Restaurants don’t employ top chefs to wash and prepare the vegetables and hospitals don’t pay brain surgeons to triage patients, so why do so many businesses employ highly-skilled data scientists only to waste their time manually importing rows and rows of information into Excel spreadsheets?
Data scientists are highly sought-after, with demand for specialist data skills increasing over 230% (source: Royal Society) in the last five years. But while action must be taken to close the skills gap and encourage people into the profession, businesses can also make far better use of the data science resources they already have.
Data scientists currently spend over 40% of their time on mundane tasks such as gathering and cleaning data, according to Kaggle, instead of concentrating on the more skilled areas of analysis and delivering actionable insight, where they can add real value. With Glassdoor finding that the average base salary for data scientists in the UK exceeds £46,000, businesses must make better use of this precious talent and automated data platforms provide the answer.
Automated data integration technologies take the heavy lifting out of data collection, allowing massive volumes of structured and unstructured information to be cleaned and harmonised quickly and accurately, with minimum input needed from data scientists. In addition to saving time, effort and resource, automated data integration also limits the impact of human error. Data integration encompasses a range of processes, including ETL (extract transform load) which separates data preparation from analysis.
The three stages of ETL are relatively self-explanatory:
For those sceptical about leaving data preparation to technology, reassurance comes in the form of ETL testing, which checks the completeness and accuracy of data, ensuring it is retrieved in its entirety and transformed correctly, fitting into the right formats and categories. Even when time is allocated to testing, automated data integration is still far quicker than manual collection and cleaning processes.
Other data integration processes can be used alongside ETL to automate the data preparation. One is ELT (extract load transform), which is similar to ETL except it provides the option to explore raw data before transforming it. Another is data federation, which aggregates data from disparate sources into a virtual database. When used together these data transformation processes break down data siloes and allow clean data to flow through an organisation with minimal manual intervention from data scientists.
Data platforms aren’t just useful for automating the collection and preparation of data, they can be used to speed up and enhance analysis too. Data scientists spend vast amounts of time trawling through data to uncover patterns, often with no idea what they are looking for, but automated AI-powered data discovery technologies can automate this tedious task. Specialised techniques such as anomaly detection can be used to identify hidden trends and augment analysis with precise insight.
Predictive analytics and anomaly detection have two key benefits. First, they can be used to uncover current errors or future challenges, both internal or external, that might threaten success or prove costly to the business in other ways. Augmented analytics with data discovery and anomaly detection allows businesses to identify these threats and react quickly, taking whatever action is necessary to minimise impact.
Second, these technologies can be used proactively to uncover and optimise new opportunities. By delivering meaningful insight into developments in data, they avoid the blind spots that are inherent in manual analysis due to time constraints or human preconceptions. By automating analysis, businesses can fully understand what is helping or hindering success. They can generate recommendations to optimise opportunities to their own goals and KPIs, driving performance and efficiency and ultimately giving them an edge over their competitors.
Data scientists are a scarce and sought-after resource, so businesses shouldn’t waste their precious time and talents in manual, tedious data preparation and analysis tasks that could be effectively automated. Much like the kitchen hand that chops the carrots and the triage nurse that assesses the patients, data platforms can take on the routine or time consuming elements of data preparation and analysis, leaving data scientists to do what they do best and generate actionable insights to drive business success.
Alexander Igelsböck is CEO and co-founder of Adverity, a data intelligence platform enabling data-driven marketers to reduce complexity and deliver value by translating data into actionable insight.