At Arinti, we have our own proven methodology to kick-start AI and Data Science projects in your organization. Our goal is to empower organizations through artificial intelligence and lead them into a brighter and more profitable future. And one of the best ways to do that is to begin your own AI journey in your business. Starting off with an AI data audit.
The Arinti philosophy
A pragmatic, benefit-driven detection of AI-powered business cases. That’s what we believe in. And our data audit does just that.
When identifying AI projects in your company , it’s vital to shift your focus first on small, short-term, and low-risk projects that are close to your company’s core business. Here’s why you should do that:
- You learn with low risk.
- You identify data management, storage, and quality issues.
- You build goodwill within the organization.
- You create potential spillovers for future projects.
However, keep in mind that your data audit should never cover all available data in your organization. Nowadays, massive amounts of data are produced and gathered every minute. Going through all that data at hand would take too much time and effort, and the cons would greatly outweigh the pros. So, only target the data that is connected to your identified business cases. Nothing more, nothing less. For now.
The Arinti methodology
Our methodology for a data audit consists of four steps resulting in three key deliverables. Each step has its own way of working, deliverables and end goals. Starting with the kick-off phase, moving into the next step where you’ll identify AI-powered business cases, then digging deeper into Data Inspection and wrapping up with a detailed roadmap for the future.
The first phase in our methodology is the kick-off session. It marks the start of our data audit.
During this semi-structured, recorded interview with your project owner and/or data owner, we identify your mid-range – let’s say, three to five years – strategic goals.
In addition, we brainstorm about how AI-enabled technologies can potentially help you reach those goals in the best and most cost-efficient way.
At the end of the kick-off session, we’re able to answer three main questions:
- How can AI support you in reaching your business goals?
- What is your current situation concerning data capture, data management, data storage and data usage?
- How do you want to build your future data culture? Fully external-expert-based, co-created with your own team and one or more key suppliers, or self-sufficient?
Next up in our data audit is identifying AI-powered business cases, keeping in mind the insights we gathered during our kick-off session.
Like we mentioned before, the key here is low-risk and high-value. In an analytical report, we work out several AI-powered business cases. Per business case, you’ll receive information going from the expected high-level outcomes to the technical implementation. Everything in this report is based on the information we gathered in the first session, and on iterative, continuous feedback.
In this phase, we:
- Identify key AI-based business cases.
- Map business cases into a high-value, low-cost quadrant to spot the low-hanging fruit.
- Define how users (both internally and externally) will consume the AI-based outcomes.
- Find critical prerequisites in terms of data and IT architecture.
In phase number three, the Data Inspection, we begin to work on case-level. We assess the required data sources, data storage, data management and data integrations for each identified business case.
The result is a high-level report that focusses on the gap analysis between the required and available data, an analysis of the data quality and possible integration issues, and suggestions for additional data gathering, management and storage if necessary.
Roadmap for the future
Almost there! We finish our data audit with a detailed roadmap on how to get going with your top AI-enabled business cases we identified in the previous steps. This high-level report is a hands-on document in which you’ll find three main things:
- The outline of all required steps to start a business case based on the defined methodology and data.
- A proposed technical architecture.
- A list of essential profiles you’ll need to develop a specific business case.
And that marks the data audit as complete. A data audit is one of the best ways to not only identify any AI business cases in your company. But also to determine which ones could be the most viable. Meaning, which business case holds the lowest risk, builds goodwill within the organization, has a clear Return on Investment, and could potentially create spillovers for future AI projects in your organization.
Is your organization ready to take the AI leap? Great! Drop us a message, and together we’ll lead your business to AI success.