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IT Operations

It's a Black Friday also for IT!! 6 successive thoughts on dealing with Big Data on the day that data comes to town.

In 2016 and beyond, if you want to compete in retail, you have to be ready to deal with the biggest big of Big Data. Here are my thoughts and conclusions:


1) On Black Friday and Cyber Monday, the many V's of Big Data (Volume, Velocity, Variety, Veracity, etc.) are at their pinnacle. That's why the success of any organization on these days - more than on any other - is dependent on successful digital operations.

2) Even if you do manage to perform well on these days, the biggest "V" here will always be Value. How do we get value out of so much data that is coming at us so quickly and from so many sources?

3) Is it still Value the day after? Dealing effectively with big data on The Day requires capabilities to analyze and act upon data - in real time. This is clearly a painful point for many IT professionals and I'm sure many believe it is simply not possible.

4)  If you are a CIO of a major retailer, and your  website's page load times are averaging more than 30 seconds, you are losing. IT needs to be equipped with insight into what’s going wrong but you also need  guidance about how to fix it - NOW.

5) If you area CTO of a major retailer, you might be overwhelming your department with a constant torrent of  tools that will most likely require individual attention or management. You may seemingly be helping them ingest the data, view it graphically and record it. You might not necessarily be helping them solve the problem of extracting value from big data.


6) Perhaps drowning in a sea of data is an opportunity to adopt new mechanisms to assist your team. As CIO, CTO or any C-level manager responsible for your company's data management success, it may be useful to focus primarily on the pain points that are putting you most at risk. In 2016 and beyond its time to:

  • Embrace process automation. Establish a standard approach to automation so that the endless repetitive, inefficient tasks required to streamline system management are transformed into predictable, scalable, and simplified processes.
  • Depend less on people. It's no secret that there is a global shortage of data analytics scientists. It's also no secret that people - even the smartest of us - are prone to error. Machines do not demand shorter working hours or make mistakes and they will relieve the workload for those people you strive to retain.
  • Increase real-time visibility: Data peaks are all about now. reviewing or attempting to analyze yesterday's logs will help you tweak for the future but will necessarily give you diminished value when it most needed.
  • Fix issues fast! Armed with 'all of the above' means that your team is at its most efficient and drastically reducing MTTR becomes feasible.
  • Be more proactive: A proactive IT manager can handle any crisis. Although reactive is any IT managers middle name, putting into place effective processes, plans and monitoring will assist your team in controlling risks, reducing costs and mitigating the effects of problems that do occur.

7) IDC claims that the amount of  data we create and copy annually on the planet is set to grow to around 4.4 zettabytes  or 44 trillion gigabytes in just 3 years from now. It's the Thursday before Black Friday and I'm sure you've got your hands full and I apologize for writing this at the last minute!

Meanwhile, if you get a minute, I'd like to ask - what kind of big data challenges do you face on Black Friday or Cyber Monday and how do you tackle them? Are you seeing the light at the end of the Black Friday tunnel?

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Loom Systems delivers an AIOps-powered log analytics solution, Sophie, to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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