Systems | Development | Analytics | API | Testing

Data governance is the process of managing and protecting data throughout its lifecycle. It involves establishing policies, procedures, and standards for how data is collected, stored, used, and shared. This requires systems that are complex to be put in place by several stakeholders across the organization. Many organizations look at selecting the right software to implement a framework.

A few months ago, a professor at San Diego State told me that most businesses do not listen to their customers. I had always thought ALL well-run businesses knew who their customers were and listened to them. I was wrong.

Five things to know about how to get data from multiple sources: These days, organizations have more data at their fingertips than ever before and collect an incredible number of data sets from various sources. This creates a paradox for businesses such as e-commerce retailers struggling to deal with data complexity. With a deluge of information (and more arriving every day), how can you get data from multiple sources efficiently and unlock the hidden insights that it contains?

Many organizations focus on the data engineering or development qualifications they require to connect specific data sources and manage data projects. But that is only half of what is needed. Soft skills are so important and sometimes overlooked. Soft skills support data management success because they help individuals effectively communicate and collaborate with others, understand and anticipate the needs of stakeholders, and make data-driven decisions.

5 important differences when comparing MongoDB vs. Redis.

Do you know where your data is? Most organizations store data in various destinations (in-house databases, SaaS locations, cloud-based apps, etc.), which makes running analytics far more complicated. Imagine pulling data from all these destinations into one data warehouse or data lake. Life would be so much easier... "But doesn't this require a lot of code?" you may ask. Not necessarily.

There are few topics in the world right now that are hotter than data and its related fields. As technology, machine learning, and computer algorithms continue to expand, so does the way that companies can use this information to benefit their business. Extract, Transform, and Load (ETL) remains one of the most important processes in the area of big data. This area is absolutely booming, and so is the demand to learn more about its various processes and components.

I just got an email from a venture capitalist. For about the hundredth time, the venture capitalist told me they were anxious to invest money in us. The only qualification was that we needed to already have at least $10 million in sales. If we had $10 million in sales, we wouldn’t need to be talking with the venture capitalist. How stupid is that? I suggested to the venture capitalist that they go invest in IBM or ATT because they do have $10 million in sales.