Developing Machine Learning Strategy for Business in 7 Steps

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If you’ve succumbed to the hype around machine learning, you’ve likely heard hundreds of ML evangelists claim that data-driven decision-making is inevitable for companies that want to thrive in the near future. And a number of questions will arise as you consider how to employ the technology in your business.. Can it significantly aid in reducing costs or increasing revenue? How can you estimate return on investment? Can you leverage the existing data to yield game-changing insights? Should you even try to get on that train right now?

What’s so special about machine learning

The way to address this is to apply an algorithm which would differ from the diligent but narrow “if-then” programs that we’re used to dealing with. Machine learning isn’t limited to narrow-task execution. An engineer doesn’t have to compose a set of rules for the program to follow. Instead, a machine can devise its own model of finding the patterns after being “fed” a set of training examples. Dealing with a “black box” of that sort–where a human is only concerned about inputs and outputs–brings almost unlimited variety of application opportunities, from recognizing cats in pictures to tracking body functions that yield individual treatment programs.

The reason machine learning is only now topping the list of tech buzzwords is that just recently we’ve achieved computational power enough to process big data: huge and unstructured data sets with possibly thousands of variables instead of small and well-filtered ones. Much talked-about AlphaGo, which has recently beaten a human grandmaster at the ancient game of Go, is just one of the examples.

Defining how machine learning is going to be the gamechanger for your business isn’t as trivial a task of simply putting the data into the black box and waiting for a magical insights sheet to roll into your printer tray. While you can utilize the approach to get insights about one or a handful of operations in a company, tangible changes happen only if the adoption is backed by a strategy. The strategy should be introduced and guided at the C-suite level, and a number talent acquisitions should be made to support this strategy adoption.

Step 1. Articulate the problem

For instance, if you want to reduce the churn rate, data might help you detect users with a high “fly risk” by analyzing their activities on a website, an SaaS application, or even social media. Although you can rely on conventional metrics and make assumptions, the algorithm may unravel hidden dependencies between the data in users’ profiles and the likelihood to leave.

Here’s another example. While it’s relatively easy to estimate performance scores in a sphere of production, can you understand, for instance, how salespeople perform? Technically, they send emails, set calls, and participate in conferences, all of which somehow result in revenue or the lack thereof. is a startup that tries to address the problem by making a machine learning algorithm to track all the sales data, including emails, calls, and conferences, to come up with the most productive sales scenarios.

The bottom line here is to define the problem where standard business logic and the set of rules aren’t sufficient to solve it. Use machine learning when decisions heavily rely on a subjective opinion of an analyst or a decision maker.

Applied predictive analytics is a broader variety of techniques that anticipate outcomes by leveraging data. While machine learning is one approach to realize predictive analysis, the current landscape of areas where it acts as a strategical reinforcement to business processes is quite broad, from content recommendations to healthcare.

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Step 2. Consider the prescription

Moreover, insights that you will get may inspire the prescription measures that you could never think before unraveling hidden dependencies in your data.

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Step 3. Ensure that the quality of your data is good enough

Qualify your data and decide the minimum prediction accuracy

In one of our projects involving fare prediction analysis in booking air tickets, we were challenged to design an algorithm which would forecast flight fares, both short and long term forecasts. Seventy-five percent of prediction accuracy was high enough to support customers with booking recommendations.

Be ready to break down silos, anonymize, and share data

We usually offer to provide a subset instead of the whole database and anonymize it beforehand. Even for the companies having a data scientist on board, it’s a common management challenge to share data among different departments. An overregulated information policy or just hoarding of data across departments can really slow down the process. That’s why data science adoption should be introduced and guided on the higher management level.

Good news: Data can be fixed

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Source: O’Reilly, The Evolution of Analytics

The bad news here is that a data scientist may require quite a while to complete data cleansing and proceed to the modeling stage. Should you try handling it yourself in advance without having proper expertise? The general answer is no. It’s very likely that your data set will need refactoring anyway.

Step 4. Prepare to bridge the gap between technical and business vision

According to a recent SAS paper, many organizations have already recognized the need to introduce a chief analytics officer to their corporate frameworks. The person should have both business and tech expertise to lead the data science initiatives, envision the options to scale the machine learning application and reconcile business and technical vision.

Otherwise, your data scientists should be ready to educate decision makers on the opportunities and limitations that different ML models present.

Step 5. Explore the options to hire the right talent

What makes data scientists so scarce and valuable is the blistering change in the technological landscape that outstrips educational capacities. Moreover, being a data scientist requires a rare skillset combination at the junction of math, statistics, programming, databases, and domain expertise.

So, here is the challenge. What are the options?

Hire a data scientist and be ready to engage

Homegrown specialists

Find a vendor team

Build relationships with educational institutions

Step 6. Models become dated, be ready to iterate

Challenger testing. When the existing model is assumed to become less accurate, a new challenger model is introduced and tested against the deployed model. The old model is removed once the new one outperforms it. Then the process is repeated.

Online updates. The parameters of a model are changed under the continuous flow of new data.

So, if you want to retain your predictive analytics on the same level of accuracy, having occasional or short-term data science services is not an option.

Step 7. Decide whether you need a custom-built algorithm

Salesforce, for instance, is offering artificial intelligence instruments that can communicate with their existing cloud solutions. The previously mentioned service along with Azure Machine Learning, Google Prediction API, and IBM Watson Analytics can be integrated with the most popular CRMs like Salesforce, Hubspot, Zoho, and some others. Guesswork offers ecommerce companies better understanding of customers by analyzing various collected data and providing tailored experiences. It integrates with ecommerce sites and can predict which visitors are more prone to conversion or it lets you tailor a newsletter to each customer. Ultimately, you can apply to Algorithmia, a marketplace of pre-built algorithms that communicate with software through REST APIs.

Is it the right time to adopt machine learning?

In a course of a few years, it’s likely that having a data science department will be the definitive point of competition in a wide range of business verticals, as CRM systems became years ago.

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You can find the original at AltexSoft’s blog: “Developing machine learning strategy for business in 7 steps”.

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Being a Technology & Solution Consulting company, AltexSoft co-builds technology products to help companies accelerate growth.

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