Does Intelligent Testing Impact Your Bottom Line And is It Worth It?

The litmus test for efficacy of business data is intelligent testing for your business . It is a test that determines data validity, data clairvoyance and the presentation formats. These evidence based tests throw light on action points that can be harnessed to make impressive numbers on the profitability scorecard.

Factors necessitating a BI budget

No longer can businesses function in isolation. Strategic decision making needs to embrace both the internal and external scenarios of a business. Taking a departure from traditional recording and processing of business information, BI technologies scrutinize data and processes in a multi-dimensional format. The following circumstances warrant the need for business intelligence and its testing:

  • Past data needs to be analyzed in order to identify key patterns that may indicate customer preferences, buying patterns, gaps in market and untapped business development opportunities.
  • In the absence of big data analytics and testing tools, strategists are at the risk of making faulty decisions or decisions that are not based on information. This can have a negative impact on the bottom line.
  • Keeping the prescriptive analytical tools under constant check is crucial to design futuristic strategies like disaster management program or a business continuance plan, to mention a few, in order to fight uncertainties and contingencies.
  • Well researched and processed information is the stepping stone for innovation in business. Innovation holds the key to tackle competition and boost profits.

What is intelligence testing?

Intelligent testing is the sequential process of testing all the business intelligence techniques deployed by an organization. Intelligent testing enhances the performance of the BI initiatives and paves way for a BI environment in the project roadmap of businesses. It includes test planning, strategy formulation, design, execution, reporting and control. It can be broken down into the following sub steps:

  • Validation of data right from source and carrying out a source-destination match check
  • Conducting a reality check on the correctness of access channels
  • Subjecting hard data through a check for timeliness and consistency
  • Ensuring that all Pathwwway game sources to collect internal data are objective, since it is very easy for favouritism and bias to creep into the internal environment of a business

The concept of ETL testing

ETL testing is the methodical intelligent testing of data through the key stages of Extract, Transformation and Loading. How this testing is worthwhile for leveraging the bottom-line becomes evident on a simple analysis.

  • At the extract stage, data flow must be consistent. A business is bound to gather data from multifarious sources, each of which must be checked right at the top of the funnel.
  • The transformation stage is very crucial to business intelligence that justifies the entire BI spend and sets the tone for BIA (Business Impact Analysis).
  • Data loading is yet another stage in analytics wherein all relevant data flows into reservoirs called data warehouses. Storing irrelevant and stale data might prove detrimental to profits. Thus, intelligence testing fortifies the profit charts by testing data for its performance and scalability.
  • Report testing is the tool that checks the most productive output of BI, namely enterprise reports. Reports must be checked for their coherence, timeliness, conciseness, readability and inference. In order to make impactful decisions, the management must be equipped with reports that can be generated, sorted, filtered and customized within seconds.

Data warehouse testing

There has been a dispute on the interchanged usage of the terms ETL testing and against DW testing. However, both are two sides of the same coin in ensuring data accuracy and on influencing meaningful decision making. DW testing includes:

  • Validation of correctness and accuracy of data beyond the ETL stage.
  • Since the end point in ETL is the flow of data into a data warehouse, investing in DW testing becomes mandatory. The level to which automation and optimization in involved in DW testing, determines the capability of the BI system to generate the promised deliverables.
  • A typical DW testing architecture includes various test points like source data testing, target DW testing and the Data mart testing, leading to the formulation of a full-fledged and flawless business intelligence system.
  • Three dimensional cube analyses wherein data sets are analyzed in more than two dimensions using relevant parameters and BI measures.

The impact of intelligent testing on the bottom line

In order to facilitate incremental changes to the bottom line, the BI budget must be justified. This is possible only by creating a development environment that can absorb the benefits of BI.

  • By transforming the development environment into an integration environment and later, into a production environment, the transactional systems begin to generate revenue.
  • By delivering actionable information in the hands of strategic decision makers, the BI approach is tested to impact profitability in a big way.
  • Intelligent testing is an end-to-end process whereby the entire data flow in an organization is tested, right from collection, transformation till analysis and reporting. This way, the testing process ensures that every hard fact is properly checked and synthesized. This results in copious savings, both fixed and recurring.
  • By diverting intelligence insights into impending threats and inherent weaknesses, the tests process keeps the entire BI mechanisms on guard, against further onslaught from unforeseen changes in the market conditions, competitor entry or a disturbance in the intra-organizational front. Each of the aforesaid can cast a shadow on profits. This vicious cycle is effectively intervened by proper testing of BI tools.

Enterprises belonging to all sectors including of business can fruitfully adopt business intelligence and appropriate testing methodologies so that all data warehouses become profit centers. Informative decisions go a long way into accurate product positioning and capturing of larger market shares, making their impact on the bottom-line visible. Properly Pathwwway game tested BI tools can lead to unbiased assessment of the performances of various monetary and human resources of the organization. Constructive monitoring against benchmarks lead to better performances that promotes business health and revenues.