Data & Analytics

Data & Analytics
ERP Usage and ChallengesERP Usage and Challenges

How are decision-makers using Enterprise Resource Planning (ERP) tools? Benchmark with your peers.

If you had a magic wand - what's the #1 daily business challenge you'd eliminate?

Top Answer: Without a doubt - Technical Debt! It's a ball and chain that creates an ever increasing drag on any organization, stifles innovation, and prevents transformation.

Data Centers in 2021Data Centers in 2021

Have your data center priorities changed? Benchmark where you’re at with data centers against your peers.

If you are a current SAP customer, when do you plan to migrate to SAP S/4HANA?

Top Answer: No plan to migrate soon.

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How much of your IT budget would you allocate towards good data hygiene?

Top Answer: Seems like close to half aren’t putting more 3%. A good follow up would be why? What’s are the other top priorities?

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What is it like to be a data provider in the marketing industry?

Top Answer: We provide data specifically on whether people are looking at your ads. As most executives know, it's very difficult to know whether or not your marketing budget is being effective and whether that translates into actual purchases. We do large panel surveys all around the world to answer advertisers’ questions about what types of ads people are actually seeing and on what platforms. We use machine learning (ML) and computer vision to determine this. The huge amount of data that we collect is then aggregated and used to optimize large advertising campaigns. There's a lot of data I need to deal with. It’s a very complex data pipeline and involves a lot of R&D.  My particular background is in that R&D space. I have a PhD and I've done a lot of roles involving computer vision, machine learning and artificial intelligence (AI) across a number of industries before going into marketing — including defense, police and medical areas in particular. Marketing is not often the direction that you go after working in those spaces, but it's been enjoyable.

How did you get into marketing tech (MarTech)?

Top Answer: I didn’t envision becoming the CTO of a marketing tech startup. I did a lot of contract work prior to my PhD and during my PhD, where companies would come in with a question that I thought could be solved by machine learning and I'd spend six to 12 months working on it unsupervised, just deciding my own work. Then I got fed up with the huge amounts of paperwork and restrictions that made it difficult to do R&D within academia. I worked in defense next, which wasn't much better. After that I went into the medical industry, which requires even more paperwork. I wanted to get into an area that would allow me to solve problems without having to fill out forms, and marketing turned out to be an excellent place for that. It proved to be a good decision.

Can CIOs act as Chief Data Officers (CDOs) for their organizations today?

Top Answer: I often hear folks say, "I've done your role before. I used to run a data warehouse.” There are a lot of people who have run data warehousing, but I don't think people understand the complexity. You have to navigate the nuances of all these regulations to be a bridge between marketing and the privacy office. You have to be able to effectively communicate and translate laws to people that are wanting to consume data. They think they have free use just because they have access to it, which is not the case.  Being a CDO is not just about running a BI shop and knowing how to build reports. It requires a much deeper understanding because you have to be able to deal with legal documents and contracts. Conflating data warehousing with a chief data officer’s job is a fundamental misrepresentation of the role. It's a broader skill set than what some people think. The other thing is that there are architecture components involved as well. When we talk about building out a scalable data architecture in data services, that's a fundamentally different skill set than building a warehouse. And a CDO has to be able to think through both. Take online transaction processing (OLTP) as an example: how do you get data and build efficiency in regards to how that data is accessed and consumed, not only for reporting and analytics, but also in the systems themselves?

Thinking about deep machine learning, how ready is your data (cleaned, prepped and labeled) for compute-heavy data models?

Top Answer: Probably depends on age and size of business. Ours has divisions over 100 years old or acquired less than 2 years ago. We struggle with data quality even within a division. Note that our smallest is probably 25,000 employees and largest 40,000. Total company 130k people, 15 major ERPs and probably 20 minor ERPs. We still have debates on Customer address and shipping fields sometimes during consolidations. My estimate 35% of the data we would like to use is ready

How is your team leveraging analytics to make business decisions? Comment below on what analytics tools you use.

Top Answer: We use Qlik Sense and have various apps for different teams that tracks information for their teams/groups. Its useful to provide analytics for systems that do not have that included natively but also to create reports/dashboard that the native analytics of the vendor is missing. For example if your phone system is missing a certain report you can pull the info from the phone system database and create a custom report that is important for you. There are is a free desktop version as well, which is useful for analyzing data from various report usually from databases or excel. Power BI is an a very straight forward SaaS tool, if you are o365 user its a no brainer to get started with it.