The State of Data & Analytics: Q&A with Ryan Wakefield, Consulting Director

We recently had a chance to sit down with Ryan Wakefield, Director – Data & Analytics, to hear what he has to say about the growing role that data plays in business and our day-to-day lives. We covered a wide range of topics, including machine learning, AI, data security, and more!

Q: As technology advances, how important is data and analytics? Is it 100% critical to the success of a business? Are there any specific factors that impact this?

Data, was, is and always will be critical to every business. We just see more and more of it because of open source technology, an explosion of digital, and the advent of cloud platforms. Almost all organizations that I speak with today understand that being a successful business means taking advantage of cloud and digital technologies and tracking and managing data assets intelligently. Everyone desires immediate or near real-time feedback on their personal performance so why not use the same approach for business decisions? The technology exists and allows you to quickly cut your losses if an investment isn’t having the appropriate ROI or accelerate investments that are contributing more than expected.

Q: What are people investing in today in data & analytics?

Cloud, Big Data Platforms, Data Science & Machine Learning (ML). All the usual buzzwords you see at a Gartner conference, but not in vaporware or marketecture form; I’m referring to tangible, scalable, and repeatable solutions with a solid codebase. Excellent products and solutions are being built today that will be the norm for many businesses of tomorrow. We just wrapped up an engagement at a major retailer where we modernized their big data platform to incorporate real-time insights and refactored the data science codebase to be more effective. These types of projects and investments are getting more and more common, not only in customer-facing applications, but also in internal applications and operations.

Q: What do you foresee as the next big thing in data & analytics? Long-term?

More advances in AI/ML and a stronger coupling back into business processes. This means more intelligent platforms and applications like recommendation engines, chatbots, AR/VR, etc.

Q: Artificial intelligence seems to be gaining a lot of traction right now, but business leaders like Elon Musk and Bill Gates have expressed concern over the long-term implications of AI.  Even now, there are some criticisms about the ethics of existing technologies like analytics in different industries (e.g. insurance and healthcare). How are you able to push the envelope and keep the ball rolling as far as innovation goes, while addressing these types of concerns in your solutions?

We could be in a simulation right now, right? Blue pill or red pill? In all seriousness, it’s difficult to see where AI will be long-term. I like the quote from Elon Musk where he says that we’re on an exponential curve right now with AI, when we look backwards, it looks flat, when we look forward, it looks vertical. We’re somewhere on that curve and we should start thinking about where it ends. I attended the MLConf last year, and almost every presenter had some consideration for ethics in their training data sets and algorithm design. In general, more people are considering the ethical behavior of their applications. Human nature has the capacity for the heroic and the heinous, so we should expect to see this manifesting with AI as well. I wouldn’t buy an underground missile silo yet; we’re a good 10+ years from I, Robot, the Matrix, Blade Runner, or pick your favorite robot movie.

Q: Data security and privacy appears to be a hot-button issue at the moment. How does your work help protect the data of companies AND their consumers?

You don’t have to search far to see the impacts of the Cambridge Analytica scandal or the disaster of the Equifax data breach. The truth is organizations have mishandled (and are still mishandling) your personal data. PII data is not as secure as you think, and in many cases, not classified correctly. And even if I cannot identify you because I only have non-PII data about you, I can cross-reference the data I have about you with third-party data providers to de-anonymize you. I expect that GDPR regulations, in some capacity, will be adopted by the US. US-based entities that do business in the EU already fall under the purview of this regulation. And there are significant fines for violations; for a $90b company like Microsoft, that can be a $1.8b fine (2% of global revenue). Building effective data governance programs and policies reinforced by tools and automation is the only way to address data security and GDPR regulations.

Q: What advice would you give a company that realizes it needs to invest in data & analytics? What’s the first thing they should prioritize?

Examine the business impact. What are your organization’s the top three problems? What keeps your boss up at night? What about your boss’s boss? Pick one and get started. And don’t spend months trying to figure out a gigantic program plan with estimated payback period, ROI, etc.; you’ll have time to do all that once you prove value in a POC. With great D&A leadership and a few engineers, you can deliver some great products.

Q: What advice would you give someone who is thinking about a career in data? College? Certification? What technologies should they focus on? Etc.

Go into this profession not because it’s one of the most popular right now (see data science on Google Trends), but because you’re passionate about data and what you can do with it. I’ve been working with data my entire career, and there are days when you’re staring at a dataset wondering which way is up. There are more and more colleges that are offering data science degrees, which is great. I’m wary of suggesting generalized advice as I’m a big advocate of individualized learning. However, there are core competencies to help guide the way; we have an internal D&A training plan based on AWS, Azure, and GCP cloud platforms with core learning around SQL, NoSQL, data engineering, ML algorithms, and data visualization. Pick something on that spectrum where you have an affinity and follow the edge to the next node.

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