SPRING POWERS|DATA CLEANSING|BETTER ANALYTICS

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Is Your Data In Need of A Spring Cleaning

Spring is here, and that means it’s time for doing that spring cleaning. You might be planning to spruce up your home, or your wardrobe, but spring cleaning shouldn’t just be limited to your personal life. There are many areas of business that could use an annual clean-up – like your company’s data. Dirty data – from whatever source, social media, your CRM or analytics platform – is low-quality data, and it comes in a myriad of ways. The data may be incomplete, missing, or the data might be redundant. Whatever the cause, chances are it’s costing your business dearly. For one, low-quality data derails marketing efforts, as both proper personalization and segmentation are reliant on accurate data. Plus, it affects lead generation, because bad data makes it more difficult to judge the quality of a lead. Finally, bad data takes your team away from more critical tasks. One survey, for example, found that data scientists and analysts spend between 50 and 80 percent of their time cleaning up data. Now the real question: How do you fix it? There are a number of ways to do it, and plenty of wonderful tools out there. But first – before you jump into a massive-scale data clean-up this spring.

How Dirty Data Can Wreck Your Sales Productivity via PipeLiner CRM Blog

How Dirty Data Can Wreck Your Sales Productivity via PipeLiner CRM Blog

Dirty data can have a huge impact on your bottom line, and it’s especially disruptive to your sales funnel. Author Aleksandr Peterson offers some examples of low-quality sales funnel data, how it affects businesses, and strategies they can use to fix it. Although the focus is on the sales funnel, many of the same principles apply for other business data use cases.

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4 Steps to Help Kickstart Your Data Cleaning Project via Dataconomy

4 Steps to Help Kickstart Your Data Cleaning Project via Dataconomy

You have a data quality problem, but how do you go about fixing it? This Dataconomy article features a four-step process that author Aleksandra Besinka’s company uses to address and diagnose the problem. But the principles outlined are useful and they can be adapted for a number of different applications, including for updating and enhancing CRM data.

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8 Ways To Ensure Data Quality via InformationWeek

8 Ways To Ensure Data Quality via InformationWeek

Prevention is the best medicine, they say. And the same holds true for dirty data. Preventing low-quality data from making its way into your databases can save you a fortune and help you grow your revenues. But that’s easier said than done, especially for large- and mid-sized businesses that don’t have an established “data culture.” Here, you’ll find some higher-level ideas and tips for preventing dirty data.

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To Avoid Poor Data Quality, Start with a Business Question via DataInformed

To Avoid Poor Data Quality, Start with a Business Question via DataInformed

Before you begin any spring cleaning project, you should know the business goals and potential outcomes that are tied to it. All too often, author Jake Freivald notes, data-quality issues are handled by an organization’s IT department, with little or no input from the sales side of the operation. To help focus your efforts, Freivald provides three questions businesses should ask prior to embarking.

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7 Ways to Tell If You Have a Marketing Data Problem via ConduitHub

7 Ways to Tell If You Have a Marketing Data Problem via ConduitHub

This short-and-sweet article offers seven specific ways to diagnose problems with your marketing data. Whether your campaigns are underperforming, or your newsletter open-rates have fallen, the root cause might be a problem with your data. Don’t know where to look for issues? This article offers a range of ideas for addressing them.

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