Notes about Cutting-Edge Technologies and Everything

  Strategic Planning Assumptions Through 2022, manual data management tasks will be reduced by 45% through the addition of machine learning and automated service-level management. By 2023, AI-enabled automation in data management and integration will reduce the need for IT specialists by 20%.  Read report >>>

We know data preparation requires a ton of work and thought. In this provocative article, Hugo Bowne-Anderson provides a formal rationale for why that work matters, why data preparation is particularly important for reanalyzing data, and why you should stay focused on the question you hope to answer. Along the way, Hugo introduces how tools and automation can help augment analysts and better enable real-time models. In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. This is the garbage in, garbage out principle: flawed data going in leads to flawed results, algorithms, and business decisions. If a self-driving car’s decision-making algorithm is trained on data of traffic collected during the day, you wouldn’t put it on the roads at night. To take it a step further, if such an algorithm is trained in an environment with car...

Data produced by applications, devices, or humans must be processed before it is consumed. By definition, a data pipeline represents the flow of data between two or more systems. It is a set of instructions that determine how and when to move data between these systems. My last blog conveyed how connectivity is foundational to a data platform. In this blog, I will describe the different data processing pipelines that leverage different capabilities of the data platform, such as connectivity and data engines for processing. There are many data processing pipelines. One may: “Integrate” data from multiple sources Perform data quality checks or standardize data Apply data security-related transformations, which include masking, anonymizing, or encryption Match, merge, master, and do entity resolution Share data with partners and customers in the required format, such as HL7 Consumers or “targets” of data pipelines may include: Data warehouses like ...

Data preparation tools have matured from initially being self-service-focused to now supporting data integration, analytics and data science use cases in production. Data and analytics leaders must use this research to understand the dynamics of and popular vendors in this rapidly evolving market. Key Findings The market for data preparation tools has evolved from being able to support only self-service use cases. Modern data preparation tools now enable data and analytics teams to build agile datasets at an enterprise scale, for a range of distributed content authors. The market for data preparation tools remains crowded and complex. The choices range from stand-alone specialists to vendors that embed data preparation — as a key capability — into their broader analytics/BI, data science or data integration tools. While most data preparation tool capabilities have been maturing at a steady state, organizations continue to cite “operationalization” — the ability to promote ...