If you follow technology news, you know that virtually every article that addresses artificial intelligence (AI) and advanced analytics follows a similar formula:
- AI is transforming the world
- All of your competitors have already started using AI
- Here’s how to build your data science team asap
Yes, AI and advanced analytics are truly transformative technologies, but they are not magic pills your company can swallow to instantly become the next Amazon, Google, Facebook, or leading tech company.
So what can your company do to make sure that it’s ready to successfully adopt this technology?
1. Prepare for the right kind of change
In an effort to avoid missing out on the latest trends, many companies take the “shoot first and ask questions later” approach. AI and data science are no exception. As a result, companies immediately build a dedicated data science team with the expectation that their newfound geniuses will be able to figure everything out on their own. An authority no less than the head of Google’s AI business has expressed concern over this approach. In his view, AI is not “magic dust” for your company.
For sure, enlisting experts who can interpret data and apply statistical techniques is a requirement to proceed with these technologies. Instead of hiring them to be independent units, they should be integrated with your functional teams.
AI and advanced data analytics can make the biggest impact when incorporated with other business improvements, such as changes to business process. But this requires change and buy-in from subject matter experts within your company, something not accomplished by a team a data experts working on their own.
2. Get the basics right
We must learn how to walk before we can run, right? The same holds true with advanced data technologies – make sure you’ve covered the basics. This includes creating:
- a formal data strategy that incorporates data security, privacy, governance, and quality
- appropriate data architecture to ensure timely access to data from all sources
- communication channels to ensure insights can be shared easily and in a timely manner
Like a person trying to unlearn a bad habit, your company may need to wean itself off of certain tools and data habits. In particular, companies with a broad array of self-serve business intelligence products may have picked up some bad habits when it comes to interpreting data, including:
- reliance on superficial analysis (i.e. not going deep enough when studying an issue)
- sharing insights too early with the wrong audience
- ignoring subjective biases that might creep into analyses
3. Define clear goals and outcomes
Perhaps the hardest part with new technologies is in figuring out what we can do with them in the first place. This is especially true when we lack expertise. Either we go too far and imagine scenarios that are completely unrealistic, or we get stuck in a rut and limit ourselves to a slightly better version of what we already do. Neither scenario is healthy for your company.
But if you’re not already an expert, how do you know what you can realistically do in the first place? The answer shouldn’t be too surprising: look to peers both inside and outside of your industry for examples, talk with experts you trust, brainstorm with your experts (both C-suite and working-level).
Once you’ve identified realistic use cases, build some clear goals for your data projects. Make sure these goals have real measurable business outcomes, and that they are tied to improvements in existing business processes.
Yes, you should dive into AI and analytics
AI and advanced analytics will transform all industries in the coming years. In some cases, the changes they will bring will be incredibly disruptive. Other industries will emerge as better, more optimized versions of themselves.
To avoid the disappointment of heightened expectation around these new technologies, it’s important to properly position your company for the coming change. Make sure that you have a solid data strategy, and always, always tie data projects with business outcomes. Once you have mastered these challenges you will be ready for the transformation that AI will bring.
Part engineer, part marketer and all data – Matthew Gierc is the Director of Marketing and Business Development for 3AG Systems, a data analytics firm that specializes in helping companies make better decisions.
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