Posts about Data Quality

In times of global, business and economic uncertainty – when data points are changing on an hourly basis and supply chain responsiveness and flexibility are more critical than ever – being able to trust your company’s data is paramount.

For online retailers, one of the biggest challenges that directly impact the customer experience is inaccurate product data. If the image doesn’t match the description, reviews aren’t associated correctly, dimensions are inaccurate, or the description doesn’t live up to the actual product then chances are good that the product will be returned. This leads to additional costs for freight and extra handling and maybe even loss of customers. By consolidating and mastering your product data, product returns can be reduced by 25%, creating tremendous opportunities for cost savings and improving customer loyalty.

Your organization, and organizations of any size, depends on the accuracy of its data to gain financial insights and improve marketing, sales and customer service. In fact, pretty much every department within your company will rely on data in some way. 

And if that data is inaccurate, hard to access or only tells part of the story, it can severely hamper operations across the business. Data errors can seriously impact relationships with customers, suppliers and business partners and result in significant financial loss.

Cloud storage, regulatory compliance, security breaches – the list of factors that have driven companies to make changes to the way they manage data in recent years is endless. Combine this with the dramatic increase in the data most organizations now hold, and you end up with a highly challenging situation (and potentially missed business opportunities).

Take a look at your own data scenario. If any of the following sounds familiar to you, it may well be you have a data problem that needs to be fixed. Soon.

WARNING: Stranger Things Spoiler Alert!

We live in a world where virtually everything is a source of data. Yes, I mean the type of world depicted in the opening credits of the movie, The Matrix – walls of mostly indecipherable information streaming in from all directions floating around in the air. This may seem like an extreme analogy to some, after all, it is just a movie, right? However, with the advent of housing data in “the cloud”, data actually is all around us. Data is constantly appearing, streaming, calculating and disappearing based on each one of our actions and interactions. So how do we differentiate between taking the blue pill, waking up with real data in our hands, and the red pill, traveling down the damaged data Matrix-like rabbit hole of duplicate and partial information?

The annual company report. The weekly project management meeting. The current sales forecast.

Important decisions are made every day based on the content of such reports and documents. But where does the information that supports this content come from? How is it defined? Who says it’s appropriate to use and for what purpose?

With “March Madness” in full effect, you can find NCAA Basketball brackets being passed around and possibly wagered upon. This has become quite normal in recent years as one of sports’ biggest moneymakers has begun. However, this comes with a hefty price for employers. Until the national champion is crowned April 6, millions of employees will spend work hours completing brackets, checking scores and possibly watching games, thus hindering employee productivity.

Over the last couple of weeks, the perception of the color of a certain dress has reminded us of the importance of a single version of the truth. The discussion began with a photo on Tumblr and spread out across social media. The hashtag #TheDress was the top trending topic on Twitter that weekend. The topic has garnered more than 6 million views on Buzzfeed. And, according to figures Tumblr released after the weekend, the original post had been viewed 73 million times.

Here is a suggested methodology for Master Data Management (MDM) strategy and data governance initiatives, including phases such as

Figure 1. Suggested MDM Strategy Methodology