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Jeremy Martin | April 6, 2018

Q & A with Olivier Travers

Background on Needle Stacker

Jeremy: Can you start by giving background on Needle Stacker?

Olivier: Needle Stacker helps publishers, trade associations and brands to attract qualified readers and grow revenue. We gather, curate, and visualize business data on behalf of these organizations to complement their editorial or content marketing efforts with dedicated data hubs. 

We also use the same analytical and visualization skills to help integrate sales and marketing data and applications for internal needs such as revenue growth. In the end, publishing on the intranet is not so different from reaching an external audience: you’re still fighting for people’s attention. 


Jeremy: How did the idea for Needle Stacker come about?

Olivier: My previous business was in trade publishing, where blending data and news was a big part of my focus. Lately I refocused my market approach to serve any company that needs help working with data and integrating software — especially SaaS and internally-produced data for sales and marketing. But on top of data you typically need to add a layer of narrative to make sense of the numbers. Show not just what and when, but also explain how and why.

Jeremy: Who are your typical clients?

Olivier: Mainly small and medium businesses. I do work with enterprise clients, but more at a departmental level. Typically I work with company founders, owners, CxOs, and people at the VP and director level. Engagements go from very short consultations (1 or 2 hours over the phone) to ongoing projects spanning several years.


Integrating Data for Small- to Medium-Sized Businesses

Jeremy: What’s your approach for helping SMBs?

Olivier: It may sound arrogant, but I want to know their business better than they do. I want to know their market better than they do — where they’re weak, where they’re strong — then feed that knowledge back to them.

Usually I will try to do this by learning where companies get their data from, then teaching them how to structure it better. In other cases, I can try to find repositories of structured data outside of the company, which saves them a lot of time from having to do it themselves. It’s easy for organizations to be inward-looking and think they face unique challenges, but in reality you run into common patterns. It’s part of the benefit of bringing someone to look at your business from the outside in.

Jeremy: I’d imagine there’s a lot of cleaning of data.

Olivier: Definitely. Years ago people would say: “There’s an app for that.” Now I say: “There’s an API for that.” So sometimes my approach to helping SMBs comes down to piping in data from Salesforce, Microsoft Dynamics, and so forth, then combining that data with other sources including public APIs and data repositories, There’s a lot of data out there if you know where to look. Then of course you have all these Excel spreadsheets that you find everywhere.

But again, before you clean data, it’s better to have a clean understanding of the business needs. What’s everyone’s actual knowledge of their system of record? Is there an onboarding team? How will this work on mobile? Do you want a snapshot of data, or a deep dive? Once I hash out these needs, that’s when I can start to think about the solutions, about whether they should ditch a piece of software they don’t really need, or help them make better use of one they already have.

Jeremy: Are there any patterns you see with what these business needs tend to be?

Olivier: A lot of the time the data gets spread out. Most businesses need a set of core applications where they can reliably store their data. Otherwise, it gets very complicated, fast. You bump into shortcomings and friction points. You might, say, generate leads through a marketing automation platform that’s integrated with your CRM. But your business doesn’t get proper revenue attribution by channel because the leads flow down the funnel, yet the sales don’t flow back up the funnel to marketing.

Jeremy: What role does IT play in these scenarios?

Olivier: Complicated integrations almost always require IT to overcome these friction points. But since there are functional needs, too, part of my job is to bridge the gap between the business side and the tech side, within a reasonable budget.

Jeremy: What’s the most rewarding part of finishing a project like that?

Olivier: I really like seeing how I’ve shifted people’s thinking about a problem. It could be changing a finance department’s idea about how they can use Power BI, for example.


At the beginning of a project a business might feel overwhelmed by lead management. They want to receive better leads and be better at managing those leads online. So I help to assure them that there’s an easier way. And by the time the project’s done, it feels good to see that they’re no longer overwhelmed about lead management and saving a lot of time, going to sleep earlier, gaining back a sense of control because of a workflow and a method I’ve helped them to implement.

For me that’s rewarding, to see the before and after and to know that they’ll experience that benefit daily. It’s a very good feeling.


BI Tools, Data Sources, & Visualization

Jeremy: What are some of the top challenges a BI user might face day-to-day?

Olivier: Everybody’s got a website, which means most people have Google Analytics. A challenge you often see is companies who’ve not yet realized the true potential of these ubiquitous analytics tools. They might have an email newsletter and live chat generating leads flowing down to sales. There’s a lot of upstream activity and sometimes it’s hard to see how that affects sales downstream. So a gap between marketing and sales is something you often see. They don’t even agree of what’s a lead.

When it comes to troubleshooting a tool like Google Analytics, there’s just so much that can happen, especially if you want cross-channel attribution. So there’s always that cost-benefit discussion. Is it worth our time to spend X more hours troubleshooting a BI tool? For most who are working within a confined budget, you have to make due with the low-hanging fruit initiatives. Few of us have millions of dollars to spend in Google AdWords and all the tracking that goes around it.

googleanalytics Jeremy: True. What other types of data sources do your clients pull data from?

Olivier: A lot have a wide mix, many companies use dozens of SaaS! Between ERP and accounting operational backends, marketing automation, CRM, and so forth. My work is to help make these systems become more integrated, hopefully without resorting to manual CSV downloads! You also often need to improve the data quality in these systems of record, because you don’t want to record garbage then feed it downstream into your BI. So I’m often digging into root systems, “operational BI” if you will.

Jeremy: What happens if your clients change systems? 

Olivier: This is pretty common, actually. They may do ETL that feeds into Power BI or use a bulk extract for Excel. Then tomorrow they want to use Tableau. If you dig into why they want to change which BI tool they use, sometimes it has to do with the data quality. Maybe half of their sales have a null country column and they want those filled in. But this belongs in their CRM. So on the surface it’s a BI problem. Deeper down, it’s a data source problem having to do with structure and quality. So I’ll start asking, “Have you gotten your sales agents trained on data entry?” And usually because sales reps are so driven to hit their quotas, an organizational design helps to keep them focused while improving the data quality. It’s a lot of work to make order of a chaotic environment. But not if you approach it in a holistic manner and automate whatever you can.

Jeremy: What are some of the key trends you’ve seen around BI over the past year? 

Olivier: IT-driven, structured BI has been around for a long time, but I think the recent self-service generation has more legs. Although I do believe there will always be a need for some form of data governance. BI works really well when it proves its business value sooner than later, but pilots and proofs of concepts need to grow with some institutional stability at some point.

"It’s a lot of work to make order of a chaotic environment. But not if you approach it in a holistic manner and automate whatever you can."

Jeremy: There are so many ways of visualizing data. How do you know when you’ve chosen the right kind for the story you’re trying to tell? What makes a graphic “insightful”?

Olivier: It depends. Right now, despite the hype I think there’s an attention span limitation and skills gap, So in reality many people are fine with relatively basic visualization rather than more advanced data analysis. At the top of an organization, simple charts often suffice because what these execs are looking for are general trends or quick updates. Advanced chart types and scatter plots require a dedicated data analyst to sit down and interpret it.

Sometimes it depends on the culture of a specific industry. On the one hand we have satellite imagery data and A.I. and self-driving cars. On the other, our daily experience with technology often ends up in confusion and frustration, with software choking on the simplest data format variations, and then you have to nudge it back into working. As we advance, more automation will take over. But you’ll always need that human touch.

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