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How Are Industries Using Predictive Analytics?
May 29, 2018
Predictive Analytics Software Prevents Downtime for Manufacturers
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Five Big Data Mistakes to Avoid When Implementing a Strategy

If leveraged correctly, a big data strategy can offer a competitive advantage. However, there are many obstacles that business executives might encounter along the way.

When implementing a new strategy to bolster your business with big data, mistakes can prevent you from realizing its full potential. Let’s take a closer look at five common blunders business executives should avoid when developing a big data strategy.

1. Handing Off a Data Strategy Project Without Sufficient Sponsorship

In many data projects that fail, the executive effectively says “let’s do data” and then delegates the project to a lower-level person. That won’t work for this type of business transformation opportunity. Successful projects begin with executive sponsorship at their core. As a senior executive, it’s up to you to take ownership of the data strategy at the highest level, so that your team can more easily overcome the barriers and obstacles you encounter.

Data can be the most important asset in a business, but the real competitive advantage lies in the intelligent use of that data: Can we use the data more effectively than anyone else to deliver a breakthrough product or experience? Data can be that transformative, so it is important to treat this initiative—even if it starts in a small corner—as an executive priority, with sponsorship at the CEO level.

2. Being Rigid in Product and Process

Begin your project in a way that is both strategic in vision and agile in execution. Determine how your data strategy and operation will look, and bring that vision to life in a global, organization-wide way.

From an execution perspective, you want be agile. Pick technologies that are open and expandable. For instance, avoid vendor lock-in by going with open source tools. During the project, it is important to foster a culture that encourages failing fast and learning from mistakes. Avoid letting egos drive the project and understand that if your team tries 10 things, eight of them might not work. Get people enrolled in your data project team who can thrive in this sort of DevOps style of work.

3. Placing Boundaries on Your Data Successes

Part of setting up a vision for data success is understanding and planning for how you will integrate new insights into the DNA of your organization. If you deliver a new insight, how do you drive it to the rest of the company? For example, if you derive a systematic insight that is easily integrating into the business logic of an app—such as a customer relationship management (CRM) or call center system—that also requires a culture change to reinforce that insight. Data insights are only effective when they actually move the entire business forward, so it’s important to harness that success and publish it. How does your insight change the technology or the processes that you deploy? This is another reason why it’s critical for the CEO to have an ownership role in a data project: these types of changes won’t take place if the CEO doesn’t own it.

4. Making Big Data Mistakes by Trying to Do Too Much

From a technology point of view, people often paint themselves into a corner. Sometimes, by trying to get a project started with a minimal level of resources, they end up with a piece of technology that can’t grow with the project as it develops and matures. After a pilot, the same platform needs to then go to the next level, and then the next level, and so on.

This makes it critically important to have a big enough vision for the architecture you’re going to be deploying right from the start, which often means choosing small technologies that are quickly developed and have open architectures that help you innovate quickly. Don’t get yourself into a situation where you’re waiting on a vendor to speed up before you can turn up the dial.

5. Neglecting Governance and Security at the Start

Governance and security are critically important these days, as privacy concerns become increasingly important. Businesses still tend to begin data projects as pilots, with just a few people working on them, and without governance and security baked in. This is a huge big data mistake.

Get governance, compliance, and security conversations started on day one of the project. Enroll the director of IT, the chief data officer, the attorneys, and whoever needs to sign off on these issues. Decision-makers must carefully choose the right governance strategies, as well as the right governance technology.

Download this study to learn more about big data challenges, architectures, and technology trends among U.S. companies.

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