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April 2019

PowerBI ML – How to build Killer ML with PowerBI

By | AI & ML, Data Visualisation | No Comments

PowerBI ML: Unleashing Machine Learning in Microsoft PowerBI in 5 easy steps

AI and ML are key tools enabling modern businesses to unlock value, drive growth, deliver insights and outcompete the market.  Its unmatched ability to handle massive sets of data and identify patterns is transforming decision making at every level of organisations. Consequently Data and AI strategy is therefore rapidly evolving to explore the ways in which AI can be best utilised to enhance business operations. However, pragmatically harnessing AI for business needs has remained challenging. This is because the solutions offered typically incur significant resource overhead, are hard to understand and may fail to deliver actionable business outcomes. A gap has therefore emerged between BI and AI; a failure to bridge the insights we learn, with the intelligence to improve. The most recent release of Microsoft PowerBI ML features aims to eliminate that gap, by bringing in Artificial Intelligence (AI) and Machine Learning (ML) capabilities into the practical setting of self-service analytics.

PowerBI has established itself to be a vital tool in modern data analytics. The easy to use interface coupled with powerful reporting capabilities has made it the reporting platform of choice in delivering reliable business insights. The recent inclusion of ML & AI capabilities has significantly strengthened the tool, by combining easy interactivity with cutting-edge data analysis.

Overview

PowerBI ML (Machine Learning) is now possible using Dataflows, the simple ETL tool that empowers analysts to prepare data with low-or-no code. Automated Machine Learning (AutoML) is then built off the back of Dataflows, again leveraging the interactive approach of Power BI without compromising on quality of analysis.

5 Easy Steps

  1. In a Workspace hosted by Premium capacity, select ‘+Create’ in the top right corner, and select ‘Dataflows’
  2. Choose the data source you wish to run the model on:
PowerBI ML Choosing Data Source

PowerBI ML Choosing Data Source

  1. After loading the data, the familiar Power Query screen will appear. Perform any data transformations as required, and select save & close:
PowerBI ML Power Query

PowerBI ML Power Query

  1. The dataflow should now appear underneath Dataflows in the workspace. Select the dataflow, then select the brain icon, and select ‘Add a machine learning model’:
PowerBI ML Add Model

PowerBI ML Add Model

  1. Create the model by inputting the relevant information. You will get the option to select the model type and inputs for the model:
PowerBI ML Select Model

PowerBI ML Select Model

After creating the model, you will need to train it. The training process samples your data, and splits it into Training and Testing data:

PowerBI ML Train Model

PowerBI ML Train Model

Once the model is finished training, it will appear under the Machine learning models tab in the Dataflow area of the Workspace, with a timestamp for when it was Last Trained. Following this you can then review the Model Validation report (a report which describes how well the model is likely to perform), by selecting ‘View performance report and apply model’.

Lastly, you can apply the model to the Dataflow by selecting ‘Apply model’ at the top of the validation report. This will then prompt a refresh for the Dataflow to preview the results of your model. Applying the model will create new entities (columns) in the Dataflow you created. Once the Dataflow refresh is completed, you can select the Preview option to view your results. Finally, to build reporting from the model, simply connect Power BI desktop to the Dataflow using the Dataflows connector to begin developing reporting on the results of your machine learning model.

Outcomes

With machine learning now integrated with PowerBI, users can upgrade from reporting on business performance to predicting it. From a business perspective, the addition of ML means that PowerBI reporting has gained an extra dimension. It can easily be incorporated into existing reporting and is capable of dramatically changing decision making. For the PowerBI ML user, no new skills are required, as ML leans heavily on the existing interface and user experience.

Common use cases where machine learning in PowerBI can be readily implemented include:

  • Improving your existing PowerBI CRM reporting by creating a general classification model to identify high and low value customers.
  • Boosting the value of your financial reporting by developing a forecasting model to help predict sales trends and downturns.
  • Enhancing your asset reporting by building a regression model to calculate the probability of asset failure or breakdown.
  • Refining your CRM reporting by constructing a binary prediction model to determine the likelihood of a customer leaving or staying.

If you want to know how machine learning can be implemented in your organisation, please contact us, and ask us about our AI services.

 

 

 

Data & AI Strategy metrics

By | Data & AI | No Comments

Why are Data & AI strategy metrics important? The beauty of “strategies” for some is that a strategy – unlike a tactic – often doesn’t come with any clear success / fail KPI’s. It allows a lot of wriggle room for ambiguous assessments of whether it worked or not. However any self-respecting Data & AI strategy should not allow this. After all, it is designed and executed in the name of improving the use of data and measurable outcomes within an organisation. A good Data & AI strategy should have measures to determine its success.

Data & AI Strategy metrics that matter

Commonly raised metrics are based around uptake and usage (software vendors are particularly fond of these). This seems based on the hope that the apparent usage of tools is inherently a good thing for a company that will somehow lead to – I don’t know – increased synergy?

Dilbert Utilising Synergy

Dilbert Utilising Synergy

Sometimes they are measured around data coverage by the EDW or project completion.  However, if I was to put my CEO hat on, I would want to know the answer to the question “how are all these Data & AI users improving my bottom line?”. After all, if the Data & AI tools are being heavily used, but only to manage the footy tipping competition, then I’m not seeing a great deal of ROI.

The metrics that matter are the Corporate metrics.

A good Data & AI Strategy should be implemented with a core goal of supporting the Corporate strategy, which will have some quantifiable metrics to align to. If not, a good Data & AI strategy isn’t going to help you much as your organisation has other problems to solve first!

In a simple case, imagine a key part of the strategy is to expand into a new region. The Data & AI strategy needs to support that by providing data & tools that supports that goal, enabling the team in the new region to expand – and should be measured against its ability to support the success of the Corporate strategy.

This is why at FTS Data & AI, our first step in defining a Data & AI Strategy for an organisation is to understand the Corporate strategy – and its associated metrics – so we can align your Data & AI strategy to it and create a business case to justify why you need to embark on a Data & AI strategy in the first place. The metrics are the foundation that prove that there is deliverable value to the business. This is why the Corporate Strategy sits at the top of our Strategy Framework:

Data & AI Strategy Framework

Data & AI Strategy Framework

We have extensive experience designing strategies that support your business. Contact us today to speak with one of our experts.