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Business Analytics Forum #BAF3
- 1. Business Analytics Forum 3 #BAF Sheffield 4th July 2019
- 2. Chris Booker – Sales & Marketing Director Business Analytics Forum Introductions
- 3. Introductions Chris Booker – Sales & Marketing Director Aims of the forum Networking Knowledge Sharing Community Building Brief Introductions Name Role / Organisation Interest
- 4. Simon Harrison 4th July 2019 Predictive Analytics Watson Studio Walkthrough
- 5. Watson Data Studio Overview of the Watson Data Studio environment Predictive Analytics, Dashboarding, Machine Learning, AI – as a learning opportunity and further development.
- 6. Watson Data Studio Exploration Phase Data Preparation • Typically the most time consuming stage • “Data Wrangling” – cleaning and shaping. Handling inconsistencies and omissions • User friendly Data Exploration • Initial Data visualisation to explore key patterns • Reviewing initial gut feelings Model Development • Iterative process, generally using a range of models to provide the strongest relationships and most robust outputs
- 7. Watson Data Studio Production Phase Model Implementation • Setting the baseline measures of the effectiveness of the model e.g. scoring using a random test dataset Model Deployment • Ensure the infrastructure can support • Can it scale to support growth Model Management • Tracking performance over time • Dealing with ‘Model Decay’
- 8. IBM Watson Data Studio • Watson Data Studio is a platform to enter into far more advanced analytics • The target user could be anyone from someone with a basic grounding in analytics and would like to learn new skills, through to highly complex features for the most capable data scientist • Key features relevant to this session are: • Import Data • Refine / Cleanse • Set data types and targets • Select and configure model • Run model and view findings
- 9. Step by step - Driver Analysis Here is a short step by step walkthrough of how to use the platform to analyse a customer loyalty dataset, and how to find patterns within the data. Using the ‘Modeller’ asset type
- 10. Step by step - Driver Analysis • Import Data • Simple process to browse for data, flat file or to connect to existing data sources
- 11. Step by step - Driver Analysis • Data once loaded can be profiled, quality checked and fully catalogued • There are a range of operations available to refine, reclassify and otherwise prepare the data for analysis as required
- 12. Step by step - Driver Analysis • The next thing we have to do is to set the data types and targets • Here we have set the Customer Lifetime Value as the target, to repeat the earlier example of what drives the lifetime value • We can also exclude fields we don’t want to distort the findings • For this example, we have also used the Select command to limit the data to ‘Smart Electronics’
- 13. Step by step - Driver Analysis • Select and configure model • A wide range of built in algorithms are selectable, just click and drag onto the canvas • Then a wide variety of settings options depending on which option is chosen
- 14. Step by step - Driver Analysis Run model and view findings:
- 15. Other Interesting Features – Jupyter notebooks Notebooks are embedded in Watson Studio:
- 16. Other Interesting Features – Machine Learning • Each project can have a range of assets introduced • Visual Recognition, Natural Language Classifier, Machine Learning • As well as SPSS modeler, dashboards • Deployable models
- 17. Simon Harrison Analytics Operations Director t: 0114 399 2820 m: 07949 763 848 e: simon.harrison@deeperthanblue.co.uk https://www.linkedin.com/in/simon-harrison-dtb/ Thank you www.deeperthanblue.co.uk
Editor's Notes
- 6 key stages Important point is the model management, over time the effectiveness of any model will decay, e.g. The met office uses a 30 year average for climate figures to smooth out anomalies, it updates the data every 10 years, the next one will be next year covering 1991-2020
- 6 key stages Important point is the model management, over time the effectiveness of any model will decay, e.g. The met office uses a 30 year average for climate figures to smooth out anomalies, it updates the data every 10 years, the next one will be next year covering 1991-2020
- Here we start looking step by step at how to analyse a customer loyalty dataset Starting with importing the data
- Who has a google home or amazon echo type device Looking at the output, we can see a high likelihood that our Gold loyalty status customers in suburban areas tend to buy smart electronics This is really useful information for a marketer
- For the open source fans, Jupyter notebooks are embedded in Watson studio, allowing the use of existing python code assets to be run within the studio environment This opens up a vast range of capabilities, more than we can cover here
- Within a project there are a great range of assets that can be called upon And within the last couple of weeks there is now an Auto AI capability, as the system improves all of the time
- If you have any further questions, please feel free to email me or follow or connect via LinkedIn I would love to have your feedback and use it to improve the content Thank you