Enterprise Applications

Enterprise Applications
Any recommendations for data governance software?

Top Answer: I believe the top two are Colibra and Alation.  My recommendation would be to first understand your requirements and ensure you have a data inventory.  Tools are just tools, without people and process you are still dead in the water.  If you are just blindly asking for suggestions without understanding your needs you will likely stumble into a bad solution.

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What tools do you use for insider threat detection?

Top Answer: There are a variety of tools available for insider threat detection. Some common tools include data leak prevention (DLP) tools, user activity monitoring (UAM) tools, and security information and event management (SIEM) tools. DLP tools help to prevent sensitive data from being leaked by identifying and blocking unauthorized attempts to copy or transfer data. UAM tools monitor user activity to identify anomalous behavior that could indicate an insider threat. SIEM tools provide a centralized platform for monitoring and managing security events. By using a combination of these tools, organizations can more effectively detect and respond to insider threats.

What was the easiest high-impact solution you’ve ever implemented?

Top Answer: For the last several years, I've been a consultant with multiple organizations, primarily helping with the effectiveness of IT and digital transformation. One of my customers was a big knowledge process outsourcing company in the US, and I helped them save almost 30% of their annual budget just by asking simple questions like, "Why do you have this, and what are you doing with it?"

What are the key hurdles to blockchain implementation at the enterprise level?

Top Answer: I have been in the IT industry for many years and I've been a CIO for over 12 years. And now I serve on boards, do advisory work and invest with an early-stage VC firm. About three years ago, I took a look into the enterprise blockchain from an investment perspective. I wanted to know if this technology could be used in corporate IT, so we got experts from Ernst & Young who have done a lot of work on blockchain. We came to the conclusion that although there are definite advantages in some use cases, we didn't see a fit for broad adoption in corporate IT. But now there are a lot of changes happening with NFTs, and many companies are setting up private blockchains. When I looked at it the first time, it was too expensive. It makes more sense for supply chain operations, but even then you would still be replacing existing systems. And there are no knowledgeable resources, so where will you get the people to implement and manage the technology?

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What are some best practices for building applications at scale?

Top Answer: When building applications at scale, we need to understand that we are not building for the scale of today. We have to plan for the scale of tomorrow. We are making our platform future-ready by identifying disruptive products and services we can provide to our merchants, but people usually just follow whatever the current hype is in the technology world. When you keep seeing a new buzzword, eventually you feel like you’re out of sync with current technology if you’re not using it. I hear a lot about AI/ML, and ML is definitely in use here, but you need to have specific use cases for AI. And you can’t implement a design pattern just because that’s the trend. You have to figure out what the right design pattern is based on your requirements. Building applications at scale also requires careful thinking about how you can create reusable components. You end up building a lot of components if you don’t make reusable ones, and all of them require testing. Every time you create a component, you have to test it for the functionality, as well as for the non-functional aspects, like the scale security. You also have to put a lot of focus on the architecture of what you are building, because the traditional way of thinking may not work. When we are designing a solution, we often try to fit the end result into the technology we have, rather than defining our end result first and then fitting the technology to that. It has to be reverse-engineered. Otherwise, you will have to change the technology when your requirements change. It is also important that you leverage the tools that are most relevant to that problem, rather than going with what you already know. If the architect has more experience with databases, for example, the solution ends up being inclined towards databases. That is a hindrance, so we have to remove our bias toward the tools we know when building applications for scale. We need to put the end result into perspective, establish what is required, architecturally build that, and then we can choose what technology will solve that particular problem, whether we have used it before or not.

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