Data IQ
News

News

Articles

Articles

Reports

Reports

Market Insight

Market Insight

Conferences

Conferences

Awards

Awards

Member Workshops

Member Workshops

Leadership Events

Leadership Events

Recognition
DataIQ 100

DataIQ 100

Advisory Board

Advisory Board

New to DataIQ?
Knowledge
News

News

Articles

Articles

Reports

Reports

Market Insight

Market Insight

Events
Conferences

Conferences

Awards

Awards

Member Workshops

Member Workshops

Leadership Events

Leadership Events

Recognition
DataIQ 100

DataIQ 100

Advisory Board

Advisory Board

New to DataIQ?

You are viewing 1 of your 3 guest articles for this month.

You can access an unlimited number of articles for free by registering with DataIQ.

Register

Already registered? Login

Data platforms: Helping data scientists do what they do best

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?  

Linked InTwitterFacebook
Chef Chopping

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 retrieval and harmonisation

 

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:

 

  1. The extract phase retrieves information from numerous sources, breaking silos and bringing all data into a centralised location. Ideally data is extracted in its entirety, in its rawest form, directly from the source, and more or less as it is being generated. Through the use of connectors, data is retrieved from multiple sources including APIs, mail attachments, FTP, file storage, and data warehouses.
  2. The transformation stage cleans and harmonises complex and varied data sets, ensuring consistent formatting and naming conventions, removing duplicates, and sorting information into relevant predetermined categories. Powerful transformation engines can slice, dice and customise data to meet individual business needs creating a clean, harmonised data stack - a single source of truth for the entire company.
  3. Finally, the load stage delivers the harmonised data to a target destination, such as a business intelligence database, ready for analysis.

 

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.

 

 

The role of data platforms in analysis

 

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.

Linked InTwitterFacebook
Add New Comment
If you would like to comment please Login

Did you find this content useful?

Thank you for your input

Thank you for your feedback

Next read

Addressing the gender gap in STEM industries

Addressing the gender gap in STEM industries

Next event
DataIQ 100 - Day of Data

DataIQ 100 - Day of Data

Sign up for DataIQ

Access to all data news and articles
Access to DataIQ 100 profiles - the most influential people in data
Invitations to leading industry events
Everything delivered to your inbox

Next read

Lyndsay Weir – Keeping quiet as a female leader

Bhagya Reddy – Engineering inspiration in data

European AI labs link up to exploit synergies

You may also be interested in

Can small businesses and start-ups benefit from AI too?

Can small businesses and start-ups benefit from AI too?

Powering strategic development with AI and machine learning

Powering strategic development with AI and machine learning

Driving data marketplace for improved junction judgments

Driving data marketplace for improved junction judgments

Assess biased algorithms with an ethical matrix

Assess biased algorithms with an ethical matrix

Data IQ
Twitter
Linked In
DataIQ is a trading name of IQ Data Group Limited
5th Floor, 10 York Road, London SE1 7ND
Registered in England: 9900834
Tel: [+44] 203-829-1112
Copyright © IQ Data Group Limited 2019