Marketing analytics skills are among the most important capabilities in the marketing organization, yet it remains difficult to recruit, hire and retain people with strong skills to support in-house teams. Most marketing teams still struggle with a skills gap in this domain. As a result, marketers seek to augment internal teams by using advanced analytics service providers that offer third-party expert resources, proprietary methodologies and models, and even managed technology to help marketers tackle some of their toughest challenges. Vendors in this market specialize in advanced analytics, including sophisticated methods such as mapping the customer journey, attributing marketing spend to measured outcomes, simulating and measuring business impact of marketing and advertising campaigns, and implementing predictive models. Engagements may be project-based or part of an ongoing partnership, and may include the use of proprietary technology.
Analytics and business intelligence platforms — enabled by IT and augmented by AI — empower users to model, analyze and share data. Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and nonmodeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling that support the independent use by, and collaboration between, data scientists and their business and IT counterparts through all stages of the data science life cycle. These stages include business understanding, data access and preparation, experimentation and model creation, and sharing of insights. They also support machine learning engineering workflows including creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances and/or as a fully managed cloud offering. Data science and machine learning (DSML) platforms are designed to allow a broad range of users to develop and apply a comprehensive set of predictive and prescriptive analytical techniques. Leveraging data from distributed sources, cutting-edge user experience, and native machine learning and generative AI (GenAI) capabilities, these platforms help to augment and automate decision making across an enterprise. They provide a range of proprietary and open-source tools to enable data scientists and domain experts to find patterns in data that can be used to forecast financial metrics, understand customer behavior, predict supply and demand, and many other use cases. Models can be built on all types of data, including tabular, images, video and text for applications that require computer vision or natural language processing.
Reviews for 'Data and Analytics - Others'
Gartner defines network design as the optimization of the location and function of supply, manufacturing and distribution networks in support of an overarching company strategy and customer requirements. Supply chain network design tools support the creation of network models with the application of analytics to determine the optimal supply chain design in a structured, scalable and repeatable way. Supply chain network design tools support the determination of recommendations about the structure of the supply chain network. This includes decisions about facility locations and size, transport lanes, and modes in the end-to-end supply chain. The scale of the changes being evaluated vary from small modifications, such as changing a mode of transport or swapping transportation lanes, to large-scale changes that involve opening/closing/repurposing several facilities in the network and the associated knock on impacts. The use of supply chain network design tools to support the decision-making process enables companies to review more potential configurations for the network than a manual process allows, supporting a complete, data-driven decision making process. These decisions are usually made in strategic and tactical time frames.