Artificial Intelligence (AI) has captivated the attention of the public, media, and business world.
The early, experimental phase of AI adoption, fueled by LLM and NLP capabilities and spearheaded by household names like ChatGPT, is already showing signs of another tectonic shift—ushering in a new era of highly-focused, outcome-first AI deployments and a new agentic AI ecosystem.
But the question everyone is asking is pretty much the same: “How fast can I start using AI? And how can it help grow my business?”
The answer to these questions all depend on one thing – and it is not an advanced Data Science skillset (although such a skillset will be instrumental down the line). The answer to how you can use AI to differentiate your organization is already there, in your data.
AI uses mathematical and statistical principles to try and understand and make decisions based on the data you feed it. This is where the expression “garbage in, garbage out” comes into play. If you feed your AI bad data, the results may not be useful. Or worse, they may be entirely hallucinated.
If the results can’t be reliably used to drive business decisions, no amount of AI sorcery is going to rectify that fact—to say nothing of the money, resources, and time lost developing it.
Garbage In, Garbage Out: What Does Bad Data Look Like?
It sounds easy in principle: put in good data and get out good results, right? But achieving a modern, reliable, and robust data source is challenging in an organization.
Historically companies have worked in silos and have implemented systems and processes that work well for each department, but not necessarily the entire company. This has left data fragmented, separated, incomplete, contradictory, and different across departments across the company.
This disconnection makes it difficult to build AI programs that understand the company as a whole and are able to be used for data decision making across marketing, finance, operations, customer experience, and so on. It does not matter what advanced statistical analysis you use in the model—it cannot overcome the shortcomings of your data.
To create innovative tools and experiences with AI organizations need to undergo a Data Innovation Journey
first.
Data Innovation Journeys![™]()
Start with Hakkōda
At Hakkoda, we believe that data maturity can be assessed and categorized across four discrete stages: from Chaos, to Order, to Insight, to Innovation.
We work with clients to understand where their organizations stand in the modern data landscape, then help them modernize their data stack, architecture, and processes to achieve what comes next.
The output is a pragmatic roadmap to plan your evolution toward your desired outcomes. Pragmatic means that you do not have to do it all. Just the right things, in the right order to meet your outcomes.
As you might imagine, it is difficult to drive meaningful insight or true innovation if your current data state is chaos. Organizations in chaos suffer from fragmented, error-prone data, and are often the first victims of “garbage in, garbage out” as a principle.
The Hakkoda team is highly experienced in guiding organizations out of chaos and into more advanced stages of data maturity. We combine vetted industry and modern data stack expertise to work with internal teams like yours in complex and closely regulated fields. We also know how to listen for what your business really wants to accomplish, and we bring the technical chops to deliver on that vision.
This journey will empower you to leverage and maximize the value of emerging technologies like Generative AI.
Ready to begin your own journey toward better data quality and practical, high-impact AI use cases? Find out more about where your business falls along the continuum of data maturity by using our Data Innovation Journey calculator or talk to one of our experts today.
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