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If you are running a business which requires the service of data analytics, then a Business Intelligence (BI) tool is an inevitable one. In this case, there is a tough competition between the BI tool developers, resulting in the availability of numerous tools in the market. Here, let us cut short the list, and focus on the two main tools – Tableau and Microsoft’s Power BI. The main lists consist of these two and it’s hard to eliminate one from it. The major reason for this is the Tableau’s outstanding ability in execution and the completion of the vision of Microsoft’s Power BI. The above tools have their own advantages and disadvantages compared with the other competitors.

A thorough study is a real need in order to choose a BI tool for your business. Considering the advantages of a specific tool over others and choosing it for the business may not do good. The main factors that have to be taken care of are the nature of the business, it’s size, needs of the workforce and the customers it deals with. Therefore, do a complete evaluation of the BI tool so that it can provide the best solution for your organisation. Here is an example,

A client recently approached Walkerscott to analyze near real-time IoT data that they had begun to capture from a few test sensors placed in strategic locations across New Zealand. They wanted our help in evaluating if and how this data could be used in conjunction with their in-house enterprise data to provide insights that deliver tangible value to their top and bottom lines.

Having built dashboards using Power BI a few weeks earlier, we decided to use Power BI Desktop to do our analysis (You can download the latest version for free from here).  We had worked with the predecessors of Power BI in the past and am blown away by the rapid evolution of the product into one that can truly deliver self-service BI in a simple environment.

The data extraction and transformation layer is impressive in terms of breadth of functionality offered and ease-of-use. We were able to mash-up data from disparate data sources – flat files and databases – and very quickly model any relationships and hierarchies using the Query Editor within Power BI. The help that we received from the active Power BI community online to work out any chinks was useful as well.

Once we had the data in place, we began with an initial exploration of the data. During analysis, we tend to follow a fairly standard process of eyeballing the data for any trends and relationships first, then slice and drill-down the data to view certain cross-sections, carry out some basic regression and correlation analysis and validate any assumptions, followed by deep-dive analysis using other statistical techniques.

Though not the most intuitive, Power BI’s visualization and charting capabilities were commendable and easily allowed for what we required to quickly make sense of high-level patterns, and drill-down as well. We found the ability to search for custom visualizations in the marketplace particularly useful as it helped us to implement certain slicers and vizzes that were unavailable out-of-the-box in Power BI.

Next, we wished to do some deeper analysis but it was then we began to struggle – we realized there wasn’t any middle ground between the high-level exploration and in-depth R scripting for statistical analysis and forecasting (save for a few custom visualizations that allowed these. We particularly missed the ability to generate a scatterplot matrix for multivariate data analysis. Using the visualization available in Power BI for each combination of metrics is time-consuming and not scalable. We realized that we would have to directly dive into R scripting to achieve this! The trend line and forecasting capabilities in the Analytics pane are woefully inadequate as well.

If we were to put on our Product Manager lens, Power BI ticks most of the boxes in terms of the functionalities it provides to do data analysis. The emphasis on some new technologies like Q&A and Insights powered by Machine Learning under the hood is forward-thinking and exciting! But we were still underwhelmed by how unintuitive it was to move from basic to advanced analysis. On the other hand, we were pleasantly surprised by the ease with which we managed to pick up a new version of Tableau that weird does not have exposure to and quickly run a few correlations, set uptrend lines in a matrix of scatter plots, create time series forecasts, and then do some deeper analysis using R scripts – the flow seemed more natural to me.

Ultimately, we used a blend of analysis from both tools to demo our findings to the client, and this proved most effective in driving home the business use cases that the real-time sensor data would (and would not) support. The technology stack for the implementation of the project will have technical implications of data availability and concurrency among other things, and we’re well placed to make these trade-offs once we begin the design of the system. We are very excited about the imminent project as it gives us the opportunity to employ data science frameworks and cutting technology to deliver tangible business value to our client.

To put things simply, both the BI tools – Tableau and the Microsoft’s Power BI are developed with more powerful features. Tableau ‘said embedded advanced analytics is super advanced when compared to Power BI, making it good for analysis and content creation. It also plays well in syncing real-time dashboards with real-time data integration. The Microsoft’s Power BI is superior to Tableau in data source connectivity due to its expertise in the field of web service, Microsoft’s API and most importantly the advanced integration options. The Power BI’s infrastructure components are far good than the Tableau. Tableau has high excellence in offering interactive dashboards and tools while Microsoft’s Power BI tops in offering best infrastructure components.

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