The Data Science Business Lifecycle

Using Data Science is talked about in most businesses, but actually practised well in very few.  There is far more to success than hiring a skilled Data Scientist.  Both technical and commercial stakeholders need to work in harmony with the right underlying data foundations to drive real business value. 

Data Engineering = Data Operationalisation    

The Data Engineering function within the cycle extracts data from operational systems, develop pipelines for movement of data ensuring accuracy and reliability at scale.  Without this process working effectively, it will be impossible for even the best Data Science professional to be successful.

Data tasks falling under the Data Engineering remit include:

• Data collection
• Data management
• Data analysis
• Data visualisation

Data Engineering teams require different skillsets, to include :

• ETL/Data extract processes
• Data API’s
• Data warehousing
• Data modeling

Data Science = Data Optimisation

The data science specific section within the business wide cycle refines value and discovers insight informing business strategy. The overarching role of this section of the data science business lifecycle performs data optimisation.

Data tasks falling under the Data Science remit include:

• Data stewardship
• Data management

The core skillset of the Data Scientist team incudes:

• Machine learning
• Feature Engineering
• Storytelling
• Data visualisation

Business Stakeholder = Data Monetisation

The business stakeholder role within the data science business lifecycle defines strategy guided by the industry and market environment, augmented by data insight. The overarching remit of business stakeholders role within the data science business lifecycle achieves data monetisation.

Data tasks falling under the Business Stakeholder remit include:

• Data interpretation
• Data modeling

The core skillset of Business Stakeholders includes:

• Critical thinking
• Business intelligence
• Financial analysis
• Return on investment (ROI)
• Critical understanding of business value

Areas of Data Co-operation

The Data Science Business Lifecycle relies upon co-operation as the intersection of cycle stages.

• Data Engineering and Data Science come together to perform programming and analysis tasks.
• Data Science and Business Stakeholders work cooperatively on storyboarding, personas and variable and metrics testing.
• Business Stakeholders and Data Engineering work collaboratively on journey mapping, value streams, deployment and integrations.
• All those involved in the Data Science Business Lifecycle need to ensure accuracy and quality of data.

The Data Science Business Lifecycle and You

The Data Science Business Lifecycle evolves and revolves within each organisation one section feeding the next offering infinite potential. Wherever your business is in its development of this essential data foundation, investment in the completion of this cycle will ensure both ROI and potential for future growth delivered by the intelligence generated from your data.

Would you like clarity on your current data foundations and have visibility on the completeness of your data science business lifecycle?

We can support you in this and help you release the commercial value your data holds. Get in touch to arrange a chat.

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