Data scientists are a new type of analyst—part data engineer, part statistician, and part business analyst. And they're in high demand. Companies are combing through résumés and job websites, interviewing recent university grads, and poaching from their competitors in an effort to bring these new talents into their organizations. Of course, we’ve had statistical analysts in our organizations for years. Unfortunately, although these people are great at analyzing data, they are not always the best at explaining their findings to executives and business workers in understandable terms.
Sponsored By Teradata
Data scientists are in high demand and companies are frantically looking for ways of overcoming the costs and shortage of experienced data science talent. To help solve this problem, both established and new start-up vendors are introducing products that claim to enable business users to do advanced and predictive analytics without the assistance of a data scientist.
Sponsored By Tableau Software
Agile development methods—as applied in data disciplines—have experienced strong adoption by users in recent years, and for good reason. As more organizations “run the business” based purely on data (and compete and innovate), data management professionals are under increasing pressure to deploy data solutions into business use sooner, produce multiple solutions, and align data solutions with quickly evolving business goals. Hence, delivery speed, development productivity, and business alignment are the leading priorities (and benefits) for agile data management.
Sponsored By Dell Software
Changes in the way that today’s business transpires have slowly been cutting away at the ability to meet fully the needs of data consumers. At some point, those who manage the data warehouse will hit a threshold or boiling point that will make modernization mandatory.
Sponsored By Hewlett Packard
Many end-user organizations are currently commencing or expanding solutions for big data and big data analytics. These organizations want to understand how to approach big data and where they stand relative to other companies, especially their competitors. In late October 2013, TDWI launched its Big Data Maturity Model Assessment Tool, which can help to guide IT and business professionals on their big data journey. The assessment looks at companies across five dimensions that impact maturity, including organization, infrastructure, data management, analytics, and governance.
Fern Halper, Krish Krishnan
Sponsored By Cloudera, IBM, MarkLogic, Pentaho
Agility is a critical success factor for today’s enterprises. Agility is about being flexible and responsive to change, from the rapid shift in business conditions to new customer preferences. Business intelligence, analytics, and data warehousing projects must also show flexibility and deliver value sooner. In this Webinar, we highlight the upcoming TDWI World Conference and BI Executive Summit (August 18–23) in San Diego, and discuss recent agile-related research and best practices, as well as case studies and courses in analytics, BI essentials, and data analysis and design.
Sponsored By TDWI
Data visualization and visual data discovery can enable diverse types of users—from data scientists working with big data to nontechnical business managers and frontline users—to see significant trends and patterns in data that they would have struggled to see in voluminous tabular reports and spreadsheets. As big data volumes grow and organizations seek to integrate diverse and complex information, users’ ability to comprehend information quickly and put it to productive use hinges on data visualization.
Sponsored By Adaptive Planning, Advizor Solutions, Esri, Pentaho, SAS, Tableau Software
No profession is getting more attention these days than that of the “data scientist.” Data scientists have made the covers of business magazines and are practically rock stars at online companies such as Google, Facebook, and LinkedIn.
Sponsored By Teradata