| | | DATA TRENDS AND PREDICTIONS 2021: THE YEAR OF DATA FLUENCY | | The past year has been tumultuous, with many lessons still being revealed today. The Covid-19 crisis has accelerated digital transformation, forcing incumbent organizations to digitize their processes, modernize their business models, enable data access, and upskill their workforce for a data-driven... | | |
| | | DATA MANAGEMENT BEST PRACTICES FOR MACHINE LEARNING | | Machine learning is on the rise at businesses hungry for greater automation and intelligence. A recent study fielded amongst the subscribers of DBTA found that 48% currently have machine learning initiatives underway with another 20% considering adoption. At the same time, most projects are still in... | | |
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| | | CONFIGURING REAL-TIME AND BIG DATA WORKFLOWS USING ARCGIS | | From tracking units in the field to analyzing important feeds, ArcGIS GeoEvent Server keeps you up-to-date on what is happening, alerting you to the items that need your attention. Do you need to analyze large amounts of data? ArcGIS GeoAnalytics Desktop toolbox allows ArcGIS Pro users to quickly ge... | | |
| | | 5 PREDICTIONS FOR HEALTH AND PHARMA ANALYTICS IN 2021 | | For all the challenges presented by the events of 2020, it also forced many organizations to find ways to adapt and thrive. At every level, from patients to costs to quality to outcomes, analytics became increasingly important to realizing the value of digital transformation. | | |
| | | TOP TRENDS IN MODERN DATA ARCHITECTURE FOR 2021 | | From hybrid and multicloud, to real-time analytics and AI, a strong data architecture strategy is critical to supporting your organization's goals. Greater speed, flexibility and scalability are common wish-list items, alongside smarter data governance and security capabilities. Many new technol... | | |
| | | DATABASE MANAGEMENT IN THE CLOUD ERA | | Cloud adoption is accelerating, alongside automation, offering companies a path to increase their database capabilities in ways not possible before. Reducing infrastructure costs, improving systems agility and supporting new use cases are all key drivers. Moreover, since heterogeneity still rules th... | | |
| | | DATABASE MANAGEMENT IN THE CLOUD ERA | | Cloud adoption is accelerating, alongside automation, offering companies a path to increase their database capabilities in ways not possible before. Reducing infrastructure costs, improving systems agility and supporting new use cases are all key drivers. Moreover, since heterogeneity still rules th... | | |
| | | DATABASE MANAGEMENT TODAY: NEW STRATEGIES AND TECHNOLOGIES | | From machine learning and automation, to hybrid and multicloud environments, technology trends continue to reshape the practice of database management. As a result, database professionals face new challenges and opportunities. Today, the average database team is tasked with managing more databases, ... | | |
| | | UNLOCKING THE POWER OF DATAOPS | | DataOps is on the rise at enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, this new methodology is poised to revolutionize data analytics with its eye on the entire data lifecycle. Howev... | | |
| | | EXPANDING YOUR DATA SCIENCE AND MACHINE LEARNING CAPABILITIES | | Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting data sets and data platfor... | | |
| | | BRIDGING THE GAP BETWEEN DATA MANAGEMENT AND DEVOPS | | Even the best DevOps initiatives can fall victim to data bottlenecks, from challenges provisioning data quickly, to synchronizing application and database changes and, in many cases, a serious lack of cooperation between developers and database administrators. The speed and flexibility requirements ... | | |