Ulrika Jägare

Big data was definitely the thing just a couple of years ago, but now there's much more of a buzz around the idea of data value — more specifically, how analysis can turn data into value. The following information examines some of the trends related to utilizing data to capture new value. Data monetization One trend in data that has taken hold is monetization.

For your data science investment to succeed, the data science strategy you adopt should include well-thought-out strategies for managing the fundamental change that data science solutions impose on an organization. One effective and efficient way to tackle these data science challenges is by using data-driven change management techniques to drive the transformation itself — in other words, drive the change by “practicing what you preach.

So, what does artificial intelligence (AI) ethics actually refer to and which areas are important to address to generate trust around your data and algorithms? Well, there are many aspects to this concept, but there are five cornerstones to rely on when it comes to the ethics of artificial intelligence: Unbiased data, teams, and algorithms.

Although you must focus on your data science strategy objectives in order to succeed with them, it doesn’t hurt to also learn from others' mistakes. Here, you find a list of ten data science challenges that many companies tackle in the wrong way. Each argument not only describes what you should aim to avoid when it comes to data science, but also points you in the direction of the right approach to address the situation.

In the past couple of years, an avalanche of different data science careers and roles have overwhelmed the market, and for someone who has little or no experience in the field, it’s hard to get a general understanding of how these roles differ and which core skills are actually required. The fact is that these different data science careers and roles are often given different titles, but tend to refer to the same or similar jobs — admittedly, sometimes with overlapping responsibilities.

What is a CDO? CDO stands for chief data officer. The CDO is a title that describes someone in an organization who oversees the overall data science strategy from conception to execution. The chief data officer is responsible for determining how data will be collected, processed, analyzed, and used as part of the overall business strategy.

After your company’s objectives have become clearer, your CDO, as part of an overall data science strategy, needs to create a business-driven data strategy fleshed out with a significant level of detail. In addition, that person needs to define the scope of the desired data-driven culture and mindset for your company and move to drive that culture forward.

At the core of your data science strategy is data quality. If you are hoping to glean useful insights from your data, it needs to be of high quality. Keep reading to discover how you can assess and improve your data quality to ensure success for your data science strategy. Assessing data quality for data science Another fundamental part of data understanding involves gaining a detailed view of the quality of the data as soon as possible.