Analytics Fireside Chat

Analytics Fireside Chat: Brent Dykes, Blast Analytics

Brent Dykes, Insights & Data Storytelling Senior Director at Blast Analytics, on navigating marketing analytics and data strategy.

Diana Daia
June 26 · 5 min 30 sec read

Get marketing analytics and data strategy tips from Brent Dykes, Senior Director at Blast Analytics, Forbes contributor, and author of Effective Data Storytelling.

You are an analytics evangelist and a true believer in the power of data. If you had to boil it down, what would the 3 key benefits of leveraging marketing data be?

For me, the three key benefits of leveraging marketing data would be a deeper knowledge of your customers, the power to optimize and improve your marketing performance, and the ability to demonstrate marketing’s contributions to the overall business.

A common challenge with campaign data is maintaining consistent and reliable tracking. Companies often share digital advertising responsibilities between multiple agencies or internal teams, and it can be a significant challenge to enforce a consistent taxonomy and process across channels.

From your experience working with Global 2000 companies, what are the common challenges that organizations face in regards to campaign data?

A common challenge with campaign data is maintaining consistent and reliable tracking. Companies often share digital advertising responsibilities between multiple agencies or internal teams, and it can be a significant challenge to enforce a consistent taxonomy and process across channels. Another challenge is not determining upfront what the desired business outcomes are. Rather than measuring campaigns against meaningful conversions, campaign performance may be based on hollow measures of activity and vanity metrics.

What tips would you give companies to make better insights-driven decisions? Are there any low-hanging fruit worth looking into?

The quality of the insights you receive will be a product of your measurement strategy and the underlying data. After you have a solid data foundation in place, I would encourage more organizations to focus on fostering data curiosity. Some employees may not be sufficiently data literate to properly explore, analyze, and interpret the data so basic training may be needed. For the individuals who are able to discover meaningful insights in the data, they will need data storytelling skills to communicate these insights effectively to decision makers. Unfortunately, there aren’t any shortcuts to generating insight-driven decisions. Companies need to approach it holistically and make investments that encompass the people, process, and technology aspects.

Many companies, especially in these challenging times, do not make leveraging marketing data a top priority. Why is that not a good long-term move?

Today, there’s no excuse not to use data to improve your marketing performance. If every euro, pound note, or dollar of your ad spend is precious right now, you want to use data to make your marketing efforts more efficient and effective. If your competitors are foolishly pulling back on their marketing investments and laying off their analytics staff, it’s a great time to gain market share and build not just for the present but for the future.

Today, there’s no excuse not to use data to improve your marketing performance. If every euro, pound note, or dollar of your ad spend is precious right now, you want to use data to make your marketing efforts more efficient and effective. If your competitors are foolishly pulling back on their marketing investments and laying off their analytics staff, it’s a great time to gain market share and build not just for the present but for the future.

How would you advise analysts to make stakeholders understand the positive impact of changing data culture within the organization?

Fostering a data culture across an entire organization is a massive effort. It’s impossible without executive buy-in and support. If you need to convince stakeholders of the positive impact of having a data-driven culture, I’d focus on showing them the difference that it makes within one team or a small department. This sample group would serve as a sort of proof-of-concept for developing a data culture. You can then extrapolate out the combined effects it would have across the entire organization.

In your latest book, which should be on the reading list of any digital analyst, you cover the science of data storytelling. As you say, 'Nothing pains me more than seeing good insights go to waste.' The truth is that many companies don't lack data. In fact, many argue that they are drowning in data that is not actionable or not put to good use.

What are your 3 winning tactics for effectively communicating with data?

  1. Too often when we communicate data, it can quickly turn into a “data dump.” When we do this, we can overwhelm the audience with too much information. Then the signal we’re trying to convey will be obscured by noise. Rather than treating all of the data points as being equal, you need to prioritize which key insight or insights will be most valuable to the audience. You can then build a data story for each key insight with the supporting information that is needed to explain its significance and drive action.

  2. The data visualizations that helped you to find an insight may not be the same ones you use to explain the insight to other people. The first phase of the analysis process is to explore the data. We often employ data visualizations to find meaningful insights. However, when we transition to sharing our findings with other people, that’s when we need to make our charts and graphs more explanatory. We may need to switch out data visualizations to ones that are easier to follow for the audience or redesign them so they communicate the key messages more clearly.

  3. If you only gave me one technique or tactic in my visual storytelling toolbox, it would be color. Sometimes, we underestimate the power of color in our data communications. It is extremely effective at highlighting key data points in a chart. For example, if you had a line chart with multiple values, you could highlight one of the series with a bold color to bring it to the foreground and then use grayscale to push the rest of the other lines to the background. I was about to sign a book deal with a publisher and discovered they were planning to print the book in black and white. I walked away from that book deal because I knew how critical color would be to explaining how to do effective data storytelling.

What are the tough truths that we don't talk a lot about in the analytics world?

I’ll share with you a couple of tough truths.

First, we like to focus on the “actionable insights” that our analytics solutions can provide us, but a ton of mind-numbing work goes into ensuring the data is relevant and relatively clean so it can yield these insights. Data standards and governance aren’t sexy, but they’re important to maintaining a sound data ecosystem.

Second, while we like to believe technology is what separates the analytics leaders from the followers, it’s the talent that really matters. I’ve seen high-performing analytics teams completely implode when a key leader or individual contributor left. In each instance, the company still owned the same technology. However, they lost the people who knew how to generate value from it.

We like to focus on the “actionable insights” that our analytics solutions can provide us, but a ton of mind-numbing work goes into ensuring the data is relevant and relatively clean so it can yield these insights. Data standards and governance aren’t sexy, but they’re important to maintaining a sound data ecosystem.

If you were to predict the biggest future analytics trend, what would that be?

I agree with a bunch of research firm analysts that augmented analytics will be a popular trend in the coming years. A lot has been written about how machines will replace humans in the workforce. However, before we’re all eventually supplanted by robots, we’ll see different aspects of our roles augmented more and more by intelligent agents. For example, rather than having to do all of the analysis on our own, each day potential anomalies may be presented to us for further exploration. With the click of a button, a few of these insights can be packaged up into a data story. After a quick edit at the hands of a skilled data storyteller, the data story can be distributed to targeted people who need to see it.

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