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Data Science – Harnessing Advanced Analytics for your Business Intelligence Digital Transformation

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BLOG BY: Susan Van Riper, GNet Group

No matter what business you are in, Data Science will be a fundamental component in your digital transformation efforts. By looking at patterns in data and implementing workflows, processes, and software that can automatically and reliably turn that data into actionable insight. Data Science can give your business an advantage that is difficult or even impossible for competitors to match.

No matter which path you choose to get started with Data Science, the important point is to get started!

Here are some steps to get you started toward a data-driven culture:

  1. Find a Data Scientist. But, who is the ideal Data Scientist? The ideal Data Scientist has a diverse set of traits and skills:
    1. Statistical analysis and machine learning
    2. Statistical and mathematical tools
    3. Programming and database
    4. Data modeling, warehouse, and unstructured data
    5. Solution deployment architecture
    6. Business domain knowledge
    7. Visualization
    8. Storytelling

Where do you find this Data Scientist? The truth is that finding one person that possesses all these skills is very rare – and if you do find that unicorn – you probably will be outbid for their services. Instead, engaging with or developing a team that possesses these traits and skills is much more attainable. But, building a Data Science team is expensive. An alternative is to hire an external Data Science service; a team of experts that you can call on when you need to.

  • Assess your data’s suitability for Data Science model development. Once you have a Data Scientist on board or Data Science team engaged, you will need to determine whether you have enough data, the right type of data, or access to appropriate 3rd party data that can provide actionable insights. If you don’t have the data, develop a road map to acquire the data.
  • Prioritize Data Science activity. In the beginning, you can start with the low hanging fruit that will give you the biggest bang for the buck, so to speak. Then move on to more complex analyses and models.
  • Deploy and automate agile Data Science solutions. Operationalize your data science models to a production environment to produce actionable insights in an automated way. Add value to your Business Intelligence by incorporating predictive analyses into existing reports and dashboards.
  • Celebrate your success and iterate. Once you have a successful Data Science solution, you will need to update your models with new data continually to ensure the model continues to remain reliable and valid. And, don’t rest on your laurels. Continuously challenge the status quo. Make your data a key asset and strive to move towards a data-driven culture in all aspects of your business.
 
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