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Organizations everywhere are looking to do more with their data assets, as well as better leverage open data and third party data sources for additional consumer insight. The good news is that there is no shortage of information available. The bad news is that wrangling and making sense of all that information can be very challenging. That is why we see 61 percent of U.S. companies stating inaccurate data is undermining their ability to provide an excellent customer experience. However, there is a new breed of data professionals trying to change all that. New talent is coming into organizations looking to unlock the power of data to transform business operations and better serve clients. Businesses everywhere are eager to bring on these data professionals; to the point it is creating a frenzy around data staffing. If you are looking to hire new data professionals, you are certainly not alone. According to a new Experian Data Quality report, Investing in Digital Transformation: This Year’s Most Sought-After Data Roles, businesses are hiring a mix of business- and regulation-focused data positions. Below is a chart showing the top roles being hired according to U.S. respondents and also c-level executives specifically: There are a few key roles I want to highlight. First, data analysts. They are the most sought-after data role by U.S. organizations, mainly because they are so versatile. They are individuals placed across departments to analyze data in such a way that it can be used for business intelligence. 57% percent of businesses spend a majority of their time analyzing data, and this role is key to ensuring they’re getting the right results. The most important role for c-level executives is the chief data officer (CDO). The CDO has seen a lot of hype in the past few years and at this point we are seeing more general adoption of the role. This individual is responsible for developing and implementing an information strategy, which includes disciplines like data security, governance, quality, and management. They also will oversee a team of data professionals who bridge the gap between the business and IT. We have more research on this role in particular coming out next month, but they will often shape cross-functional data organizations and how well businesses can achieve the data insight they desire. In general, with all of these roles, talent shortages are a problem. Relative to demand, very few experienced individuals exist on the market. This means organizations need to come up with creative ways to attract and maintain this talent to keep up with the changing business landscape. For more information on these and other data roles, download a copy of our new report: Investing in digital transformation: This year’s most sought-after data roles. Download the complete "The Year's Most Sought-After Data Roles" report.

The real-time economy is all around us. With the swipe of a finger, we can order a car, find a babysitter or make a mortgage payment. At the heart of this revolution is the smartphone — a whopping 97 percent of Americans between 18 and 44 own one. This makes the smartphone the single most ubiquitous piece of consumer technology in the United States today. But for all the sectors it has transformed, consumer credit — and the lending experience overall — sadly hasn’t been one of them. That changes today, with the launch of Experian’s Text for CreditTM. In the past, obtaining credit was a tedious and often frustrating experience. Let’s use buying a car as an example. After test-driving a few makes and models, you’ve finally settled on the perfect car — an SUV, perhaps. You head to the dealership and then fill out page after page of personal information to prove your creditworthiness. Finally, after what can sometimes be hours, you’re (hopefully) approved. Not only does this take up valuable time, it also makes it less likely you’ll shop around for the best deal. On top of this, you’re sharing sensitive personal financial details with a complete stranger and leaving a paper trail. Text for Credit solves for these issues. And it’s as simple as it sounds: When seeking credit, you text a keyword such as “CREDIT” to a number supplied by a business, the same way some retailers now allow consumers to “text to buy.” Within seconds, you’ll receive a text message response that takes you to a hosted website where you can review credit offers, apply and receive an instant decision. In most cases, you’ll be recognized by your device’s credentials, which lets you avoid filling out a lengthy credit application. If approved, you’ll have real-time access to the credit via a barcode or account details sent to your device. To develop Text for Credit, we thought about consumers’ biggest concerns when applying for credit. In a July 2017 Experian survey, consumers most frequently cited privacy as a top concern. This was followed closely by the time it takes to apply. Lengthy application processes are such a challenge, in fact, that one in 10 consumers surveyed said they have simply walked away from a purchase because the application process was taking too long. Perhaps more frustrating, roughly the same number have given up because the person in front of them was going through a lengthy credit application process. Text for Credit addresses these concerns by using sophisticated mobile security techniques to verify identity and provide an almost instantaneous credit decision, alleviating potentially dirty looks from your fellow shoppers in line. Obtaining credit should never be done impulsively or recklessly, but it shouldn’t be needlessly complicated, either. Smartphones and the real-time economy are making countless facets of our lives more efficient and allowing us to dedicate our precious time to pursuits that matter to us. Text for Credit harnesses the power of mobile and real time to make stress-free credit a reality.

Believe it or not, my personal journey as a woman in data science started with physics. I was always very curious by nature and tried to understand what happens around me. I studied for both a master’s in physics in Spain and a Ph.D. in astrophysics in the Netherlands before making my shift from academia to industry (first in a Big Four consultancy and later in Experian). Gradually, I realized that I liked the academic side of working with data and applying the scientific method to solving problems, but I wanted to do something faster-paced that had more tangible impact. So, before finishing my Ph.D., I joined a data boot camp to further develop my skills, and after defending my thesis I transitioned to data science. Now I’m a full-fledged data scientist at Experian DataLabs. The world is at a very interesting time in terms of technology and innovation, and STEM fields are only going to continue growing. As a data scientist myself, I may be biased, but I think the future of this field is particularly interesting. I can see data being applied in such a variety of ways – from self-driving cars to early medical diagnoses and beyond. In fact, I don’t see the momentum slowing down any time soon, which means that data scientists will continue to be in high demand. I want to do something about the disproportionate amount of men to women in science, showing girls that STEM is for them, too. There’s no quick-fix solution, but I think it’s essential to start educating girls when they’re young about STEM – both at home and in school. Young girls should be encouraged to be curious, to try and fail! For me, data science isn’t about getting it right the first time; it’s about the path of discovery and innovation along the way. The sooner and the longer that girls are encouraged to explore and play with less-conventional toys, like computer games, construction toys or logic puzzles, the likelier they may be to choose careers based on what they personally enjoy doing and not what society expects them to do. Gender stereotypes can be really constraining, especially for children. So, what can a diverse workforce offer that a narrow one can’t? The answer is easy: different approaches, different views and different solutions. With more women in fields like data science, everyone benefits. No one should have to automatically rule themselves out of a career path based on gender.