Collaborative analytics model boosts decision-making
The rise in remote work and efforts to democratize data science are driving organizations to consider a collaborative analytics model that improves cooperation during the decision-making process.
It is still early as organizations work through some of the data sharing, security and workflow challenges of analytics collaboration. However, popular analytics tools are adding support for better collaboration features, and enterprises are beginning to connect the dots between data sources, analytics tools and collaboration platforms.
“To be really data-driven, an organization must tap into the knowledge, perspectives and goals of all their employees, not just those with data modeling or technological skills,” said Eric Madariaga, CMO at CData Software.
Organizations can only gain actionable insights into their business by leveraging departmental stakeholders’ expertise on their data.
“Firms that are reaping the greatest benefits from data-driven decision-making have deployed collaborative analytics and cultivated an open data-driven culture,” Madariaga said.
A collaborative analytics model changes the way organizations approach their BI initiatives by opening data-driven analysis and decision-making to more members before the process concludes. This allows organizations to do more than simply react to what’s already happened. “The traditional approach to business intelligence has too often reported on what happened rather than what’s happening or likely to happen,” said Satya Sachdeva, vice president of insights and data at Sogeti, part of Capgemini. This can enable organizations to peek into the future with predictive and prescriptive analytics.
Predictive and prescriptive analytics are advanced practices that weave together data from across the enterprise. Collaborative analytics can help organizations optimize business functions, discover new business models and create new products and services to monetize data. Predictive analytics attempts to predict what happened based on models trained on past data. Prescriptive analytics goes a step further to recommend actions. This can be as simple as a product recommendation engine or as complex as a better supply chain plan considering new circumstances.
A vital aspect of both disciplines is taking advantage of more contextual data than required with traditional BI. Various types of subject matter experts can provide insight into the appropriate context for various aspects of the model.
For example, a better recommendation engine might involve collaboration between product marketing experts, customer service reps, data scientists and UI experts. Collaborative analytics tools can help streamline communication across different experts so each person can focus on the relevant detail without getting bogged down in file formats, data schemas and other details. “Analytics collaboration is the ability for organizations and business users to collaborate on discovering insights and taking actions to create better business outcomes,” Sachdeva said.
One of the most essential practices for collaborative analytics is creating a business intelligence competency center. This can facilitate collaboration among IT, organizational change management and business functions to evangelize analytics, foster innovation, monitor results and continuously enhance analytical capabilities, Sachdeva said.
Some examples of analytics collaboration include the following: Collaboration between IT and business users to discover new data sets, use cases and define requirements. Visual collaboration to model data and insights among various business functions — such as product design, sales, marketing and service — to find avenues to monetize data for new products and services. Collaborative workflows among business users to save and reuse data sets and insights. Chats and comments among different user groups to make comments, ask questions and annotate various aspects of analytics.
