Extract, transform, and load (ETL) is the process of integrating data from multiple, typically disparate, sources and bringing them together into one central location. It is a key component to businesses successfully making use of data in a data warehouse.
Sure, the process itself is fairly straightforward, and when done right, ETL prepares an organization for powerful business intelligence initiatives. However, a lot goes into a successful ETL process. Let’s discuss the three steps involved and why data management practices are an essential foundation to carrying ETL out properly.
In schoolyard terms, data migrations are the equivalent of the old “Telephone” game that you may have played as a kid. You get a line of people together, and the first person in the line whispers a sentence to the second person – “The quick brown fox jumped over the lazy dogs.” The second person then whispers this phrase to the next person, and so on, until they get to the end of the line. At that point, the last person says what the sentence is – in this case, “The slick clown’s socks slumped over the crazy bogs.” As you can see, the end result may be similar to the start, but it’s definitely not the same!
A single customer view is a consolidated, consistent, and holistic representation of the data a business possesses about each of its individual customers. It’s often discussed as a marketing tool, frequently in the context of retail customers or consumers. Yet having a robust single customer view has value to most medium or large businesses – those whose customer base is too large for any single person to know and understand. And it has value beyond the marketing department...
Bright and early on a Thursday morning, hundreds of data professionals convened for the 2017 Data Governance Financial Services (DGFS) conference. Hosted in Jersey City, New Jersey, DGFS brings together likeminded and passionate data professionals from across the country with a common mission: to share best practices and overcome challenges related to their data governance programs.
Have you ever met someone who made a great first impression, but the more you got to know them, the more flaws you noticed? Maybe they said one thing but did another. Or you expected them to show up at the designated place and time and they failed to show up. Or sometimes, they are just nothing like what you’d thought and you realized you made false assumptions. Well, sometimes your data can be the same way. At a first glance, it isn’t always immediately clear what issues may be lurking just past the surface within your data. That’s where data profiling comes in.
This past week I was lucky enough to attend Strata Data Conference. The conference allows big data's most influential business decision makers and strategists to gather in order to share experiences, thoughts, strategies, and products with the goal of positively impacting their business or technology. The event was held at the Javits Center in New York City. Placing the conference in the heart of NYC allows companies from Wall Street and Silicon Alley to attend with relative ease, ensuring all industries are tapping into the opportunity that Strata presents.
Data is at the heart of every organization, and data migration projects are important undertakings for many businesses as they strive to keep up with the pace of technological advancement. Data migrations to more updated systems underpin the success of many strategic initiatives. While 35 percent of organizations have a data migration project planned for this year, a staggering 80 percent of all data migrations fail!
Despite the fact that the importance of data is widely recognized among company executives, there is a gap between this recognition and the number of organizations that are leveraging data to empower business decisions. To close this gap, organizations are investing in data management practices to establish trust and control of their data.
We've used this analogy many times here at Experian Data Quality, but that's only because it makes a lot of sense when referencing data standardization. What analogy am I talking about? The one where we discuss how a robust data management strategy relies on a methodical, step-by-step approach—much like how you'd approach building a house.
Data quality can be boring. Yes, I said it. And this comes from someone who has worked in the data quality space for more than 20 years. When I’m at a social gathering, I dread the inevitable “so what do you do?” question. My short answer is usually “boring computer stuff.” Heaven forbid they try to dig deeper! Then it becomes this awkward explanation about reducing the amount of junk mail they get or some pseudo-relatable data activity, and then watching their eyes begin to glaze over. At the end of the day, I use my tried-and-tested conversation changer, “enough about me, what about you?” I double majored in Computer Science and Psychology, and while Computer Science ultimately provided the foundation of my career, I’ve found that the psychology background is often more valuable for dealing with people and sticky situations.
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