The act of data analysis is usually limited to a single, already prepared dataset. (It’s generally agreed that other slices are other activities, from collection to storage to visualization.) Data analysis consists of cleaning, transforming, modeling, and questioning data to find useful information. Sharing the data to business users or consumers, often in a dashboard or via specific storageĬonsider data analysis one slice of the data analytics pie.
Analyzing the data to extract patterns, trends, and insights.Performing ETL ( extract, transform, load).Storing the data in hot, warm, or cold storage.Managing the data, usually in databases, data lakes, and/or data warehouses.Categorizing the data into structured/unstructured forms, which might also define next actions.The data analytics practice encompasses many separate processes, which can comprise a data pipeline: Data from a variety of IoT devices in a certain environment, such as your server room, a power station, or a warehouse, could indicate whether you’re providing the safety and reliability you need at the lowest cost possible.An increase in cyberattacks might mean you need to take proactive preventative measures.A bakery might use its data to realize its demand for bread bowls increases in the winter-which means you don’t need to discount the prices when demand is high. Using facts, not guesses, to understand how your customers engage might mean you change your sales or marketing processes.Think of the many ways data analytics can highlight areas of opportunity for your business: One tangible result of a data analytics practice is likely well-planned reports that use data visualization to tell the story of the most salient points so that the rest of the business-who aren’t data experts-can understand, develop, and adapt their strategies. Many subject areas comprise data analytics, including data science, machine learning, and applied statistics. Being systematic is vital because data analytics uses many different activities and draws on all types and sizes of data sources. Like any true practice, data analytics is systematic, consisting of many computational and management steps. When done well, data analytics can help you:
Data analytics includes all the steps you take, both human- and machine-enabled, to discover, interpret, visualize, and tell the story of patterns in your data in order to drive business strategy and outcomes.Ī successful data analytics practice can-should-provide a better strategy for where your business can go. Instead, it’s what you do with that data that provides value. The primary goal is for data experts, including data scientists, engineers, and analysts, to make it easy for the rest of the business to access and understand these findings.ĭata that sits raw, as-is, has no value. To explain this confusion-and attempt to clear it up-we’ll look at both terms, examples, and tools.ĭata analytics is a broad term that defines the concept and practice (or, perhaps science and art) of all activities related to data.