3 Data Science Trends To Watch in 2020
Big data has never been a bigger deal. Organizations as diverse as direct-to-consumer lifestyle brands, distributed software services, and political campaigns all rely on access to accurate, granular information about the characteristics of their audiences and how they behave. It’s no surprise, then, that the field of data collection and analysis is a dynamic and exciting place. Ahead, learn about three of the most interesting new advancements in data management today.
Understanding Complex Data Flows With Speedy Cleaning
Before data is useful, it has to be inspected and cleaned. During this process, incomplete and inaccurate data is discovered and repaired. The data cleaning process involves deduplication, correcting spelling and syntax errors, standardizing sets, and deleting or filling empty fields. New approaches to cleaning data take a more visual, less technical approach so that companies can free up their best specialists for more complex tasks, allowing a greater number of less-experienced users to expedite the cleaning process. This is especially crucial because cleaning and fixing datasets is often the lengthiest part of the analysis process, but it cannot be skipped, since insights generated from noisy datasets are often wrong.
Predicting Outcomes With Autonomous Machine Learning
Aggregated, multi-source data sets are often large, unwieldy, and even a little chaotic, which makes drawing conclusions from them challenging for most humans. However, recent advancements in the machine learning field means that artificial intelligence algorithms are increasingly effective tools for analyzing data and making robust predictions, and they can be useful even in organizations that aren’t tech-first.
By using data that has been collected and organized to set parameters, machine learning can use a supervised algorithm to generate actionable inferences that inspire new ideas. Alternatively, data scientists may use an unsupervised algorithm that discerns unseen trends in undifferentiated data to compare their own insights with machine learning processes. Using both tools can be helpful for groups or businesses with complex concerns that benefit from multi-stage analysis. As predictive algorithms become more accurate, analysts will spend more time working alongside these intelligent machines instead of simply guiding them.
Acquiring New Datasets With Connected Devices
Researchers and analysts are accustomed to the flow of data that comes from consumers’ computers, phones, and tablets, but the proliferation of networked home hardware vastly increases the amount and type of data available, as users interact differently with each type of device. Each touch point or sensor measurement creates a data point, which can be saved and sent to servers for analysis. While common understanding of the Internet of Things revolves around consumer applications, like smart fridges that notice when certain ingredients are depleted, applications in the industrial world are even more diverse.
Existing protocols like SCADA and MQTT make it easy for companies that already use automated industrial processes to integrate networked data collection into their workflows. One of the most promising uses of IoT data collection and analysis is smart logistics and supply chains, in which automated systems can take responsibility for designing the most efficient possible delivery routes in real time by considering vehicle and driver availability, traffic and weather conditions, and priority levels for various shipments.
Even though more businesses and organizations than ever are already realizing the benefits of advanced data collection and analysis, cutting-edge developments in the field that make collection easier and analysis smarter are expanding these tools to new industries, allowing more groups to improve services and outcomes through information-driven strategic planning.