Dataception

Businesses that realize the value of their data and make the effort to utilize it to its greatest potential are quickly outcompeting those that do not. But like any complex system, the architectures that utilize big data must be carefully managed and supported to produce optimal outcomes. One of the chief obstacles in this continual process is the isolated nature of many data environments these days. Whether in the data center, colocation facilities, or the cloud, data silos prevent the kind of data integration that is needed to excel in a digital economy. This isolation is not only physical, but categorical as well. Multiple processes tend to create volumes of increasingly diverse data sets – such as sales data, finance data, even maintenance records – that at first glance might not seem to influence each other but in fact provide broader views of the truth when analyzed in tandem. The value of data, after all, is enhanced primarily through its relationship and influence on other data...

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. You might not even make it out of the starting gate. So, what can you do to ensure your data is up to par and you’ve achieved data trustability?  Data lakes, data pipelines, and data warehouses have become core to the modern enterprise. Operationalizing these data stores requires observability to ensure they are running as expected and meeting performance goals. Once observability has been achieved, how can we be confident that the data within is trustworthy? Does data quality provide actionable answers? Data observability has been all the rage in data management circles for a few years. What is data observability? It’s a question that more and more businesses are asking as they strive to become more data-driven. Simply put, data observability is the ability to easily see and understand how data is flowing through your system; it’...