Hello INNOVATEwest Community,
Welcome to the 2nd edition of INNOVATEwest PULSE Newsletter. For AI to truly impact our world, it has to be trustworthy—and that requires the right infrastructure. Speaking with INNOVATEwest, Michael Matrick, the VP of Enterprise at Improving after the company acquired his startup Jump Analytics, explained more about how business leaders can build the crucial elements that make AI possible.
Key takeaways:
- Founders need to be analytics driven, which means seeking the right information, at the right time, to make decisions.
- Great data requires infrastructure, which starts with governance processes—only then can you build out your technology and work with stakeholder partners.
- As you look to build your analytics foundation, seek out real-world examples of how others have done it so you can get inspiration and avoid potential pitfalls.
Dave Tyldesley
Co-Founder/Producer SAAS NORTH & INNOVATEwest
Editor INNOVATEwest PULSE
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AI will be coming into most organizations in one way or another.
Yet many companies might be precluding themselves from using AI—or at the very least making implementation much more expensive and painful than it needs to be.
The solution: data infrastructure, a drum that Michael Matrick, Vice President of the Enterprise Group at Improving and steering committee member for INNOVATEwest, has been beating for years. His career has been marked by data, first as co-founder of a small data visualization startup he sold to Microsoft, then as co-founder of Jump Analytics, which Improving acquired in early 2023.
Ahead of the inaugural INNOVATEwest summit, Michael shared the framework he uses to help companies build the kind of data foundation that makes AI evolution possible.
Becoming analytics-driven
The best data is not necessarily about the greatest quantity; it’s about whether you can use it.
“Being analytics driven is really having access to the information that you need to make quick, efficient, reliable decisions about your business and do it in a cost effective way,” said Michael.
But this is often easier said than done—if you don’t have the right foundation in place, you can’t access the data you need. Or, perhaps worse, you can’t access it in time for the insight to be valuable.
“What I’ve seen in my career in working with some of the largest companies in the world is that when they get to an output—which is a report—many times the data is wrong or it’s delivered too late to fix a problem,” said Michael. “So people are looking backwards and not forwards.”
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Build a data foundation
Once you have that analytics-driven mindset, what’s a company leader to do to avoid the too-late-to-fix challenge?
Whether startup, legacy small business, or enterprise, Michael’s advice is the same. The first, most crucial step is executive alignment and governance, which is how you collect data so you can use it for its intended output—making decisions. And if governance isn’t strong with executive buy-in, then the rest doesn’t matter. For instance, if you say you have to collect data in a certain way so it’s usable, but execs don’t actually care, you can all but guarantee data will not be collected properly. Some companies try to get around this by hiring a data scientist, but Michael said this is more of a bandaid than a real salve.
“[Without buy-in and governance], you’re trying to create a data driven business, but you’re not putting the discipline into the processes that are required to be able to get there,” said Michael.
Then it’s about infrastructure—ensuring your technology is set up to be trustworthy by itself and to empower your governance structure. On the platform side, this often means double-checking integrations and ensuring your data feeds are sending information to the right cells. On the governance side, this might look like the platform intervening whenever someone tries to manually change data; they may not be allowed, depending on their permissions, or might need to leave an audit note explaining why the change was made and where the new data comes from.
The third piece is building strong stakeholder partnerships with the organizations that make the data platform you’re using. That might be a point SaaS solution all the way up to a Microsoft Power Platform or SAP level data warehouse solution, but the key is to make sure you’re leveraging the platform’s best practices and support so you can build the best possible data analysis for your business.
“Essentially what it means is you have to lay a foundational architecture to be able then to start to fill those KPIs and those reports up to the organization to then be able to utilize,” said Michael.
These three steps all make AI possible; without any one of them, it will fail (or develop a problem that will be incredibly expensive to overcome).
“You’ll spend money trying to do AI and you’ll fail miserably because you’re not adopting the governance, technology, and processes that you need to set up in your organization to be able to support that level of analysis,” said Michael.
The need for real-life examples
From his time at Jump Analytics to now leading teams at Improving, Michael’s theory is that entrepreneurs in organizations of all sizes need more education opportunities to learn about how to leverage data in their organizations.
In particular, he feels they need real-world examples of businesses building good data architecture—this will not just explain the concepts, but show data in action. This is one of the main reasons he got involved with INNOVATEwest; to help build programming that showcases real examples.
And if you ask him why, it’s all about making data analytics at all levels—from basic insight to AI models—more accessible, trustworthy, and understood.
“If you can’t trust the data… you can’t use it,” said Michael. “So that’s what I want to see more at events like INNOVATEwest; places to share practical expertise, knowledge, and understanding about how to build these solutions.”
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