10 steps to a data- and model-driven workflow


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To revolutionize your organization through its wealth of data, you will need a solid understanding of how to enable and operationalize a data- and model-driven workflow within your enterprise. The main goal of transforming your business to become a data- and model-driven enterprise is so that it can respond quickly and effectively to all the predictions that are coming from these very sophisticated learning models. To do this successfully, you will want to communicate effectively with both technical and non-technical teams, and also develop critical thinking about each of the workflow’s phases. 

When outcomes or predictions arrive on your desk, you need to know how to evaluate them: How to critique recommendations, ask the right questions, and ensure that the analyses and project are headed in the right direction. 

This workflow is based on the “ask, acquire, analyze, act” data science workflow, which breaks each phase down into smaller, more precise recommendations and actions.

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Ask phase one: Translate crucial business problems into data opportunities

To get the best out of your data, you need to know what questions to ask. These should be questions aligned with your business objectives that data can answer. 

In my data science for executives workshop, I ask executives to write down their top five business problems and think about how these can be solved by leveraging information that their organizations already have. Think about interesting case studies with extremely high visibility and high business impact. You’re looking for cases where you can move the needle and push your organization forward. You’re looking for cases that can revolutionize your business.

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Ask phase two: Prioritize the opportunity

Before investing time, energy and finances into a problem, make sure that you’re embarking on a journey with use cases that will move the needle. Ask yourself what the back of the envelope ROI calculation for this project is. 

For example, what will the potential impact of identifying more creditworthy applications be in the first, second, and third years? Identify the revenue uplift that might result. How revolutionary will this model’s business impact be? Think about the opportunity in terms of feasibility (I have the data, technology, right people) and impact (this project may move the needle and take my organization to the next level). 

Even back-of-the-envelope calculations can determine the potential ROI of machine-intelligence initiatives and help you determine where to place your resources.

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Acquire phase one: Acquire and prepare data for analysis and modeling

In this phase, you — or rather, your technical team — acquires and processes raw data. That data is then labeled, normalized and standardized as needed. The main element of this step is to explore the data you already have and shape it so that it can be put into your models. 

This is when you want to partner with different departments and verticals within your organization, gather the relevant data, make sure that it is in the right shape — for example, labeled correctly — and that you have the necessary documentation.

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Acquire phase two: Conduct an exploratory data analysis

The second step in the acquire phase is to instruct your technical team to conduct an exploratory data analysis (EDA). During this step, the technical team, with your help, works on validating your hypotheses and intuitions. It also discovers whether the data contains any anomalies or outliers. 

At this point, you will be better equipped to determine if your intuitive hypotheses are valid. As you proceed, you may often discover new trends and anomalies that go beyond what your intuition originally told you. If there are any data outliers that could skew the analysis, alterations must be made.

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Analyze phase one: Build relevant models

This is where the process becomes increasingly technical. As you begin to analyze your existing data, you will need to select and employ a range of models to determine which one is best able to solve your initial business problem and meet your goals. 

This is critical: There’s no single “winning horse” or single model that can explain everything. A few different models must always be chosen and tested. Three such models are random forest, logistic regression and neural networks. A full description of each one is beyond the scope of this article, but you can find more details on Google or in my book.

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Analyze phase two: Compare results

At this phase, your job is to compare the results yielded by different models by using a range of evaluation techniques. This process enables you to choose the model that best fits the data, and that demonstrates high performance with respect to the metrics you have determined. 

Sometimes, this phase can reveal interesting and unexpected results, because machine-intelligence models think differently than we do. A model can crunch millions of data points in an egalitarian way. There’s quite a lot to learn from the process of interpreting these results, and you, as a business leader, must invest time and energy doing so.

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Act phase one: Evaluate the output of machine-intelligence models

Data is an amazing tool but, like any other tool, it’s only as good as the hands that wield it. Therefore, the act phase of this process is the most important. The first step of this phase is to evaluate the results or output of your machine-intelligence models. 

In this step, your goal is to learn from the results to inform product offerings, strategies and to correct implementation. You interpret the results from the models, make sure they are aligned with your intuition and figure out what can you can do to solve the initial business problem.

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Act phase two: Data visualization

This step is critical to proper interpretation and evaluation. Data visualization is all about telling the story behind the data. Every data scientist can code, but not every data scientist knows how to communicate the results of these models efficiently, effectively and artistically. 

Even with smart, sophisticated models, business leaders will only be able to understand the results if they are presented in a way that resonates. If people in the organization don’t understand the story behind the data, the results won’t affect decision-making, which is the entire purpose of the data science workflow.

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Act phase three: Test the models and demonstrate ROI

At this point, you need to test your models and discover whether they work in the real world. If they do, your organization can start incorporating the model’s predictions into decision-making, which means that you’ll be able to measure the project’s ROI more accurately. 

At this point, you should also be able to see the increase in, say, how many more clients you have or how much more efficient your organization is. Once you take the crucial step of demonstrating ROI, I urge you to celebrate your success across the organization. Most businesses aren’t very good at this and need to get better!

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Act phase four: Operationalize machine intelligence to enhance decision-making

This final step in the ask, acquire, analyze, act model is the step that transforms your business into a data-driven organization. After you create a model, it will take time to operationalize it fully. However, don’t wait to begin reaping the benefits. 

For a while, you may need to combine manual work with dynamic input from your machine-intelligence models. It may make sense to go through machine-generated predictions and see how the results align with some of your older methods. Figure out both short- and long-term strategies for operationalizing the model. Don’t give up!

This article was adapted from the book, Data Science For Executives: Leveraging Machine Intelligence to Drive Business ROI, and was syndicated by MediaFeed.org.

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