Big data and advanced analytics are helping companies obtain greater insight from their business systems. The opportunities for leveraging data are endless. However, companies should be cautious in deciding if and how to set up a big data program. In previous posts, we explored “What is Big Data?” and how to get started. In this interview, Geotab asks technology author Phil Simon some key questions about starting up a Big Data project.

Phil Simon is a recognized technology authority and an award-winning author of seven management books, most recently Message Not Received. He consults organizations on matters related to communications, strategy, data, and technology. His contributions have been featured on The Harvard Business Review, CNN, Wired, NBC, CNBC, Inc. Magazine, BusinessWeek, Quartz, The Huffington Post, The New York Times, Fox News, and many other sites.

GEOTAB: What is your definition of big data?

PHIL SIMON: Put simply, the term big data represents the vast quantities of largely unstructured information streaming at us faster than ever. It’s comprised of tweets, emails, blog posts, sensor data, satellite images, and many more things.

Perhaps it’s best understood against its antithesis: structured data. Spreadsheets, lists of paychecks, sales, employees, and other transactions are neat and orderly that integrate easily with Microsoft Excel. Big data is organized differently, as such, requires new tools to be stored, analyzed, and understood.

GEOTAB: How can an organization determine if big data and advanced analytics are right for them?

PHIL SIMON: There are no magic checklists, but I always start by asking simple question: Can you tell me about a time in which the organization made a key business decision based on data?

If a Chief Experience Officer cannot provide a simple answer to that query, then it’s unlikely that the organization’s culture has embraced data-driven thinking. And make no mistake: success with big data is as much about people and culture as it is about technology and the number of petabytes of information that it stores. “Data” and “technology” don’t make decisions. People do.

Look at Amazon, Netflix, Facebook, and Google. These organizations excel at big data for many reasons—not the least of which is that they have embraced cultures of analytics. At these über–successful companies, data-based decisions are the rule, not the exception.

GEOTAB: What resources are needed to work with big data?

PHIL SIMON: Employees can only do so much with Small Data tools such as relational databases, SQL statements, and traditional business-intelligence dashboards. As I write in The Visual Organization, employees need access to better data-visualization tools.

They need to embrace a mind-set of data discovery and exploration. This means not having to submit report requests to the IT department ad nauseum.

Contrary to what many people believe, hiring a proper data scientist is no elixir. I advise hiring employees who enjoy learning new things and change their minds when presented with information that contradicts their preconceived notions. There’s a reason that a common Google interview question is, “Can you tell me about a time that you changed your mind?”

GEOTAB: What is your top tip for success with analytics and big data?

PHIL SIMON: First, realize that no organization goes from “zero to Google” overnight. It takes time to build an organization’s proficiency with big data. Google (a.k.a. Alphabet) can do things now that it couldn’t possibly do back in 1998.

Second, there’s no one right way to “do” big Data. In fact, there are different methodologies and philosophies of organizing a big Data project. The project can be led by a specific business unit, by a team, or by an entirely independent group. Along these lines, you don’t have to do everything in-house. It’s increasingly common to “rent” data scientists via sites such as Kaggle.

GEOTAB: What is the biggest mistake that companies make when starting up a Big-Data project?

PHIL SIMON: They think of them as traditional IT “projects.” This is almost always a recipe for disaster. Rather, it’s best to think of big data is a marathon, not a sprint. An organization is never finished with big sata. That’s one of the most common myths around Big

Data: there is a finish line. There isn’t. Act accordingly.

Next: Study up on your big data/analytics vocabulary by reading our post: Big Data Glossary: 50+ Terms Defined.

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Everything You Need to Know About data.geotab.com

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