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Three Speakers You Don't Want to Miss at DataWorks Summit Berlin 2018
April 11, 2018
Three Things Every Business Owner Should Know About the GDPR
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Five Common Big Data Myths

With so much hype around big data, it’s not uncommon for its value to be oversold and its power exaggerated. This has the unfortunate side effect of creating several persistent big data myths.

So what are some of these fictitious narratives? Let’s take a look at the most pervasive big data myths and break down the reasons why IT and business decision-makers should think twice about the hype and investigate the power of insights as soon as possible.

Myth 1: We’ve Missed the Boat and Already Been Left Behind

The amount of information continues to grow inexorably. According to IDC, the global volume of data will rise tenfold to 163 zettabytes by 2025, which is equivalent to watching the Netflix catalog 489 million times. Even if your business has been slow to exploit big data, the continuous growth of information means it’s not too late to get involved.

Big data, however, should be treated as more than a sandbox for innovation. Start small and focus on a tightly scoped project that meets a business requirement. Tie your big data initiative to a specific use case, such as understanding how and why customers interact with your brand or website, improving operational efficiency in a supply chain, or reducing the risk of cyber crime. Honing in on a strong use case will help prove the benefits of the approach and enable the rest of the organization to understand the value of the insights. Start working with a smaller amount of information and grow your efforts over time.

Myth 2: Collecting Huge Amounts of Information Will Produce Answers

Exploiting big data doesn’t have to mean working with massive amounts of information. While some organizations are struggling with petabytes of data, you can still derive insight from data sources that collect 100 terabytes of information—or even less.

Remember that collection is simply the first step in the big data process. Although bringing together disparate sources is crucial, according to the American Marketing Association, the key to success isn’t the amount of information you collect, but the level of insight you obtain.

This insight will come from the quality of analytics you run on the data you collect in lakes and warehouses. Success relies on a tightly defined goal. Collecting data with a business aim in mind will help your organization tap the right sources and create insights through analytics that drive true change.

Myth 3: Big Data Projects Are Technically Difficult and Financially Expensive

As we’ve mentioned above, smart firms will start small by picking a range of sources to investigate how a big data project works and how new business intelligence can boost operational efficiency. According to Gartner, your company can tap into a broad range of open source tools to explore analytics without a significant financial outlay. You can set up a small cluster and scale capacity on demand as the benefits of big data are proven across the business.

Scaling up doesn’t have to be expensive, either. Senior stakeholders might be concerned that increased interest in big data from line-of-business executives could lead to new budget demands. Again, open source tools, deployed on commodity hardware or the cloud, can play a key role here, helping your organization run a rising number of big data initiatives in a cost-effective manner.

Myth 4: The Work of Data Scientists Will Be Replaced by Machines

Demand for data scientists is growing. According to The Financial Times, 100,000 to 190,000 data science jobs will go unfilled in the U.S. by the end of this decade. While algorithms and artificial intelligence help organizations analyze data, automation is best viewed as an adjunct to human data science capability, rather than a replacement for highly prized labor.

A combination of smart machines and people allows firms to interpret data in new ways. Data science involves a range of skills—such as data preparation, manipulation, analysis, and business engagement—that are unlikely to be held by one individual.

As organizational demands for big data develop, be open to the idea of creating a data science center of excellence that allows your business to train multiple people across a range of skill sets. These specialists will provide the analytical explanations and recommendations your business needs on an ongoing basis.

Myth 5: Using Big Data Involves Too Many Sensitive Issues

It’s true that big data can be a bit of a minefield. Collecting certain types of information—and then using this data safely and sensibly—involves a range of concerns, including careful governance, customer/user consent, and high-level security.

Continuing legal pressures, such as the forthcoming General Data Protection Regulation (GDPR), mean data integrity remains paramount, both for compliance and in terms of negating the costs of a data breach. By embracing security and governance from the beginning of your project, your organization can ensure that its data management is compliant. In this way, governance and security can help your organization use data more effectively.

Now’s the Time to Bust Big Data Myths

The only way to debunk these big data myths is to jump in and get started. Your organization might be concerned about some of the financial, technical, and business issues that often cause executives to hesitate before making the most of analytics. Your best bet is to start small, prove the benefits of big data, and grow carefully. The sooner you get started, the sooner you can reap the dividends.

Learn more about how a big data program can help your business unlock the power of insight.

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