Data Pipelines

Drilling for Data: The New Black Gold

In the age of Big Data (an overused but accurate term), few resources contain more inherent value that the information populating servers and hard drives around the world. Six of the top seven companies in the world (as measured by overall market value) are technology companies, and make no mistake that one enormous competitive advantage these companies enjoy is the ability to obtain, store, and ultimately navigate literal oceans of data.

The good news is that you do not need an ocean of data to be able to generate actionable insights into your company’s performance. Predictive models and artificial neural networks can be developed on standard laptops using open-source programming languages like R, python, and newer arrival Julia. In this environment, you do not need to be a Google or Facebook to use these tools to make better and more informed decisions.

What you do need, however, is data. Specifically you need data that contains relevant information regarding the challenge or opportunity you want to address, and you need it in a format that matches your modeling goals. Creating an effective and stable pipeline is the first step to generating value from your data, and it is often the most intensive stage from a programming standpoint.

As modeling tools become easier and easier to use, effective data extraction remains a common bottleneck across companies large and small. The difference is that large companies have teams of data scientists building back-of-the-house pipelines to feed their analytic engines. For smaller companies, Dead Reckoning Analytics & Consulting can perform much the same role, designing custom pipelines that refine your data into a usable form. Reach out to learn more about what we can do for you.