Top 10 Big Data Trends For 2017
- The Proliferation of Big Data
Proliferation of big data has made it crucial to analyze data quickly to gain valuable insight. Organizations must turn the terabytes of big data that is not being used, classified as dark data, into useable data.
Big data has not yet yielded the substantial results that organizations require to develop new insights for new, innovative offerings to derive a competitive advantage
- The Use of Big Data to Improve CX
Using big data to improve CX by moving from legacy to vendor systems, during M&A, and with core system upgrades.
Analyzing data with self-service flexibility to quickly harness insights about leading trends, along with competitive insight into new customer acquisition growth opportunities. Using big data to better understand customers in order to improve top line revenue through cross-sell/upsell or remove risk of lost revenue by reducing churn.
- Wider Adoption of Hadoop
More and more organizations will be adopting Hadoop and other big data stores, in turn, vendors will rapidly introduce new, innovative Hadoop solutions. With Hadoop in place, organizations will be able to crunch large amounts of data using advanced analytics to find nuggets of valuable information for making profitable decisions.
- Hello to Predictive Analytics
Precisely predict future behaviors and events to improve profitability. Make a leap in improving fraud detection rapidly to minimize revenue risk exposure and improve operational excellence.
- More Focus on Cloud-Based Data Analytics
Moving data analytics to the cloud accelerates adoption of the latest capabilities to turn data into action. Cut costs in ongoing maintenance and operations by moving data analytics to the cloud.
- The Move toward Informatics and the Ability to Identify the Value of Data
Use informatics to help integrate the collection, analysis and visualization of complex data to derive revenue and efficiency value from that data. Tap an underused resource – data – to increase business performance
- Achieving Maximum Business Intelligence with Data Virtualization
Data virtualization unlocks what is hidden within large data sets. Graphic data virtualization allows organizations to retrieve and manipulate data on the fly regardless of how the data is formatted or where it is located.
- Convergence of IoT, the Cloud, Big Data, and Cybersecurity
The convergence of data management technologies such as data quality, data preparation, data analytics, data integration and more.As we continue to become more reliant on smart devices, inter-connectivity and machine learning will become even more important to protect these assets from cyber security threats.
- Improving Digital Channel Optimization and the Omnichannel Experience
Delivering the balance of traditional channels with digital channels to connect with the customer in their preferred channel. Continuously looking for innovative ways to enhance CX across channels to achieve a competitive advantage.
- Self-Service Data Preparation and Analytics to Improve Efficiency
Self-service data preparation tools boost time to value enabling organizations to prepare data regardless of the type of data, whether structured, semi-structured or unstructured. Decreased reliance on development teams to massage the data by introducing more self-service capabilities to give power to the user and, in turn, improve operational efficiency. Source
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