The term Data Visualization loosely refers to the techniques used to communicate data or information by creating visual objects that are contained in graphics. The end goal is to communicate information clearly and efficiently to users via the information graphics selected, such as tables and charts. In his 1983 book “The Visual Display of Quantitative Information”, Professor Edward Tufte defines ‘graphical displays’ and principles for effective graphical display in the following passage: “Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency. Graphical displays should: Continue reading this post>>
One of the most challenging tasks when planning a new business intelligence project is the selection of the right tools to achieve the best possible return on investment. You will have many decisions to make depending on your company’s needs. This poses many questions such as: Will you need new servers? Will you need to host it in the cloud? Will ETL (Extract-Transform-Load) tools be needed to manipulate data or to combine multiple data sources? Will you need cube technology (usually dubbed OLAP)? What type of reporting tool will you need? All of these questions need to be answered carefully as they affect each other on your way forward.
A criteria-based approach should be used in selecting each software. This approach in evaluating software provides you with a quantitative measurement of quality before you commit to a specific tool. When evaluating business intelligence reporting and analytics software, the following 5 criteria are your top priority, but should not be the only criteria used: flexibility, security, learnability, mobility, and evolveability. Let’s take a deeper look into each of these areas.
Year after year the hype surrounding data analytics becomes louder. Thought leaders and research organizations have sung the praises of analytics as a means for generating much-needed insight into business operations, and companies that have embraced analytics have been able to translate insight to better operational productivity and faster, more accurate decision-making. In a competitive business environment where your competition is just as hungry as you are to reach and secure new customers and where business leaders need to make accurate, fact-based decisions about the company’s future, analytics can be the game-changer that makes the difference between success and failure. Here’s how:
The beauty of analytics is that it can serve as a guideline for transforming sub-par business performance to one that is efficient and profitable. Whereas reports provide a historical view of what transpired, analytic output is forward-looking, and plays an integral role in helping executives plan for the future.
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While it might seem like every company on earth is using business intelligence tools to glean insight from their corporate data, surveys say that nearly 10% of companies do not yet have BI in place. Even though 91% of companies may have it deployed somewhere in their organization, anecdotally BI vendors like to trot out the statistic that only 20% of potential users have access to business intelligence. The more companies we talk to, the more this seems true.
If you run a company or a department that doesn’t have access to BI tools, you might wonder how you can use them. Boris Evelson from Forrester Research compiled a list of analysis types that may apply to your situation:
- Historical (what happened)
- Operational (what is happening now)
- Analytical (why did it happen)
- Predictive (what might happen)
- Prescriptive (what should I do about it)
- Exploratory (what’s out there that I don’t know about)
When you’re first starting out with BI, you’ll likely be most interested in historical and operational analytics, though we often work with finance teams who want to dive right into predictive analytics. Let’s look at a few practical BI use cases in various departments of an organization.
Finance Department: Historical, Operational, Analytical, and Predictive Analysis
Financial Transparency – Architecting Success
Recorded Date: December 10, 2013
Duration: 45 min.
Presenters: Dwight deVera, arcplan Senior VP of Solutions Delivery; Jeff Lovett, Teradata VP, Finance & Performance Management
About this webinar:
Too many finance organizations manage their data using people, processes, and systems that are separated from the rest of the organization. This walled-off ecosystem requests data from other areas of the company and produces its own analytics often with different definitions of the same metric e.g. (Revenue, Margin) that conflict with those of line managers. With the ever increasing pace of change in the business this siloed, duplicative approach to financial analytics cannot deliver a transparent, integrated view that serves both finance and operations. Defining a simple architecture optimizes data management and makes it easy to visualize joint opportunities across both organizations.
- Leveraging insight into financial results, drivers and KPIs to provide visualized, actionable views of financial performance
- Telling the integrated contextual story of a company’s operations through common views and analysis
- Ending reliance on averages for more accurate, behavioral measures of customer or product profitability
- Utilizing the next generation of analytical techniques to unearth trends and predict organizational performance