Published on July 4, 2024, 6:27 am

When executive leaders express their desire to become a data-driven organization, a key objective is to empower business decision-makers to utilize data, predictive models, generative AI capabilities, and data visualizations to enhance decision-making processes. The aim is to achieve smarter decisions that yield positive business outcomes, quicker decision-making to respond to opportunities promptly, safer decisions that mitigate risks, and change management disciplines that increase the number of employees using analytical tools across the organization. Additionally, leaders seek scalable solutions that leverage cutting-edge machine learning models, AI capabilities, and new data assets while ensuring data compliance, protection, and security.

“To compete better, you must innovate better than your competitors, which relies on making quick and effective decisions,” states Wayne Jackson, CEO of Sonatype. “Leaders need a comprehensive view to make informed decisions, and attaining that level of visibility requires comprehensive data. However, data alone will not enhance or expedite the process; one must be able to make sense of that data.”

Although many organizations have invested in data architectures, deployed analytics tools, built machine learning models, and implemented data visualization capabilities, end-user adoption may lag behind and business impacts might be underwhelming. For instance,’ The State of Data Science and Machine Learning report indicates that 45% of organizations deploy less than 25% of their machine learning models into production.

This article explores seven steps aimed at addressing the gap between mere analytics deployment and adopting analytics for end-user decision-making. The first four steps focus on how individual teams, departments, and companies can enhance their analytical development process while the last three steps pertain to expanding these practices within larger enterprises.

It is crucial to undertake preliminary work in discovering a new dataset or analytical domain. However it’s easy to go overboard with these efforts by deploying proofs-of-concept into production without essential steps such as defining end-users’ needs reviewing their workflows & discussing the decisions requiring analysis.

“Additionally examining several questions can help users understand how analyses fit into workflows comprehend necessary integrations& identify areas suitable for automation Further improving datasets quality is an iterative process but not addressing it early enough in development may erode users’ trust leading them back to traditional work methods”, explains Irfan Khan

Apart from focusing on data quality teams should concentrate on two other time-related analytic metrics – Time-to-Data taking into account delays in receiving & processing information & Time-to-Decision considering human factors ease of use integration & level of automation from when info is available till final decisions are made

In conclusion each step discussed earlier can significantly enhance analytical implementations & decision-making processes for individual use cases Expanding analytical decision-making across multiple companies departments or domains necessitates evolving an operational analytic model implementing policies& establishing data governance practices{


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