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Use Cases

An article presenting use-cases of humans and machines collaborating.

The purpose of a platform is to store, process, integrate, manage and analyze several datasets which are relevant to decision-making. The objective is to create a system, known as the platform, that is useful for a larger number of stakeholders, including decision-makers.

The proliferation of platforms, commonly referred to as platformization, is a response to the big data era. When big data is stored in various silos, it becomes low value. But when it can be consolidated into one data-driven decision platform, it is constantly being updated, analyzed, and accessed by decision-makers. Platforms also facilitate communication and collaboration between several decision-makers both intra- and inter-organizationally, in a way that leads to better-quality decisions and opens doors for innovation.

Platforms enable cross-company decisions to be taken in a coordinated manner at the macro, meso, or micro-level. The data-driven decision platform will be the foundation upon which members of a particular ecosystem share data and insights towards better decision quality.

The DDDM platform

There is a growing interest in technological platforms. Accordingly, we posit that data-driven decision-making (DDDM) will be facilitated via a platform. Platforms have been used to support analytics.

The DDDM platform will leverage the potential of big data analytics (BDA) and incorporate it into the decision-making process. BDA is an approach to managing, processing, and analyzing big datasets by applying advanced analytics techniques. It allows for the creation of actionable insights which could be used for various purposes such as performance measurement, rendering competitive advantage, and supporting data-driven decision-making.

Accordingly, decision-making can be substantially improved through sophisticated analytics and extracting valuable insights, which would have otherwise remained hidden.

A DDDM platform should focus on all the elements of contemporary decision theory: decision-maker, decision, decision-making process, data, and analytics. In the following section, we will look at some of the state-of-the-art DDDM platforms.

Board: Mostly via cloud-based dashboards linked to various data sources e.g., databases, data warehouses, big data, IoT data, or flat files. The platform focuses on serving BI and data management.

GiniMachine: No-code platform which allows users to upload their data and run AI/ML algorithms on top to enable automated decision-making.

Intelligence2day: The promise here is to turn big data into actionable insights via the platform.

TDP: This is a clear example of a company offering DDD via a platform. TDP enables real-time data-driven action via a platform. The platform focuses mainly on the data and analytics.

Sword: Here the focus is on the data and analytics algorithms.

Fingertip: The company offers a decision-making lifecycle – where all phases are documented for each decision – and offers a solution aimed at facilitating what is known as a “social decision-making environment”. Their application integrates into existing CRM and makes decisions, and also allows learning from past decisions via the decision log, these are called historical decisions. The focus here, however, is on the process and its documentation rather than on the data and analytics.

Pyramid: The platform focuses on several aspects pertaining to the data warehouse, data pre-processing, business analytics, data science, and an AI self-service. The platform provides information that allows decision-makers to make faster decisions. It provides fast access to data and AI algorithms in a form of self-service.

We see clearly that the platforms focus on either data management or analytics while providing little insight into the interaction between the human decision-makers and the algorithms. Accordingly, we recommend to those organizations using the platforms to further investigate the challenges unfolding pertaining to human-machine collaboration, such as accountability, human-in-the-loop, explainability, and openness.

© Luleå University of Technology
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