There is no doubt that digital transformation is a need nowadays, and as ‘Gartner’ says: ‘Digital era requires data analysts on every profession, every process, every decision and every action’.
However, not every organization is facing this digital transformation from the same start line; and for that reason, it is very important to understand and assess internally where do we stand and what to do to reach the next step.
Also, and perhaps more importantly, let’s understand what different roles and functions within a data science team are required along the way to a successful Digital Transformation.
Data science focuses on predicting something, prescribing something, or in some cases explaining something, making it distinct from business intelligence (BI), which focuses on backward-looking factual reporting (describing something that happened).
Data science is also distinct from big data storage and processing technologies like Hadoop and Spark. Successful organizations coordinate all three areas (data science, business intelligence, and big data) to achieve maximum value. Defining a global framing called the ‘Data Science Maturity Model’ will help to reduce ambiguity and improve work efficiency.
The Data Science Maturity Model (DSMM) assesses how reliably and sustainably a data science team can deliver value for their organization, as this model is based on the concept of levels or stages that the organization traverse as it matures managing data.
Understanding where the organization is standing within this maturity model is crucial, so it will draft the strategy to move to the next stage.
The model consists of three levels of maturity and is split into seven stages that apply to any organization.
There is no turn-key solution to unlock the power of data science. As data science teams mature, they can drive increasing value throughout their organization, further reinforcing data science as an essential capability of any business.
What happened?
Ad-hoc reporting
- Start to define the key questions that analysis can help answer to bring value to the business
- Start to define the key questions that analysis can help answer to bring value to the business.
- Discuss best practices for data cleaning, preferred algorithms, and validation amongst the team.
- Review and investigate existing historical data to understand the context and identify drivers, gaps and correlations.
- Talk to stakeholders about what goes into research projects (e.g. data and other resources), what the results could be mean, and how they can use results to improve the business.
- Show value quickly: Data science can be expensive, and people want to know the investment is worth it.
Strategy to move forward
- Document your process in a shared location, like a wiki, so that it’s easily consulted by team members.
- Think about the types of metadata that are helpful to capture, such as documentation and commentary about how decisions were made.
- Share results with the whole organization so they can see the types of problems the team is capable of solving and start inquiring about new applications.
- Track how data science results are incorporated into the business. Is data science doing more BI-type work? Make sure the information you are providing is used effectively and that you can show the value your work brings.
Why did it happen?
- Defined and Controlled
- Each step of the analytical process is documented on the intranet as a set of best practices that each new member of the team must learn.
- Remove the risk that people don’t follow the standard process
- Data science is inherently exploratory, with many variations of features and models being discarded before settling on a final result. How can you preserve and expose this iterative process, so that it’s highly transparent to team members and consumers?
- Are there untapped data sources that could be used to deliver better insights?
Strategy to move forward:
- Look at causes of variation. When there are surprises in outcomes, could they have been prevented?
- Educate business users to the new environment and the new practices
What can happen? and Optimize Actions
Optimize and Automated
- The Data Science team has a process to identify and evaluate potential innovations quickly while minimizing the cost of exploring dead ends.
- Educate and improve processes beyond the data science organization.
Strategy to move forward:
- Partnership: data scientists are working to help improve the entire analytical lifecycle from data discovery to business action.
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For more insight on Digital Transformation like this, check out the Digital Acceleration Binge from DAC, featuring actionable advice and guidance from market visionary Bob Evans of Cloud Wars and others in a 3-hour “short form” free content program.
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