How Data Flows Within An Organization

. <span>Photo by <a href="https://unsplash.com/@markusspiske?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Markus Spiske</a> on <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a></span> <img src="https://miro.medium.com/max/3600/0*nm3fnRPz9ZhlwxVv" width="auto" height="250px" class="mb-5" /> There are generally four steps for which data flows through within an organization. The process starts by collecting data, most often from web traffic, surveys, or media consumption, and then it is stored in its raw form within a database. The next step is to prepare the data for its intended use case, where the data goes through cleaning and formatting. We would find missing or duplicate values and convert the data into a more organized format that improves the quality. In doing so, we increase the overall productivity of the dataset. Once the data is clean, it is aggregated for exploratory data analysis, visualizing the data, and building dashboards that provide insights. During this stage, the relationships between the dataset’s features are uncovered to determine the right statistical model. Lastly, experiments can determine whether a model can be used to accurately predict business trends. # Data Engineers Data engineers are responsible for the first step of this process, consisting of collecting and properly storing the raw data needed by an organization. The data engineer essentially provides the organization with the prerequisites to efficiently extract the essential data, so it is all in an easy-to-access format. Without the data engineer, then the data could be scattered around, corrupted, or just difficult to access. With unstructured data, the rest of the team cannot explore or experiment with the data since the information is not organized to any standard. Data engineers need to collect the correct data, make sure it’s in the right format, make it accessible to the right people within an organization, and do so as efficiently as possible. # Big Data The data engineer is responsible for the entire process of collecting the organization’s data from various sources and optimizing the data for analysis by cleaning and removing corrupted data. As a result, they then have to develop and test different methods to maintain the data architecture created to manage the data. The data engineer needs to do this for massive amounts of data because organizations are increasingly handling loads of data at an accelerating rate. The existence of data engineers becomes from the rise in big data. Big data is when the data generated come at large volumes and a fast pace, for instance, social media or e-commerce data. Typically, large datasets are characterized by their features volume, variety, velocity, accuracy, and value. # Data Engineers & Data Scientists With the data engineers laying down the proper data infrastructure for the organization, the data scientist then handles the rest of the data flow chart. In comparison to the data engineer, the data scientist exploits data according to the business needs of the organization. Data scientists utilize the tools created by data engineers to provide the organization with insight into their business. Due to this data engineers have stronger software skills, while data scientists have stronger analytical and statistical skills. # Data Pipelines “Data is the new oil” in the internet economy. In sticking with this analogy, crude oil is extracted from the ground at a fracking facility, where they transfer the oil to a storage unit that distills the oil to its proper use case (gasoline, diesel, kerosene, along with other oil products). A pipeline is created to deliver the oil products to the proper customer or distributor. So the pipeline either leads to the end-user or it leads to another pipeline that further filters and refines the oil; where depending on the type of product the pipeline differs. For instance, the gasoline provided to vehicle owners has a different supply chain than the kerosene supplied to airlines. A data pipeline works in a similar fashion where a company collects data from multiple sources, which then has to be processed and stored in various ways depending on the intended business use. Data pipelines handle this data flow, which allows the data scientists to access the most up-to-date information within the organization and make sure that they are accessing the correct information. Data pipelines ensure that the flow of data is efficient and with minimal human invention and error. [Data Normalization In Python](https://www.digitalstream.dev/post/7/) [Python Packages For Data Science](https://www.digitalstream.dev/post/6/) [Privacy In A Big Data Economy](https://www.digitalstream.dev/post/4/)