Data-based decision making is great—if you’re using the right data. Don’t let organizational silos create misaligned data sets that skew your decisions. With BizOps, your teams can stop wasting time, focus, and resources on initiatives that aren’t aligned with strategies. Establish implementations that are aligned across these dimensions:
“There has long been evidence across many decision domains that data- and analytics-based decisions are more accurate than those made by human intuition.”
To keep in front of rapidly evolving technological environments, business requirements, and security threats, your organization needs to augment and automate decision making. Across your organization, teams need to leverage new, advanced solutions that combine advanced AI and machine learning algorithms. These capabilities grow increasingly vital as teams look to pursue their BizOps initiatives.
Toward that end, advanced AI and machine learning platforms should fully harness big data, integrating business, development, and operations data to generate actionable insights. This is an essential ingredient in helping your teams establish continuous improvement in the business outcomes of digital initiatives.
At a high level, it’s important to enable teams to access data from across a number of silos, while preserving the context of the source content and enabling this context to be shared efficiently. This contextual, comprehensive visibility is vital in delivering the actionable insights today’s decision makers, developers, and operations teams need. Here’s a brief overview of three key architectural requirements these AI platforms need to address.
Often, when it comes to deriving value from machine learning, it isn’t the algorithms themselves that matter. Typically, what matters is the way machine learning algorithms are orchestrated and scoped. To be able to use algorithms effectively, look for a platform that employs a multi-domain knowledge graph that describes the enterprise in great detail. Once this detailed information is available, the platform should decide what machine learning technique to apply, and which specific data set to apply it to. In addition, the platform needs to evolve dynamically as the organization and environment change.
In effect, we want to match the situational awareness of a real human analyst so the platform can pick and apply the right analysis technique to theright big data set, dynamically, based on what it discovers about the environment. This approach means that any specific analysis we apply can change and adapt to the enterprise as it evolves. This is very different than the rule-based expert systems of the past, which were simply too brittle for today’s dynamic enterprises.
In the market today, many topology approaches are closed in nature, bound by specific technological approaches and linear models. By contrast, advanced AI platforms should employ an open, source-agnostic approach. The platform’s architecture should deliver flexibility in several key ways:
In development, vendors can choose whether to create big data models that are very general or very specific. While very specific models may be pragmatic in the near term, they typically require manual modifications to accommodate new technologies, data types, and so on. Over time, these specific models tend to become brittle, leaving teams unable to react quickly to changes.
Instead, it is important to look for a platform that employs a property-graph based approach that has history awareness. With property graphs, complex relational lookups can be done instantaneously. As a result, they provide an excellent structure for doing ontological inference. By comparison, using a traditional relational database management system (RDBMS) for this model would require an impractical amount of join queries between schemas and tables, introducing an unacceptable level of performance-degrading latency.
In addition, here are a couple other characteristics to look for:
These insights are drawn from our blog post, Putting Machine Learning Algorithms to Work, which includes more information on the optimal architecture for maximizing the value of data science.