How to Develop a Data Integration Master Test Plan

In part one of this 3-part series, we covered why assessing risks early and often is key, the best practices for addressing risks, and best practices for common risk mitigation. Part two covered examples of quality risks for integration projects and best practices for tackling them. In the final article of this series, we’ll show you how to develop a strong data integration master test plan.

Although Agile testing tends to deprioritize test planning, teams working on data integration projects would be remiss to overlook the long-standing motives and rationale for a project-wide, data integration master test plan (MTP).

A “Data Integration****Master Test Plan”_ (MTP) represents a plan of action and processes designed to accomplish quality assurance from the beginning to the end of a data integration development lifecycle. The test plan should describe all planned quality assurance for each SDLC phase and how QA will be managed across all levels of testing (ex., unit, component, integration, system testing, etc.). The MTP provides a project-wide, high-level view of the quality assurance policies (often based on IEEE Standard 829)._

Such a plan may be developed using the data project documentation

• Business and technical requirements
• Data dictionaries and catalogs
• Data models for source and target schemas
• Data mappings
• ETL and BI/analytics application specifications

It’s essential to purge the data integration target data of the most severe and disruptive bugs. The sooner data quality/testing objectives are defined, the better your chances of exposing issues early when they’re easier, faster, and less costly to fix.

Why Develop a Master Test Plan?

Give your developers a standard test plan document that lays out a logical sequence of actions to take when performing integration tests. Doing so keeps testing consistent across the project and allows project managers to allocate the right resources to begin the integration testing process.

A data integration MTP should describe the testing strategy/approach for the entire data integration and project lifecycle. The MTP will help the project team plan and carry out all test activities, evaluate the quality of test activities, and manage those test activities to successful completion.

#data mapping #quality assuarance #data analysis

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Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

AI and BI Projects Get Bogged Down With Data Preparation Tasks

IBM is reporting that data quality challenges are a top reason why organizations are reassessing (or ending) artificial-intelligence (AI) and business intelligence (BI) projects.

Arvind Krishna, IBM’s senior vice president of cloud and cognitive software, stated in a recent interview with the Wall Street Journal, “about 80% of the work with an AI project is collecting and preparing data. Some companies are not prepared for the cost and work associated with that going in. And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.” [1]

Many businesses are not prepared for the cost and effort of data preparation (DP) when starting AI and BI projects. To compound matters, hundreds of data and record types and billions of records are often involved in a project’s DP effort.

However, data analytics projects are increasingly imperative to organizational success in the digital economy, hence the need for DP solutions.

#big data #data integration #data governance #data validation #data warehouse testing #data integrations

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management