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The Analytics2Go Blog

Why Micro-Apps Are Essential to Business Decision Making

by Rafael Fanchini, on Mar 18, 2018 8:38:42 PM

Advanced Analytics comprise a combination of mathematics, statistics, and computation to produce machine-learning algorithms that can help any organization process and consider all relevant data in order to optimize decision making. See this blog post,  Machine Learning and Business are Made for Each Other, for further information.

Machine learning algorithms provide computers with the ability to identify hidden and often very complex performance-driver relationships within historical data. In turn, these analytic insights can be used to make predictions in the present by considering current performance-driver values.  

This technology is a key element of artificial intelligence (AI), and its use outperforms human intelligence very significantly on complex use cases with large data sets, complex data forms, or very high-volume scenario comparisons. Machine Learning is the key capability that has allowed companies like Google, Facebook, Amazon, and Uber to transform the world.

As the technology evolves, it allows new forms of applications to be developed. Augmented Analytics is the latest, most powerful and user-friendly form of data analytics now available to businesses of all sizes. Augmented Analytics provides optimal recommendations to decision makers at the time a recurring decision is made. Today, most organizations, large or small, still rely on the intuition of skilled and experienced individuals to make complicated decisions, most often with far too little analytic support to guide them. Researchers at Google have proven that an analytically-enabled decision process will outperform what they call the “ad hoc” process that it replaces by at least 10%. The analytic performance difference is often an advantage of nearly 20% in terms of higher revenue, increased profits, and better quality, etc. 

Since Machine Learning is a complex discipline to adopt, it is not surprising that the early adopters have been primarily technology-focused companies with deep pockets and distinguished science teams. In general, investments made by these companies have paid off in the long run, but only after efforts have been made to translate insights into actions. Early adopters have often been frustrated to find that valuable data science insights do not automatically or even easily translate into meaningful business results 

There are a couple reasons for this. The underlying data science is complex and hard to execute. Very often, data science practitioners are specialists who do not have a complete understanding of how organizations behave. The typical solution has been for domain experts to team up with data scientists in order to overcome this problem. This approach has only partially resolved the problem though, since the business domain experts are not always well-informed about the full set of recurring decisions that are made.

Think of it as plumbing. Even if you live nearby an adequate water supply, you do not automatically have fresh water delivered to your faucet. You need plumbers to provide pipes to transport water from the source to your glass. The so-called “last mile” of analytics is the same. Delivering great insights to decision makers at the time they need them is essential for the promise of analytics to be realized.

Seen from this perspective, it is not surprising that the early adopters have not been entirely satisfied with the positive impact they have received from analytics. In fact, organizations of every size face the same challenge. They wonder, “How can I use analytics to optimize the important decisions that I make every day?" We believe that analytic micro-apps are an essential and significant part of closing the remaining gap. 

Micro-apps are at the opposite end of the spectrum, when it comes to analytic maturity and complexity.  Data scientists usually try to find the key driver-relationship that describes a phenomenon in a broad domain area, such as pricing, inventory, or quality. The applications they develop are usually very sophisticated and often require a complex architecture for production. Although they are efficient and effective, it required a lot of energy and resources for them to be developed, deployed, and utliized to enable decision makers to access analytic insights in a form that they can use.

Micro apps are designed to be “bite-sized” and focus on an easy-to-use tool for individual decisions. For example, we have prepared a 30-minute demand prediction app for a quick-serve restaurant (QSR) chain.  This app is used to optimize when to start cooking or preparing food. This is an important factor for an individual QSR location that needs to harvest all the demand that comes in the door. If customers see a long or slow-moving line, potential orders are lost. Likewise, food that is made too early may “time out” before it can be served. The results from this simple app are impressive. QSR customers report that their revenue increases by 2% to 6%, when they use the app to know when to start the cooking process.

Traditionally, data scientists have offered end-to-end analytic solutions that often end up being complex or cumbersome to use. Micro apps are focused on a single important recurring decision. The scope is clear.  The implementation is far easier, and the output of the app can be used directly by the decision maker. The insight-to-results gap is eliminated using micro-apps.

Each micro app is designed to deliver optimal, best-practice decision recommendations. Since the scope of each decision is limited, it is far easier to design the micro app so that it runs efficiently in real time. Likewise, adoption is easier, because the micro app fits into the formal or informal workflow of the decision maker, and its intent and alignment with current process make it easy to understand. 

Our analytic platform is designed to optimize the design and use of micro apps. We source and maintain fully validated external data models for the businesses we support. For example, we offer about a dozen micro apps for the QSR industry. Our external data model includes all the data we recommend be used to operate any or all of the micro-apps.  We also specify internal data models that we recommend our customers collect and store on our platform.  

The final step is automation. For the businesses that we support, individual customers can begin using any one of the available micro apps based on their individual priorities and preferences. Provided that they load and maintain their entire internal data model, they can very easily expand their use to include any or all of the remaining micro apps.

Topics:Data AnalyticsAugmented AnalyticsMicro apps

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