Choosing the Right Self-Service Analytics Platform for Your Organisation

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At Softelligence, our expertise covers the most important Business Intelligence and Analytics platforms like Tableau, Power BI and Qlik, so we can help you make the optimal decision to invest in a self-service analytics solution that best suits your organisation.

Let’s have a look at a side-by-side comparison of Microsoft Power BI and Tableau to meet the self-service analytics needs of medium to large-scale organisations that seek to make data-driven decisions.

Microsoft PowerBI versus Tableau: a side-by-side comparison | Softelligence

 

When life gives you lemons  data, make…
#DataDrivenCulture

 

 

 

 

 

 

Get a taste of Business Intelligence

The Financial Services industry leads early big data analytics adoption, with 76% of companies reporting current usage. According to the latest research, the most common initiatives strategic to business intelligence are ReportingDashboards, Advanced Visualization and End-user Self Service.

 

Why the need for Self-Service Analytics?

 

Data driven culture

Organizations generate and collect data each minute. The goal of enabling self-service analytics is to help executives and employees alike to make better decisions, take smarter actions and operate more efficiently.

With visualization techniques, people across all levels in your organization can dive deeper into data and use the insights for faster, more effective decisions.

We recognize that, to have an organisation driven by data, we must first help to instil a data culture within their organization. We do this through our Data & Analytics model.

This is founded upon the four key areas that combine to create a data culture of self-service: People, Data, Technology and Process. Our experience helps us understand the importance of each and how to address them individually and collectively to ensure the successful delivery of your data strategy.

 

Power BI vs. Tableau

 

Power BITableau
A sample PowerBI visualisation

A sample PowerBI visualisation

A sample Tableau visualisation

A sample Tableau visualisation

Power BI is recognized by Gartner and Forrester as a Leader in Analytics and Business Intelligence Platforms, and Enterprise BI Platforms.Tableau‘s capabilities offer visual data journeys for the individual analyst user as well as for teams and organizations at scale and embedded analytics.

Power BI Synergies:

embedded seamlessly in the Microsoft ecosystem

Tableau Synergies:

flexible deployment

Power BI End users:

technical/non-technical, executives,
general stakeholders

Tableau End users:

data analysts

Power BI Investment required:

low

Tableau Investment required:

high

Power BI Visualization:

custom visuals enabled by open visualization SDK,
easy to use

Tableau Visualization:

curated, sleek visualization

Power BI Data shaping and modelling:

native PowerBI Query Editor tool for data shaping,

strong modelling capabilities

Tableau Data shaping and modelling:

no tool for data shaping (done externally in Excel),
light modelling capabilities

Power BI Ability to scale:

sizable developer community

Tableau Ability to scale:

niche community

Power BI Infrastructure:

SaaS

Tableau Infrastructure:

any (separate server costs)

 

Power BI: Key Strengths

 

  • Integral to the Microsoft technology stack i.e. Office 365
  • Able to connect to external data sources
  • Great functionality at a good cost
  • Works best for basic dashboards and reports
 

Tableau: Key Strengths 

  • Excellent for telling advanced and interactive stories with data
  • High flexibility in deployment
  • Works in the cloud and on-premise
Power BI: Key Weaknesses

 

  • Lack of flexibility in deployment in an on-premise scenario
  • Some features are still catching up to Tableau’s offering
Tableau: Key Weaknesses

  • Expensive
  • Will likely come with additional data warehouse configuration requests, adding effort and budget complexities

 

A cost-efficient alternative to Tableau

 

Gartner study

 

Vision: ”Microsoft is furthest to the right on the Completeness of Vision axis and has also continued to execute on its roadmap with frequent (monthly) product releases. Microsoft was relatively early to introduce search-based queries with Power BI Q&A, and has recently introduced Quick Insights as a basic form of smart data discovery. Microsoft continues to integrate its machine-learning capabilities as part of a complete solution, the Cortana Intelligence Suite. This vendor has also moved a step closer to linking insights to actions, with the recent integration of PowerBI with Microsoft Flow and within its business application, Microsoft Dynamics” (Gartner.com)

Through Power BI, the data visualization platform, Microsoft has been an analytics and BI leader for the past 12 consecutive years, as illustrated in the most recent Gartner Magic Quadrant.

 

 

 

Choosing the Right Self-Service Analytics Platform for Your Organisation

Power BI is ideally suited for the need to democratize the analytics function, while Tableau is built for data analytics and specialized users rather than the general audience. Additionally, the natural integration of Power BI with the rest of the Microsoft ecosystem with other compatible and notable residents like Azure, SQL, Office 365 and Excel, is a key benefit in favour of the Microsoft solution.

 

 

To help you make a choice, we invite you to reflect on these items:

 

What’s your company revenue?

If your organisation’s revenue excelled $20 mln. then you can consider Tableau. For less, Power BI could better serve your needs.

What’s your budget for implementation?

If it’s less than $50K, then it might make more sense probably to opt for Power BI.

Who are the end-users?

If they’re Analysts, then Tableau is a nice fit. For the general audience with casual reporting needs, we recommend Power BI. However, ad-hoc explorations do work better in Tableau.

 

What system integrations will be required?

Going beyond the Microsoft environment? Points for Tableau. Datasets over 10GB? The larger the dataset, the more points for Tableau again.

 

Tableau is the obvious choice for the professional analyst. Nevertheless, for self-service analytics, Power BI covers most of the capabilities a casual user will need to work with data. For generating reports and setting up dashboards, Power BI will do the trick at a much lower cost than Tableau.

 

A framework for assessing Data Maturity

Regardless of the chosen technology stack, it is important to plan and include a clear roadmap for data and analytics in your organisation’s cross-functional business strategy.

A framework for assessing Data Maturity

 

Please find the framework for assessing Data Maturity transcript below:

Where are you on the path to data maturity?

 

Level 4 Data-Driven

Goal: Maximize scale & minimize cost

 

  • Embed data into all business processes
  • No decision is taken without foundational data
  • Data drives machine learning
  • Automated decision making
  • Decisions enabled at all levels of enterprise

Level 3 Data CENTRIC

Goal: Data-based decisions

 

  • Data is operationalized and is used to drive key executive decisions optimized through predictive analysis
  • Ability to drive business decisions from intuitive data analysis
  • Data is democratized and everyone has access to ad hoc data experiments in visualization tools

 

Level 2 Data Specialist

Goal: Track KPIs using a BI platform

 

  • Standardized reporting on an organization-wide reporting platform
  • Data accessed centrally and distributed in a controlled manner without duplications
  • Users are specialized business people (core data experts)

Level 1 Data Conscious

Goal: Standardized reporting

 

  • Manually compile non-standardized reports from different systems led by IT

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