Is Your Data Strategy AI-Ready?
As companies now have lavish amounts of data at their fingertips and AI continues to offer new business opportunities, data strategy becomes a fundamental part of maximizing ROI from AI initiatives. How exactly is data captured? How is it processed? What is the end goal of collecting and processing? These are just a fraction of important questions AI developers need to answer to succeed in their AI technology implementation.
Choosing the Use Case
Given that AI offers a myriad of opportunities for driving business growth, companies may be hesitant about where to start. Most importantly, an AI use case should correlate with a specific business objective. Unsurprisingly, organizations often tend to dive into AI implementation haphazardly because of the hype surrounding this technology.
However, the application area largely defines the implementation effort needed for successful AI adoption. For example, deploying AI for product development enhancement usually calls for structural changes, revamping of business workflows, and extensive data preparation. At the same time, augmenting customer service with chatbots will require less hassle when preparing data for this initiative.
Building a Data-Driven Culture
Once you have defined the use case, developing a data-centric culture should become a priority. Far too often, organizations are getting heavily invested in all the technicalities of AI implementation, leaving their workforce disincentivized and underprepared for taking advantage of the new tools. For AI adoption to succeed, it’s crucial to ensure that workforce is ready both psychologically and technically.
This is usually done by introducing a succession of training sessions dedicated to data literacy. It’s important to stress how exactly new AI tools will enhance current workflows and help achieve better performance. Workforce training usually takes a considerable amount of time, especially when carried out in large enterprises. This is why it’s often crucial to start upskilling incentives as early as possible in the AI adoption cycle. By creating functional prototypes with all the essential features, you can start retraining way before the actual AI deployment.
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Addressing Data Quality
When it comes to assessing data readiness, data quality is the place to start. There are many attributes of data quality to consider, including accuracy, completeness, consistency, validity, integrity, and lack of bias.
Far too often, companies put an excessive amount of resources into data cleaning. While it’s certainly logical, determining which exact datasets need to be scrubbed clean is what really matters. Even as companies are faced with a continuous influx of data, most of the time it’s only a fraction of this data that is useful for a particular business case. This is why it’s important to figure out the appropriate strategy and determine a specific AI application before diving into data cleaning.
Ideally, companies need to establish practical data storage and transfer frameworks, meaning that only relevant data should be systematically collected and processed. This way, an AI strategy will become more economically efficient.
It’s also important to route all the incoming data into a single data management hub, be it cloud-based or locally stored. The idea is to make data easily accessible to all important actors, including business analysts, stakeholders, and clients. Moreover, by establishing solid data architecture, it becomes easier to adopt other AI use cases as your business grows.
Cloud or On-Premises?
Migrating to the cloud is one of the most critical steps on the path to AI adoption. The cloud allows organizations to scale, gain much-needed flexibility, and significantly decrease costs. While this step is rarely needed for short-term AI success, it’s one of the main enablers for going full-on in the long term.
Most importantly, AI requires to process huge amounts of data in real time to operate at scale. When data is stored in different organizational systems like corporate email, CRM, or invoices, it becomes hard for AI to process it. Cloud solves all these problem by making it possible to store and process data from a single place.
In most cases, the trick is to store data on multiple cloud platforms, rather than one. Cloud platforms can drastically differ from each other in terms of their functionality. By going multi-cloud from the inception, companies can save themselves from the trouble of relying on any single cloud vendor.
Ethical Data Usage
Responsible data aggregation and processing should be at the core of your data governance structure. As users are becoming increasingly concerned about their privacy, this requires organizations to become more transparent about data usage and reevaluate their ethical data use policies when it comes to AI operation.
Logically, the more data AI has access to, the better decisions it can make. This is why it’s often tempting for an organization to stockpile every bit of data available. However, such an approach poses significant discrimination risks that can affect long-term AI success.
For example, AI can consider a customer’s determined gender to decline a loan or a job application, increase the price, or make a totally irrelevant product offering. Moreover, when there are too many data points, it becomes harder for data scientists to explain AI decision-making rationale. Not only is this unethical but also legally risky, as common regulatory frameworks like the GDPR require companies to be able to clearly explain reasoning behind AI-made decisions.
Conclusion
A robust and scalable AI strategy always rests on a solid data foundation. Here are a few steps every company should take to make it happen:
- Define what datasets you need for your particular AI use case.
- Based on the business use case, decide what datasets need to be cleaned and establish a solid data governance structure.
- Turn to the cloud to support more scalability.
- Address data ethics issues and constantly monitor how exactly AI tools process their underlying data.
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