Implementation of A.I. in Your Work: An Overview
For a PDF version click here.
Economic Data Sciences (EDS) is focused on bringing the power of Artificial Intelligence (A.I.) to businesses in an understandable way. We believe that no matter how good any solution is, if the people using the tool do not understand the ‘why’, the ‘how,’ and the rationale behind it, then that tool is of little use to anyone. In this short note, we hope to give an intuitive overview of how the EDS toolset can be applied and the fundamental value-adds that it can offer to any data-driven solution.
The Tools – Enabling Technologies
When A.I. is discussed in media, it is often talked about in a singular form. Our experience shows that any successful enhancement to the business process will rely on many combined technologies working together. This makes sense, because A.I., as an enabling technology, is meant to enhance the business process from end-to-end and just like the business process, there are several parts that make the whole function seamlessly. In the three boxes below, we have descriptions of the three main pillars of any business process which are (i) to get measurements/information that will inform decisions, (ii) make a decision that is the best possible trade-off of the options available, and (iii) to monitor and analyze outcomes and potential future scenarios. We tie these parts of the business process to the enabling technology that is best suited to the specific process. In the EDS offering, we have made advances in each of these areas to bring the power of A.I. to real-world problems.
Let’s discuss each of these in a bit more detail and talk about what the tools actually do.
Measure
Before any informed decision can be made, information must be collected and measured. EDS uses machine learning to enable human teams to look broader and deeper during this step. We achieve this outcome through some simple and fundamental value-adds.
Automated and Informed Model Discovery: One of the most time-consuming parts of a measurement process is looking through all of the possible ways and combination of ways to make that measurement. In this step machine learning answers questions like, which factors should be included? Which models work best? How can I combine different models? Machine learning is essentially a workhorse in this area and can look through more potential models and combinations than would be possible for a human team alone in a timely fashion. This allows the human team to focus more on the insights.
Checking for Robustness and Consistency: Most businesses do not have strong feelings about which model is used, but focus on whether it is consistent and are its insights robust (i.e., meaningful in the real world). Again, EDS uses machine learning to automate what is otherwise a tedious and time-consuming process.
Decide
The core of any business is making the most informed decisions possible that fits the specific needs of the business. The decision-making process must evaluate trade-offs between the conflicting risks and opportunities available. In the earlier measurement step, EDS used machine learning to increase the amount of information to consider. So how do we now allow decision-makers to arrive at the optimal solution?
A Holistic Perspective: Risks and opportunities come in many different forms and through many different parts of the business process. The traditional way to address these issues is to set them in a sequence and then tackle them one at a time. The issue with that approach is that each of these decisions affects other parts of the process. In short, everything is interconnected. Probably the most important innovation that the EDS technology enables is the breaking of this traditional constraint so that any number of problems can be simultaneously addressed. As a result, the connections and implications of decisions can be more fully understood.
Making ‘apples-to-apples’ Comparisons with Trade-offs: In business as in life, everything is a trade-off. Building on the holistic perspective that EDS provides, as well as its understanding of the connections within the process. Using the framework of trade-offs is a powerful way to analyze and more importantly decide on the many different metrics and factors. Trade-offs can clearly account for how much must be given up in one area, to gain in another, and how that decision will affect all other areas, so that the best ‘price’ or trade-off can be found.
Monitor & Analyze
While these tools are enabling broader and deeper measurements, leading to more informed decision-making, it is important to take a step back and remember the reason we are doing any of this is so we can communicate the insights. EDS uses customized ‘Big and Fast’ data solutions that put insight first, where traditional approaches were designed around storage.
Connecting Data Sources with Customized Solutions: Connecting sources of data is not a new idea. In fact, in some cases, those that have received this advice from a consulting firm naturally follow up with the next question of ‘How can we implement this idea?’, only to be disappointed with no clear path forward. EDS has the expertise to customize data connections, on-site or in the cloud, to fit your business’s needs.
Communicating the Expanding Data Universe: In the sections above, we discuss measuring broader sets of data with deeper analytics and using this information for more informed decisions. EDS data solutions are geared towards communicating these insights and analytics to the people who are responsible for those decisions. We do this by building customized reporting, scenario simulation, and forecasting tools.
EDS - A Customized Combination of Transparent Tools
The EDS toolset is a completely transparent set of technology tools, using extensions on well-known research. We focus on customizing our A.I. powered solutions to individual needs and clearly communicating trade-offs and results. The fundamental value-adds that these technologies bring are (i) a more connected and coordinated data infrastructure, (ii) which allows for more information collection and measurement in a more timely fashion, (iii) and that in turn leads to more informed decision-making.
To see how A.I. can help in your business process, email questions to info@EconomicDataSciences.com
<1> For our ‘super analyst’ we assume the analyst produces a new model every 10 minutes, 8 hours per day. Other numbers are average estimates.