3 Foundational Practices for IoT Data Processing

IoT isn't just a capability you plug into an existing enterprise architecture. It requires validating existing capabilities and investing in new foundational ones. And what is needed depends on whether the business has done any data integrations with sensors before, the volumes and types of data being collected, and whether the business opportunities around IoT are customer facing.

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Why Tech Sales Professionals Should Read Driving Digital

Being a SaaS or enterprise technology sales professional isn't easy.

It's hard to get meetings. It's hard to understand the industry, business challenges, the full breadth of technology capabilities of the products or services being sold, and the competitive landscape. It's hard developing meaningful relationships with technology and digital executives. It's extraordinarily hard to close sales.

When a sales professional does win business, it's even more important for them to stay involved and make sure the buyer has a good experience, gets what they paid for, and achieves business value from their investment.
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Is AIOps the answer to DevOps teams' ops prayers?

DevOps teams have a two-front battle to keep enterprise and customer facing applications, databases, APIs, and data integrations stable, performing optimally, and secure.

On the one hand, there are all the proactive developments that DevOps team want to, and need to, spend their time on including automating CI/CD pipelines, configuring new infrastructure as code, patching environments, addressing security vulnerabilities, and

On the other hand, there’s the day to day work of responding to outages, disruptions and incidents, performing root cause analysis, and reviewing key operational KPIs and analytics.

Can AIOps, the ability for DevOps, IT Ops and NOC teams to leverage AI and machine learning in IT operations, help DevOps teams shift more of their time from incident response to more strategic work?
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How Business Transformation is Fundamentally Different from Innovation

An interesting debate transpired during the last #CIOChat on Thursday at 2pm around Myles Suer's question, "How is #innovation fundamentally different from business transformation?" I had significant feedback to my tweets and decided to dedicate this week's blog post to detail my perspective.

I provided a two-tweet answer
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10 Questions before starting a Machine Learning POC

37% of organizations have implemented artificial intelligence in some form according to Gartner. Since AI is a a broad category of algorithms covering cognitive, natural language, pattern recognition and other areas, I suspect the number of organizations experimenting with machine learning or deep learning is a lot smaller.

Yet there are daily stories of different organizations benefiting from machine learning algorithms. This week I read about Frito Lay using ML to determine chip texture, MIT reporting on ML used to create extra-delicious basil, and an ML that helps to detect gunfire.

Last week I posted a more futuristic prediction  when software architecture will be driven by machine learning. This week I want to follow up with these more practical steps on getting started with machine learning.
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Beyond microservices; Software architecture driven by machine learning

It's not a question of if, it's a question of when and how AI and machine learning will change our programming and software development paradigms.

Today's coding models are based on data storage, business logic, services, UX, and presentation. A full stack developer elects to build a three-tiered web architecture using an MVC framework. An IoT application calls for an event-driven architecture with services processing events and broadcasting state changes. 

These two architecture paradigms converge with microservice architectures where user interfaces are just one type of interaction node fulfilling high level functions by interfacing with many services.
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