Big Data

Big Data
Which data warehouse does your organization use?

Top Answer : We use multiple OLAP repositories and analytics tools.

Can CIOs act as the CDOs for organizations today? Or do companies really need a separate CDO who can partner with the business and IT to drive digital transformation?”

Top Answer : First, worth clarifying if we are talking Chief DIGITAL or Chief DATA Officer. In either case, while I used to think that the existence of the CDO meant that the CIO was failing at their jobs, in  today's world, I think the roles are distinctly different, though in the end it really depends on the people.  CDOs as digital officers are really focused on the digital transformation of their companies, and inherently have an external focus.  CDOs as data officers are focused on key information management within the company - optimizing data as an asset.  The CIO at many companies predominantly focused on internal operations, business and user productivity. The skillsets needed and more importantly, the incentives for these objectives are often different.  Intertwining them is possible, but I think in many cases companies are better served by having a separate focus.   All of that is dependent on whom we are talking about both in terms of people and the business.  The people matter even more in this question than anything.  A company with a super strong CIO working for a digitally minded CEO probably doesn't benefit from a CDO nearly as much as an internally focused CIO working in a business with complex operations that has also recently decided to transform the delivery of their products to include more digital delivery would.   So in the end, I think it really depends.

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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

What are your thoughts on SaaS management platforms (SMP)?

Top Answer :

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What do organizations get wrong when it comes to data lifecycle management?

Top Answer : The way we’ve produced consolidated reporting is all the data gets dumped into Snowflake, and then you try to find connections. It's less of a data warehouse and more of a dumping ground. Maybe we could try to create consolidated reporting and then try to figure out the data integrity issues. That's one area that I think we are hoping to modernize at ZoomInfo. In a way it's just an evolution that’s part of any startup story, they're growing too fast. It's a great opportunity to bring in that rigor so that we can scale to the growth, streamline and hopefully optimize all of the operations, systems, and technology. We need to have data lifecycle management, otherwise we'll just keep collecting it all.

Is it possible to aggregate Big Data for intelligence purposes without sacrificing risk mitigation?

Top Answer : I’m not sure if there is an appropriate line to draw between risk mitigation/compliance and giving access to data to the right folks so that intelligence can be derived. The challenge is extracting the purified content out of it—leave out the noise and pick up the signals so that you can do something with them. With all of these different platforms and cloud solutions that folks are adopting, their data footprint ends up all over the map. At some point people want to put it together, make connections across these data sets to derive intelligence for operational efficiencies, new market identifications, optimizations and new service offerings.

How should we approach the problem of excessive data collection?

Top Answer : I started the idea of multi-tenancy for IoT, realistically multi-tenancy for data back in 2016. The basic idea is, we need to find the right way to get the maximum value out of the infrastructure we're building, and thereby not create even more sets of data about the same stuff. I have no idea if this is even possible, but I've used a similar model for infrastructure design and build in the past. What if you could work with manufacturers from an application standpoint to define data value prioritization and retention models that applied to specific operational environments like shop floor or manufacturing machines, to where you could apply a policy that could be defined for you. While it sounds great, the reason I think it would never work is that there's never been a time where somebody has said, "Well, can you be 100% certain that I'll never want to go back and look at that data?

Will the cost of data make smart cities economically feasible?

Top Answer : Given that you're in Las Vegas I'll tell you this, Swim predicts ahead, the four looking two minutes for every single intersection in Vegas in real time. And that information, or those predictions are streamed to customers like Uber and FedEx and so on. You know how much I get paid for that? 25 cents per intersection per month. It's not good. It may go up in time, but it's not a great business to be in and it takes enormous effort to get there and it's expensive too. And it'll take a long time to turn into a retirement fund.