Need More Insight? 

Use Data Smarter

Quick Contact

(e) contact@abaxdata.com.au

(m) +61 (0)408 755 639

abaX Data is a Business Intelligence consultancy that provides our clients with the tools, techniques and processes to make decisions based on data.
 
From inception to completion, we have the experience, tools and expertise necessary to deliver and support successful Business Intelligence solutions.

Why abaX ?

What is Business Intelligence?

The abaX Definition

the process of using information to gain a competitive advantage by making decisions and managing operations based on data

Today, Business Intelligence is a very broad field. Originally, the term was applied to a specific application of information management (that is, data warehousing). However, over time and the inevitable change in technology and business operations, the meaning of Business Intelligence has added many different disciplines to its definition and it is not uncommon to hear the term being used in association with techniques that include data discovery, visualisation, statistics, data mining and reporting. Even within the same organisation, department and people, the term can assume different meaning.

For abaX, the definition of Business Intelligence is both clear and redundant. Clear because we focus on delivering outcomes - adding value to your organisation through the better use of data. Redundant because we don’t care what it’s called, just the value it adds. What types of activity can this include?

  • Database Design
  • Historical Recording
  • Consolidation & Integration
  • Transformation
  • Representation & Reporting
  • Analytical Engines
  • Master Data Management
  • Key Performance Metrics
  • Advanced Analysis & Interactive Investigation
  • Visualisation
  • Geographical Analysis
  • Management by Exception
  • Data Mining & Predictive Analytics
  • Embedded Analytics
  • Streaming Analytics
content-sep

How did we get here?

Originally, the term “Business Intelligence” was targeted at technologies and practices that consolidated data from disparate operational systems into a consistent enterprise wide view of information (commonly called the “data warehouse”). The goal of the data warehouse was conceptually very simple ... it would serve as the information store for the entity and provide information that would be used in decision making (or at least was the source of information that support decision-making). In contrast, the operational systems would take care of the day to day running of the business (i.e. transaction processing) and the data warehouse would store historical information in such a way that it could be used to report and analyse business performance. In short the data warehouse would provide “a single source of truth” of information for entity decision making.

Of course at the time when this definition was most applicable, operational systems were much different than they are today and the cost of computing was considerably higher. The data warehouse was novel idea because it is replaced the transactional data store by combining data from different systems and enhancing it with information that may not have been recorded in an operational system. In addition to that, the data warehouse was designed to record historical context so that information which was changed in the source system (and therefore lost forever) would still be available in the data warehouse. Finally, the data warehouse was designed to deliver information for reporting and analysis workloads and not operational processing.

The common approach to software development (including data warehousing projects) was the waterfall development cycle. Under this approach, all requirements are defined upfront, then the project be can be planned, designed, built, tested and released. Unfortunately the time taken to implement a data warehouse using this methodology is notoriously long (an average of 2-3 years) and with business being conducted in an ever-changing world, it is not surprising that the requirements specified at the start of the project were redundant by the time the product was delivered. Further, changes in corporate structure often created a change in corporate priorities and the data warehouse failed to accommodate these under its original scope.

It is not surprising therefore to learn that many in data warehouse projects failed on the promise to deliver a single version of the truth within the projects iron triangle (time, cost, quality). In reality, users often supplemented the information obtained from the data warehouse (whether that information be direct or indirect) with their own data to analyse and monitor business performance.

However, the development of data warehouse methodology under this paradigm delivered several important disciplines to information management. These are all included as components of business intelligence and include;

  • Database Design
  • Historical Recording
  • Consolidation & Integration
  • Transformation
  • Representation & Reporting
  • Analytical Engines
  • Master Data Management

Of course, there have been enormous changes to the way that business operates in the last 30 years (since the traditional definition of business intelligence was coined). An underappreciated point to consider is that this change has also occurred from an information technology perspective. End users have considerably more computing power (actually, they now have it rather than operate dumb terminals), and interaction has become symbolic without the need for direct code. There are also a multitude of devices available. Today, a standard desktop can overpower what was under the domain of an entire department (IT & Processing) at a fraction of the cost.

These changes have impacted the way that information is used within an organisation. Today, it is not uncommon to consider the term “business intelligence” as a generic term that offers an entity a competitive advantage in the way that they use information (or their data). This includes disciplines such as;

  • Key Performance Metrics
  • Advanced Analysis & Interactive Investigation
  • Visualisation & Geographical Analysis
  • Management by Exception
  • Data Mining
  • Embedded Analytics
  • Streaming Analytics