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5 Minutes with a CTO: Darren Peirce of Magnitude Software

What's it like to be a chief technology officer? We look at the job itself and what role analytics plays with Darren Peirce of Magnitude Software.

• By James E. Powell
• January 17, 2017

What's it like to be a chief technology officer? What skills do you need, and how do analytics fit into your job? We asked Darren Peirce, chief technology officer and vice president of products at Magnitude Software for his perspective.

The Role of CTO

UPSIDE: What is the one thing you wish people knew about your job?

Darren Peirce: Finding balance between enterprise customer priorities and market trends is a continuous challenge. The adoption cycle for new ideas within the enterprise, no matter how transformational or obvious the benefits, is always longer than one expects. A critical part of my role is translating new ideas and innovations into solutions that are both a priority and concretely actionable for enterprises today.

Is there a piece of advice you wish someone had told you when you first became a CTO?

Don't focus on the most urgent items, focus on the most important. It is easy to get sidetracked by items that have the appearance of urgency but are not necessarily important.

What's one thing you think some leaders do wrong when managing analysts?

Going in with the expectation that analysts know your lingo. Sometimes leaders get caught up trying to convince analysts using their own internal language and terminology rather than the terminology and context of the audience. It's like expecting a third party to understand the nuance and history of an inside joke between two people they don't know.

What personality trait do you think CTOs need to succeed?

Above all, perseverance, combined with a refined set of active listening skills. Why? Every effective software solution is targeted at addressing a specific area of pain. The better you understand the problem, the more complete (and therefore effective) your solution will be. Listening is a great way of understanding the problem.

Your first attempt will seldom knock it out of the park. You need to carefully establish and set organizational expectations such that you can continually iterate on refining your understanding of the problems and the solutions you bring to market.

Your Work

What trend or idea do you think is the most overhyped today in the corporate world -- be it about analytics or business in general?

Big data. Most organizations are still a long way from delivering effective governance and gaining insights from "little data" (otherwise known as enterprise data), so expecting them to effectively leverage big data in the immediate future is pretty idealistic. The terminology is due for a revamp.

I do not, however, mean to imply there are not great opportunities for new technologies and approaches popularized under the big data banner. Those initiatives should not be confused with the data itself or getting value from that data.

What is your favorite tool or technique that has made your job easier (and how is it easier)?

My mindset and technique is to continually tie an idea back to a specific person and their problem. There will never be a shortage of good ideas -- even great ideas -- but these must address and ultimately prioritize the intended beneficiary or the idea cannot be made a priority.

Whether it's the latest smartphone or a real-time stock ticker, what's the one thing you can't do your job without?

Microsoft OneNote or Evernote. I need ubiquitous access to a knowledge base. Each day is packed with dozens and dozens of conversations both inside and outside my organization, so it is essential that I can continuously refer to prior ideas circulated, conclusions reached, and actions taken.

What's the most common roadblock you hit in your work? How do you deal with it?

Trying to achieve outright consensus. I imagine this is a common refrain among my peers, and I've come to live by the rule that ultimately it's more important to decide and move forward on a plan of action (while continuing to iterate) than to defer on a given decision in the absence of consensus.

Using Analytics in Your Work

How are you, personally, using analytics in your day-to-day decisions? What data do you look at every day? Every week?

We use analytics to gain and maintain visibility into project status and pipeline, along with the supporting information about those projects.

What form of analytics do you use to make sense of all the data (for example, daily reports, real-time dashboards, exception alerts)?

For me, it comes down to daily reports. With a daily touchpoint, you can more readily spot anomalies and outliers that may otherwise slip through the cracks.

Do you run reports yourself or are they pushed to you (for example, in a daily email message or a first-thing-in-the-morning dashboard)?

I run them myself -- on demand.

Is your organization using analytics every way it can? Are there other decisions you'd like to see analytics used for?

There are always more ways to leverage analytics to drive more proactive management of the business -- of any business.

What one thing do you wish your organization could analyze better (and why)?

Even speaking as the CTO, I believe we could produce more near-real-time product profitability models and sales metrics.

What's the most significant outcome you've seen come from analyzing data?

We've gained incredible insights into product profitability, which in turn drive product optimization decisions.

If you had unlimited resources, what would your data analytics wish list look like? Are there new technologies you want to try? Would you want to hire a data science team?

Yes! I'd love to see more integrated analysis of market and competitive performance, followed by more accurate modeling and projection capabilities for future scenarios.

Where is data analytics/data science headed in the next few years within your organization? Within the world at large?

I see the largest opportunity in moving from real-time decision making to future state modeling by way of advances in AI and machine learning. In addition, we will see increased value derived by accessing and incorporating a larger volume of external data sources so as to more accurately predict market and competitive shifts.