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If you had a magic wand - what's the #1 daily business challenge you'd eliminate?

Top Answer: Without a doubt - Technical Debt! It's a ball and chain that creates an ever increasing drag on any organization, stifles innovation, and prevents transformation.

ERP Usage and ChallengesERP Usage and Challenges

How are decision-makers using Enterprise Resource Planning (ERP) tools? Benchmark with your peers.

We are about to select Dow Jones RiskCenter Third Party API for screening of our business partners. Has anyone already experienced it? Any advice?

Top Answer: Sorry, I have not had any interaction with Dow Jones RiskCenter, nor used their Third Party API. 

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Thinking about deep machine learning, how ready is your data (cleaned, prepped and labeled) for compute-heavy data models?

Top Answer: Probably depends on age and size of business. Ours has divisions over 100 years old or acquired less than 2 years ago. We struggle with data quality even within a division. Note that our smallest is probably 25,000 employees and largest 40,000. Total company 130k people, 15 major ERPs and probably 20 minor ERPs. We still have debates on Customer address and shipping fields sometimes during consolidations. My estimate 35% of the data we would like to use is ready

What are your biggest concerns as AI capabilities become a standard industry offering? 

Top Answer: If AI is done right, it's a huge opportunity for technology; if we get it wrong it’s a huge slippery slope. There's already been evidence, in some cases, of the consequences involved if we mess up broad aspects of AI: a few years ago, Microsoft put out a chatbot that didn't have the right monitoring and people were seeding it with harassment and hostile language. That basically trained the chatbot to say inappropriate things back to people because people had flooded it with all this negative stuff. There have been instances of AI that have already had severe consequences, like the criminal risk assessment algorithms that are used by some courts to determine if people will reoffend. There was a research study done by a group of PhD students that found that this system was substantially discriminatory against African-Americans because of the way in which the data was put into it. It used machine learning to determine somebody's likelihood to recommit crimes based upon the anchor point of a historical bias, which the machine then learned to continue going forward. I've worried a ton about that at Cylance and even during my time at Intel.

Data Centers in 2021Data Centers in 2021

Have your data center priorities changed? Benchmark where you’re at with data centers against your peers.

What advice would you give IT organizations who are overloaded with requests?

Top Answer: Our organization is forward-thinking in terms of their KPIs, so we have invested in visualization tools to create a self-service business intelligence (BI) interface. Otherwise, users will keep coming after you for one piece of data or another. We’ve chosen tools that give people that opportunity to roll up, drill down, etc., at their own level. If people don’t have that ability, you will have a lot of requests coming to IT, and then IT can’t do what it’s supposed to do at that point in time.

If you are a current SAP customer, when do you plan to migrate to SAP S/4HANA?

Top Answer: No plan to migrate soon.

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What’s the most challenging part of business intelligence (BI) or data warehouse implementation?

Top Answer: The most challenging part is that every user wants everything immediately. Every technology in today's world has to be data-driven, so the biggest challenge we’ve had to solve is real-time data availability. When we were thinking about how to make this possible, we did not want to invest because real-time data involves a lot of information exchange between the source and the target system. If something fails, then you have to put in a lot of manual effort to correct that information. Then whatever productive work you had planned for that day goes out the window, because business as usual (BAU) becomes more important. So defining the architecture and the kind of KPIs that you’ll need at the starting point is essential. If those aspects are not taken care of well, we’ll have to do a lot of reworking. We cannot go to the source over and over again, because our production systems are built for transactions, not for analysis and reporting; the data warehousing systems are built for that. If we have not done the base architecturing work correctly, it will require a lot of maintenance. To solve this issue, we have invested in change data capture (CDC) tools that help us move the data on a real-time basis. We have also automated many things, which helps reduce the manual effort needed.

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How much of your IT budget would you allocate towards good data hygiene?

Top Answer: Seems like close to half aren’t putting more 3%. A good follow up would be why? What’s are the other top priorities?

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What are the best resources to learn Kubernetes?

Top Answer: There are several useful courses that can allow one to learn Kubernetes. What specifically are you wanting to focus on? System design and planning (Architecture), Operations, Dev Ops, or just general working knowledge? Are you looking at self hosting, Cloud implementations, etc.?