10 Ways Data Is Like Water

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1. Companies cannot survive without data.

Humans will perish without water after three days. Similarly, data sustains the continuous operations of any company, whether it is a sales order, marketing lead, or employment data. In addition, the best-performing marketing organizations tend to be highly data-driven. In this digital age, a company simply cannot compete or even function without data.

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2. Companies can drown in too much data.

Data is essential, but too much data can also overwhelm marketers into paralysis. Studies estimated 90% of the world’s data was created within the past 12 to 24 months, and this growth is accelerating. Just like humans need to manage water resources to prevent flooding, companies need to manage their data to avoid drowning in it. New data automation technologies that help companies collect, store, clean, process, and analyze data are like the levies, dams, and pipelines for data.

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3. You can be surrounded by data that you can't use.

Raw data is like being thirsty and surrounded by ocean water. There is plenty of water but not a drop you can drink. Without the ability to process raw data into a usable form, a company can have access to all of the information in the world, and yet it won't make any difference. Big data can transform your marketing operations, but only if you have the ability to process it into usable forms in a timely manner.

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4. Data flows everywhere.

Water will flow through every crevice, finding the path of least resistance. Data flows the same way within a company and across companies. Even with the most comprehensive data security tools and data-governance practice, it is nearly impossible to completely control the intentional and unintentional distribution of data. Data often doesn't flow to where it should but flows to where it shouldn't. Marketers should be aware of the intended and unintended audience when collecting and distributing sensitive data. Want an example? Search “Uber God View.”

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5. Data gets dirty and stale if left unattended.

Unattended data becomes nasty, just like water in a neglected swimming pool. A good example of neglected data is to count how many company names you have in your CRM and marketing automation systems. It seems the longer the company list is, the faster it grows. And similar to contaminated water, the dirtier the data is, the harder it is to clean it up.

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6. Expensive data may not be better data.

Your municipal tap water may be better than expensive bottle water. In the same way, the data you already own may be better than what you could acquire from a data vendor. Your data could be enhanced by leveraging Open Data, which is now widely available through government agencies, educational institutions, and nonprofit organizations. You just need the ability to access it, work with, and make sense of it.

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7. Packaging matters.

The right packaging can sell bottled tap water for $3. It’s the same for data. Data-visualization solutions can help you package your data to enhance persuasion and impact. However, data visualization must operate on clean data; otherwise it is just garbage-in, garbage-out. For every one hour spent on data visualization, you will spend four to nine hours collecting, cleaning, and preparing data.

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8. Data management is a long-term project.

Whether the problem is too much water or too little water, municipalities need to have long-term plans and invest in capital projects to manage their water problems. The same approach is required for data management. While solutions for data visualization and analytics are easy to experiment with and switch, solutions for the management, collection, storage, and processing of data are more durable and expensive to switch, thus requiring forethought and investment.

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9. Data quality should be fit for purpose.

You wouldn’t buy Fiji water to water your lawn, yet companies often take a perfectionist approach to data projects. The pursuit of perfection leads to projects that take too long and cost too much. Whenever possible, take a pragmatic and agile approach to managing data quality by doing just enough to meet the business needs.

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10. Clean data at the source.

It's more efficient and effective to have the municipal water district sanitize your water once, in comparison to cleaning it bucket-by-bucket for each application. However, companies continue to spot-clean their data for individual systems and use a number of different tools for this process. This approach is expensive and inefficient, and can lead to severe data quality and governance issues. It is best to clean the data at the source if at all possible.