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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Category Archives: Business Intelligence Guide


The ABCs of Master Data Management

Basics of Master Data Management 282x300 The ABCs of Master Data ManagementA:  What is it?

At a basic level, “master data” is simply data that is used by two or more applications or systems in an organization.  Common examples found in most companies include Customer data, Employee data, Product data, Asset data, and Location data. “Management” of master data is needed in order to ensure that master data is exactly the same everywhere it is accessed in the organization.

Fundamental reality:  Master data exists in all companies, whether or not it is called master data, and whether or not it is managed in an organized manner.

Master Data Management (MDM) is a plan for creating and maintaining consistent, accurate, and appropriate lists of master data.  This is more complicated than one might think, because:

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The ABCs of Data Quality

data quality 300x225 The ABCs of Data QualityA:  What is it?

Fundamentally, “data quality” refers to the reliability and value of data in terms of the purpose for which it is being used.  For some uses, data can be approximate, inconsistent, and even inaccurate, but still serve its purpose effectively.  For other uses, data must be absolutely pristine—clean, consistent, and perfectly formatted—in order to have value.  Every organization should identify the level(s) of data quality it actually needs, and commit to maintaining that level.

Data quality is a broad concept that breaks out into several key components.  Among the most important:

  1. Correctness:  How well does the data correlate with the reality it represents?
  2. Suitability:  Is the data appropriate for its potential uses?
  3. Consistency:  Are the same facts represented the same way throughout the data aggregate?
  4. Cleanness:  Is the data aggregate free of inaccurate or outdated data, duplicated data, etc.?

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Landscape: Data Warehousing and Business Analytics

Spotfire Landscape:  Data Warehousing and Business AnalyticsThe Terrain

This landscape has changed dramatically in recent years.  Not so long ago, IT investment was focused on building data warehouses to store large amounts of static data in a structure designed to facilitate reporting.  The reports were usually designed and delivered by BI workers inside the IT department.  But as organizations began to recognize the value of their warehoused data, demand grew for broader and more flexible access among business users.

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The ABCs of KPIs

j04305161 150x150 The ABCs of KPIsA:  What are they?

KPI stands for “Key Performance Indicator” and usually designates a metric providing specific information about actual performance in a mission-critical area.  Some companies use the term KSI (Key Success Indicator) to mean the same thing.

As a rule, a KPI should be:

1. Measurable
2. Clearly related to organizational goals
3. Carefully defined
4. Very important (“key”) to performance/success

Once KPIs have been identified, the relevant information has to be tracked and reported.  KPI information is often displayed in the form of a “scorecard,” which usually shows change over time, or on a “dashboard,” which usually gives a snapshot of performance at a specified time.  A dashboard may show KPIs in a variety of formats, and may display scorecards along with other types of reporting.

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The ABCs of Data Visualization

Spotfire Data Visualization 300x173 The ABCs of Data VisualizationA:  What is it?

The business value of data visualization more than proves the old adage:  A picture really is worth a thousand words.

Data visualization” is (basically) displaying the result of data operations in a format that communicates primarily through shapes, colors, and/or representational images.  Data visualizations almost always have labels to identify/explain the non-verbal content.

Data visualization was a minor factor in most businesses up until a few years ago–but that has changed dramatically.  Here’s why:

  1. There’s a lot more data to deal with.
  2. More people in the business need to see and understand data.
  3. Data is being used in more complex ways.
  4. Visualizations are much easier to produce.

There are two main formats for data visualization:  displays and presentations.  Displays provide information “at-a-glance,” usually as a dashboard or scorecard.  Most displays focus on metrics of some kind (such as Key Performance Indicators) and show high-level overviews.  They range from simple indicators (such as red-yellow-green “stoplights”) to interactive landscapes that allow users to see rolled-up numbers quickly and then drill into the data.

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11 Guiding Principles for a Successful Business Intelligence Implementation

work team1 300x199 11 Guiding Principles for a Successful Business Intelligence ImplementationBusiness intelligence is not a software solution. It’s a methodology and process that uses technology as a way to implement change. Here are 11 steps, or guiding principles, for a successful business intelligence implementation from Booz & Co.

  1. Drive Change From The Top Down and The Bottom Up

Like any new solution, business intelligence is only effective if people use it. For it to be completely adopted, it needs to be used not only by line managers but also by executives.

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Landscape: Social Media and Business Analytics

social media landscape 297x300 Landscape:  Social Media and Business AnalyticsThe Terrain

This landscape has two major features:  the wild space of “social media” (where almost anything can happen, and does) and the formal garden of “business analytics” (where everything must be carefully organized).  There are also two overlapping areas—one where analytical processes are applied to social media, and one where social processes are used for business analytics.

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6 Steps To an Agile Business Intelligence Implementation

work team 300x199 6 Steps To an Agile Business Intelligence ImplementationInterest in using the Agile Methodology for business intelligence development and implementation is increasing. Agile methods emphasize a strong partnership between end-users and developers that can increase user adoption and help limit scope to the most critical functionality. It also helps users benefit from BI solutions faster, in smaller pieces, rather than all at once when a single solution is delivered. Ken Collier, a senior consulting with Cutter Consortium, describes six steps to achieving a successful agile business intelligence implementation.

  1. Involve Users Early and Often

End users should be involved in the process from the very beginning and should stay engaged on a daily or weekly basis. This will help ensure that end-users get the functionality they need the way they need it.

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The ABCs of Enterprise Analytics

Enterprise Analytics The ABCs of Enterprise AnalyticsA:  What is it?

“Enterprise analytics” is a widely used term these days.  As often happens, though—it’s being used in different ways, by different groups, for different reasons.  Enterprise analytics can refer to any or all of these three concepts:

1. Access to analytics capability (so users throughout the enterprise can perform their own local analytics)
2. Access to enterprise-level analytics (so some users can see reports or dashboards that incorporate data from the whole enterprise)
3. Analytics platforms that can function at an enterprise level  (working with multiple data sources and formats)

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The ABCs of Desktop Data Mining

desktop computer 150x150 The ABCs of Desktop Data MiningWhat is it?

“Data mining” can be described as the process of finding patterns and relationships in data.  There are several different flavors of data mining, using different methods and pursuing different results.  For example, a data mining project might look for:

  • associations (interconnected events or information items)
  • sequential relationships (one event leading to another)
  • affinities (events that occur in clusters, information items that are frequently found together)

There are also two levels of approach to data mining.  “True” data mining is an IT-intensive process, using complex algorithms and very sophisticated procedures to discover deep and/or unexpected patterns in data.  At the business level, however, data mining is often viewed more generally, as a way of exploring data to answer questions and support analysis.

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