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3 Strengths of Feature Flagging Tools With In-App SDKs


Split - 3StrengthsOfInAppSDKsForFeatureFlagging

While most feature flagging platforms and homegrown solutions are designed with cloud-based SDKs, some are built to operate locally inside your app, not in a third-party cloud. The latter are the ones you want to look for.

As odd as it sounds, this “local” variety of SDKs doesn’t have the answers to A/B test questions. Instead, they hold the rules, allowing your feature flags to react to users in real-time based upon what you assign. Collectively, in-app SDKs can jumpstart your feature experiments quickly, securely, and with accurate data. The inverse isn’t quite as intuitive. Traditional, cloud-based SDKs can’t make decisions on their own, and must therefore pass through a vendor cloud. This exposes your feature flags to a long journey. Not to mention a long list of issues from frustrating to detrimental.

Remember this: If you want to release smarter software features with less risk, look to feature management and experimentation platforms built upon an architecture of in-app SDKs. They are your foundation for powering smart and safe software delivery.

Major Advantages of Feature Flagging Tools Built Upon In-App SDKs

Here are three major advantages of feature flagging tools built upon in-app SDKs, AKA “rules engines.”:

1. Meatier Privacy Protection

In-app SDKs don’t share personal identifiable information (PII) with the cloud. Therefore, you can target your feature flags without putting user data at risk. Unlike other feature flagging solutions, in-app SDKs are rules engines. They’re not thin clients, which are mistakenly thought to be agile. Instead, they’re reinforced and intuitive by design. Most importantly, they’re able to pull the rules down directly from a feature management cloud, store them, and cache each one locally—right inside your app for privacy reasons.

Having the rules locally within applications brings authority to the SDKs. When the SDKs are called by your code, and that private data is passed through, the evaluation can be made locally. This keeps you from exposing your data to security breaches. In this case, a feature flag is only released to another page on your app or another page on your website. And only then do we make the calls, pass that private data, plus evaluate.

Being able to use that private data with less risk of a security breach gives software teams the ability to A/B test and experiment. As a result, they can push continuous feature releases without fear.

2. Faster, Real-Time Reactions

In-app SDKs react instantly to customer interactions right inside your application. Why is this beneficial? For those looking to innovate fast without losing customers, real-time metrics are everything.

When all evaluations performed by in-app SDKs are driven by local data (rather than a cloud evaluation), processing time is virtually instantaneous. Like under a few milliseconds! Plus, these on premise evaluations can be re-run and updated at any time. Ultimately, this enables the ability to trigger tests, to capture fast actionable insights, and to make smarter feature changes on the fly.

Evaluating contextually relevant data locally will only help you innovate quickly and without hesitation. Empowering your engineering and product teams with data-driven decision making like this, you’re putting software delivery into overdrive.

3. Cleaner Data With Clear Results

It’s important to ensure that you’re capturing high-quality data without misleading variables clouding your results. In-app SDKs improve the accuracy of A/B testing, experimentation, and even customer support systems.

How does this work?

Essentially, in-app SDKs know when a customer interacts with your feature-level A/B tests. Therefore, you can know exactly who’s viewed your experiment and who hasn’t. Let’s say you’re testing an experience at the bottom of your application page, and only a small percentage are actually interacting with that test. While cloud-located feature flags only separate control groups from those pre-determined to receive the experiment, in-app SDKs identify users that are actively completing experiments from start to finish.

Because in-app SDKs are rules engines reacting to user behavior in real time, flags will only be evaluated when people are fully exposed to an experiment. The system of record is therefore only updated to reflect results when the triggered event takes place. This leaves you with results as clear as day, not murky like mud.

The Right Features, the Right Users

As you advance software and release new features, you need a supportive foundation. Especially if the goal is to create experiences that are valuable to your customers and company. In-app SDKs give feature management and experimentation tools a reinforcing infrastructure, and this is why Split’s Feature Data Platform™ was architected with them. It’s also the reason why Split is the bedrock for over three billion unique users and devices.

Unlike homegrown and other feature flag solutions, Split delivers a robust, scalable, feature flag and experimentation platform. With Split, you can safely use private data for targeting, so that you can roll out the right features to the right users, at the right time. It also helps you deliver the data you need to drive the results you want. That’s always a plus!

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