Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue.
Data quality is top of mind for every data professional — and for good reason. _**_Bad data**costs companies valuable time, resources, and most of all, revenue. So why are so many of us struggling with trusting our data? Isn’t there a better way?
The data landscape is constantly evolving, creating new opportunities for richer insights at every turn. Data sources old and new mingle in the same data lakes and warehouses, and there are vendors to serve your every need, from helping you build better data catalogs to generating mouthwatering visualizations (leave it to the NYT to make mortgages look sexy).
Not surprisingly, one of the most common questions customers ask me is “_what data tools do you recommend?_”
More data means more insight into your business. At the same time, more data introduces a heightened risk of errors and uncertainty. It’s no wonder data leaders are scrambling to purchase solutions and build teams that both empower smarter decision making and manage data’s inherent complexities.
But I think it’s worth asking ourselves a slightly different question. Instead, consider: _**_“what is required for our organization to make the best use of — and trust — our data?”**
It’s a scary prospect to make decisions with data you can’t trust, and yet it’s an all-too-common practice of even the most competent and experienced data teams. Many teams first look to data quality as an anecdote for data health and reliability. We like to say “garbage in, garbage out.” It’s a true statement — but in today’s world, is that sufficient?
Businesses spend time, money, and resources buying solutions and building teams to manage all this infrastructure with the pipe(line) dream of one day being a well-oiled, data-driven machine — but data issues can occur at any stage of the pipeline, from ingestion to insights. And simple row counts, ad hoc scripts, and even standard data quality conventions at ingestion just won’t cut it.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
Building your models and analysis on solid foundations.Garbage in, garbage out. So goes the familiar phrase, born in the early days of Computer Science, pressing the importance of validating your inputs.
A closer look at data analytics for data scientists. With a changing landscape in the workforce, many people are either changing their careers or applying to different companies after being laid off.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.