Nearly ten years ago, it was predicted in a Harvard Business Review article that the sexiest job in the 21st century would be that of a data scientist. The unique combination of skill sets required for this role (mathematics/statistics, computer science, domain knowledge and DevOps) underscored the scarcity of qualified candidates.  

Nearly a decade later with the omnipresence of big data and the proliferation of advanced degrees in data science springing up at colleges and universities around the world, do data scientists continue to enjoy their exclusive rock star status?

Data Wrangling Takes Away Valuable Time

A recent survey revealed that data scientists spend around 45% of their time wrangling with data and preparing it for analyses. According to Anaconda, this time spent reduces overall job satisfaction. ‘Data wrangling’ involves data loading, preprocessing, standardizing/structuring, cleaning, and consolidating.

While these tasks may sound somewhat interesting to the non-data geek, the reality is that data management is mundane and takes away valuable time from data scientists who are trained to apply their advanced knowledge to data analyses, model selection and training, testing and deployment. In essence, the success of these models developed by data scientists rests on the accuracy and predictive capabilities of the models to solve real business problems.

We Eat Our Own Dog Food (Sort Of)

Being data geeks ourselves, we, at idaciti, understand the challenges that data scientists face.  We are users as well as developers of our own product; it's a reliable part of our daily workflow. Given the general pain that data scientists face with data wrangling, we wanted to build a solution that would automate the tediousness out of this portion in the data science lifecycle.

Leveraging our XBRL-first framework, we built an application that efficiently executes data wrangling for the financial domain and provides the on-ramp for the data scientist to take the data to the next level. Our team has lived and breathed data wrangling for nearly two decades - now we have automated it. Our history ties in with the global XBRL movement to digitize, structure, standardize and clean financial information since its inception. Our well-oiled, data wrangling machine performs at scale, providing data science teams with loaded, processed, structured and cleaned data for them to do their own magic with the data.

Data Scientists Should be Rock Stars

Where do we fit into the whole data science value chain? idaciti’s XBRL-first framework loads financial and non-financial documents, digitizes them, parses and structures data embedded in the documents, and makes them available in a clean format for data visualization and analyses.  By doing so, we can already eliminate inefficiencies and give back valuable time to data scientists to do what they do best and are measured on. Whether you are looking to extract detailed financial data from the footnotes to financial statements or unstructured data about climate change or ESG, we have been there and done that. That is just part of our make-up.

A decade later, we contemplate whether the promise of ‘the sexy’ in the data scientist role has been fulfilled? We hope to answer that question by reducing monotony and enabling data scientists to focus on what matters most to them - building models that help businesses, societies and the global economy at large to navigate through the challenges we currently face.

Spend less time wrangling data and benefit from technology that is designed to make your data life easier. idaciti solutions support collaborative modern workflows, and contain useful metadata, easily accessible via API or web applications. Curious to see more? Book a demo today --->