Pdf lesen Data-Driven Law: Data Analytics and the New Legal Services (Data Analytics Applications) (English Edition)
Beschreibung Data-Driven Law: Data Analytics and the New Legal Services (Data Analytics Applications) (English Edition)
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For increasingly data-savvy clients, lawyers can no longer give "it depends" answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions. Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as: Mining legal dataComputational lawUncovering bias through the use of Big DataQuantifying the quality of legal servicesData mining and decision-makingContract analytics and contract standardsIn addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable.Data-driven decision-making requires firms to think differently about their workflow. Most firms warehouse their files, never to be seen again after the matter closes. Running a data-driven firm requires lawyers and their teams to treat information about the work as part of the service, and to collect, standardize, and analyze matter data from cradle to grave. More than anything, using data in a law practice requires a different mindset about the value of this information. This book helps legal professionals to develop this data-driven mindset.
Data-Driven Law: Data Analytics and the New Legal Services (Data Analytics Applications) (English Edition) ebooks
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How Trump Consultants Exploited the Facebook Data of ~ Kogan built his own app and in June 2014 began harvesting data for Cambridge Analytica. The business covered the costs — more than $800,000 — and allowed him to keep a copy for his own .
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Big Data: 33 Brilliant And Free Data Sources Anyone Can Use ~ Many companies of various sizes believe they have to collect their own data to see benefits from big data analytics, but it’s simply not true. There are hundreds (if not thousands) of free data .
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DATA ANALYSIS, INTERPRETATION AND PRESENTATION ~ "Data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, time-consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Qualitative data analysis is a search for general statements about relationships among categories of data."
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Data Analytics Definition - investopedia ~ Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical .
Was ist Big Data / Lösungen & Software / SAP ~ Erfahren Sie hier alles über Big Data: Finden Sie 360-Grad-Lösungen für Ihr Unternehmen und entdecken Sie bislang verborgene Wachstumschancen.
Using big data to make better pricing decisions / McKinsey ~ Setting the best prices is not a data challenge (companies generally already sit on a treasure trove of data); it’s an analysis challenge. The best B2C companies know how to interpret and act on the wealth of data they have, but B2B companies tend to manage data rather than use it to drive decisions. Good analytics can help companies identify how factors that are often overlooked—such as .
Ten Ways Big Data Is Revolutionizing Marketing And Sales ~ Customer Analytics (48%), Operational Analytics (21%), Fraud and Compliance (12%) New Product & Service Innovation (10%) & Enterprise Data Warehouse Optimization (10%) are among the most popular .
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