The Rise of Decentralized Business Intelligence

Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop, and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of the large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.1

The Transformation of Business Intelligence

Business intelligence and analytics (BI/A) platform offerings have been undergoing a transformative shift in recent years. Historically, the initiatives have been closely controlled by IT groups for a number of reasons including control of access to sensitive company data and the relatively complex nature of the software applications, both in terms of size and cost, themselves.

This complexity (and perceived need for control) has also resulted in the standardization of reporting and analyses for as many people within organizations as possible- think one size fits all.

The shift that is occurring is from this centralized model to one that is more decentralized. Fueled in part by demand for business users and analysts, these BI/A deployments tend to be smaller and more specialized and, perhaps most importantly, improve access to data for the business users and analysts that work directly on those businesses. For smaller, more agile companies the transformation has been crucial because now organizations of all sizes can produce value quickly using the new generation of BI/A platforms. The systems are no longer the sole domain of IT super-users and data scientists. Instead, domain experts are on the front line.


Tableau: A Business Intelligence Stand Out

Gartner’s annual Magic Quadrant for Business Intelligence and Analytics Platforms is the gold standard that in-market companies point to when highlighting their success against the competition and a multi-year standout has been Tableau.2

Tableau helps people see and understand their data. It gives users the ability to move large amounts of data around a canvas quickly and organize it into meaningful pictures, visualizations. It enhances the natural human ability to see patterns and identify trends within seconds. This process can be repeated over and over until meaningful views within the data emerge. Things get exciting as users combine multiple views into interactive dashboards, highlight and filter data to show relationships, and string together specific insights into a guided story to explain the ‘why’ behind the data. The result (when used together with Tableau Server for collaboration and publication across the web) is the delivery of online, interactive, mobile-ready insight. Not just a dashboard, or a report and certainly not just data. Insight.

While most of the business intelligence and analytics applications make extensive use of visual analytics, Tableau’s offering easily ranks at the top of the class. For clarification, visual analysis is not a graphical depiction of data. Visual analytics is a means of exploring and understanding data. It supports and accelerates the analysis process itself. You can ask a question, get the answer, and ask follow-up questions—all within visual interfaces. A story unfolds from one visual summary to another. You maintain your train of thought without taking your eyes off the data. Later, you can retrace the story to rethink, explore further and share. In short, visual analytics allows you to go in any direction with your thoughts while leveraging your visual perceptual system to guide you down the most useful paths.3

Essential Features That  Visual Analytics Applications Should Have

The 7 Essential Elements of Visual Analytics Applications

Visual Exploration Querying, exploring and visualizing data are a single process.
Augmentation of Human Perception Visual thinking is encouraged and developed – the brain’s ability to process pictures far faster than text is leveraged.
Visual Expressiveness Visual displays have depth, flexibility, and multi-dimensional expressiveness.
Automatic Visualization Effective visualizations are automatically recommended.
Visual Perspective Shifting Shifting among alternative visualizations of any given data is effortless.
Visual Perspective Linking Multiple images are intimately linked, so a selection on one shows related, relevant data in the others.
Collaborative Visualization People can easily share and collaborate on useful information visualizations.

 

Conclusion:

As the market for business intelligence and analytics platforms continues to evolve we’ll be watching what happens next. For the foreseeable future, we expect that a primary goal will remain to close the gap between how quickly we think and how fast we can analyze and explore those thoughts and convert them into valuable action.

Many agencies are only equipped to provide one-dimensional reports from industry-standard solutions. Ask us about our High-Definition Reporting (HDR) tool that provides greater insights into the behaviors of our media targets, along with the ability to focus media investments on our clients’ best prospects.

Cited Works:

  1. Olivia Rud (2009). Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. Hoboken, N.J: Wiley & Sons.
  2. Rita L. Sallam, Bill Hostmann, Kurt Schlegel, Joao Tapadinhas, Josh Parenteau, Thomas W. Oestreich, Magic Quadrant for Business Intelligence and Analytics Platforms, February 23, 2015.
  3. Pat Hanrahan, Chris Stolte and Jock Mackinlay, Selecting a Visual Analytics Application, whitepaper.
Marc Johnston

About the Author

Marc Johnston is the Chief Financial Officer for DirectAvenue and can be reached at:
Website: www.directavenue.com
Phone: (760) 579-4240
E-Mail:  marc@directavenue.com

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