client’s case, no. Or, not exactly. She was using the fitness band to justify eating poorer quality foods more often. For some people, however, they do work amazingly. I regularly meet marathon runners who worship the Garmin watches that help them track speed, as well as fitness band enthusiasts who saw the fat melt away from the moment they plugged in. The crux is this – in order to live a healthier lifestyle, you have to change human behavior. While these health-tracking devices cannot force behavior change, they can make us more aware of our actions and choices.
Interested in using health & fitness tech to kickstart or continue your healthy lifestyle? Check out CNet’s review of top wearables under $200:
