The work environment today demands that your organization advances the efficiency to execute business processes for continuous operations to have a positive impact on business performance. The capability to be responsive to any range of minor to disruptive business events is required to support business continuity and level of organizational readiness to meet the needs of digital business. Ventana Research asserts that in 2025, one-quarter of organizations will remain digitally ineffective in achieving the business priorities for customer-, product- and people-related processes. It is essential to eliminate bottlenecks and become an organization that places action and decision-making at is center to optimize the execution of business processes.
Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Cloud Computing, Contact Center, Data, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, Digital transformation, natural language processing, AI and Machine Learning, continuous supply chain, agent management, Digital Business, Experience Management, Field Service, Process Mining, Streaming Analytics
When artificial intelligence emerged from the labs and vendors started offering it as a component of their software, many contact-center buyers shied away from it. From their point of view, AI and machine learning tools were new, expensive, relatively untested and had an uncertain use case. This stance was understandable, as contact center professionals are traditionally expected to be risk-averse when deploying technology into their operations. Contact centers are, by design, supposed to be hardened, mission-critical sites of high reliability. There has historically been a bias towards avoiding new technology, deploying only when it has been thoroughly vetted across the industry.
Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of ML analyses though. Our research shows that external data sources are also a routine part of data preparation processes, with 80% of organizations incorporating one or more external data sources. And a similar proportion of participants in our research (84%) include external data in their data lakes.
Alteryx is a data analytics software company that offers data preparation and analytics tools to simplify and automate data wrangling, data cleaning and modeling processes, enabling line-of-business personnel to quickly access, manipulate, analyze and output data. The platform features tools to run a variety of analytic functions such as diagnostic, predictive, prescriptive and geospatial analytics in a unified platform, and can connect to various data warehouses, cloud applications, spreadsheets and other sources.
Collibra is a data governance software company that offers tools for metadata management and data cataloging. The software enables organizations to find data quickly, identify its source and assure its integrity. Line-of-business workers can use it to create, review and update the organization's policies on different data assets. Collibra’s software uses a microservice architecture and open application programming interfaces to connect to various data ecosystems. Its data intelligence cloud platform can automatically classify data from various sources such as online transaction processing databases, master repositories and Excel files without moving the data, so the information assets stay protected.