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Unraveling the Mysteries of Data Warehousing and Business Intelligence on IBM i

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Vendors have so many different types of products to offer. How do you sort through it all?

 

It's easy to get confused when investigating the requirements and options for implementing a successful business intelligence (BI) initiative. There's an amazing array of product offerings from different vendors, each with its own unique mix of functionality, strengths, and weaknesses. If you are new to BI, it can be difficult to sort through these offerings and identify what is best for your organization.

 

You will likely hear conflicting messages from different "interested parties." The front-end tool vendor wants you to think that it's easy and that their tool is all you need to be successful with BI. The expert, speaking at COMMON or one of the other conferences, talks about best practices that include a data warehouse, data marts, metadata, and extract, transform, and load (ETL). And a few years ago, even IBM was confusing you by telling iSeries shops to implement BI on a pSeries box, even though you probably had no expertise in that platform. This was purely because some IBM executive had drawn up a matrix to specify that each workload belonged on an i, a p, or an x box. And BI just happened to end up on p. Thankfully, Big Blue eventually came to its senses on that idea and recognized that trying to foist a UNIX box onto die-hard i shops usually doesn't fly.

 

Just a few days ago, I heard that a "front-end" tool vendor was trying to convince one of my customers that they should throw away their data warehouse, and just implement this vendor's front-end (query/reporting) tool directly against their operational data. Because my customer has reaped the benefits of their data warehouse over the past several years and understands its value, they were not hoodwinked by this sales pitch and will keep their data warehouse.

 

With these confusing, sometimes contradictory messages, it's no wonder that the principles and best practices of a good BI implementation are not always well-understood. In the space of this article, we cannot explore all of the factors that you need to consider, but we'll try to cover the more important issues.

 

Of the many BI products available, which ones do you need, and why? The answer is, as usual "it depends." The correct approach (as with any project) is to determine what you are trying to achieve from a business perspective. This will be different from organization to organization, and for many, there will be several different distinct requirements. We can categorize the front-end BI tools into three broad categories, as shown in Figure 1.

 

092611JordanFig1

Figure 1: Front-end BI tools fall into three categories: reporting, analysis, and prediction. (Click images to enlarge.)

 

The most basic BI implementations simply focus on reporting what happened (last week, last month, etc). These are often static reports, produced on a scheduled basis. Many organizations successfully start their BI efforts at this level. Success, however, has its price: Not long after your business users get access to these useful reports, they'll start asking more difficult questions and want to analyze the data to understand why something happened. These questions are best answered by online analytical processing (OLAP) tools. Typically, these use a multi-dimensional data structure to provide fast drill down into the detail. There is a much higher business value here: Analysts can explore the data to understand trends and then use that information to make informed business decisions.

 

More advanced still are the "predictive analytics" applications that can use your historical data to predict what will happen in the future. Data-mining applications can tell you the answers to questions you never thought to ask, unlocking more of the value hidden in your data. Unfortunately, very few organizations achieve this level of sophistication. It's substantially more difficult to successfully implement these applications, partly because you probably won't have the skills in-house to use the tools (consultants are usually required), but also because the underlying data required to support them is more difficult to prepare and deliver. As you get further to the right (in the above figure), the need for clean, integrated, and well-organized data grows exponentially.

 

The only way to provide this data is by implementing a data warehouse and associated data marts. To understand why this is, let's explore some of the common issues that you'll encounter. Most of these are found in almost every organization, regardless of size.

Data Complexity

The data contained in an operational database can be quite difficult to interpret. There are a number of reasons for this:

 

  • Data (information) in an operational database is designed to be accessed by applications, by the software that it was created to support. Therefore, the design of the database is based on principles and best practices that support this. Unfortunately, these design principles are often quite unfriendly when we try to access the data for business intelligence purposes. The principle of third normal form (avoid redundancy of data) results in many tables in the database. Even the simplest reports could need to join a dozen tables to bring in all the required data.

 

  • There is usually no user manual describing the structure and meaning of the database. Again, this is because the database was not designed for human access.

 

  • In all but the very best database designs, you will encounter all sorts of inconsistencies. This is usually because the application has grown over time and has been developed by many different programmers. The same piece of data may have a quite different name and description in different tables. Redundant columns may have been re-used for a different purpose, and therefore have totally incorrect names and descriptions. Meaningful information may be embedded inside another column. And so on.

 

Disparate Data

Understanding and using data coming from just one application may be difficult enough, but that pales in comparison to the task of joining data across applications. If those applications reside on different systems and use different database types, it gets even harder. What are the odds that similar, related pieces of information are stored in the same format?

 

Similarly, you may have the same piece of data in multiple databases, with different identifiers. In one example I came across recently, a manufacturing company found it had 90 different "parts" files, most using different parts numbers, such that the same part in different files could not easily be cross-referenced. This is an extreme example of a very common scenario.

Data Quality

This is arguably one of the biggest issues; however, data quality is given little attention in many shops. While there are many reasons for this, probably the one major underlying factor is the belief that "I don't have any data quality issues." While it is possible this is true, based on the experiences of every business intelligence consultant and data analyst, it is highly unlikely. Operational applications (e.g., ERP systems) generate enormous amounts of data in many tables and columns. When bugs (associated with data issues) are encountered in the daily use of the application, the problem gets attention and is fixed. However, there are many instances when data is generated by a process and not touched again by the application; or if touched, the error is such that it does not result in a recognizable problem. These data errors do not always get discovered and corrected.

 

So when you hear someone in your organization say, "We don't have any data problems," be very skeptical. You have a problem if you are using that suspect data for BI reporting. If you have done nothing to validate critical pieces of data (information), how confident can you be in the reports you are delivering or consuming?

Performance

When reporting directly from operational data, we must use whatever source of data is available to us containing the information we need. For transaction data, that usually means the most detailed level stored in the database (e.g., invoice line-item level). If you need a summary report by Division and Brand, and then another one by Customer Group and Region, your only option is to process all line items (matching the selection criteria) and perform the aggregation. And then do it again for the second report. This often leads to significant performance problems. If these reports are run on an ad-hoc basis during the day, they not only take a while to run, but also can affect everyone else using the system.

What to Do?

When you point a front-end reporting/OLAP tool toward operational data with any of the above issues, you'll need to attempt to solve these problems within the reporting tool itself. The problem is that the vast majority of these front-end tools are not designed to solve these data issues. Conversely, a well-designed data warehouse and associated ETL processes can address all of the above issues (and many more).

 

The following chart shows a typical data warehouse implementation.

   

092611JordanFig2

Figure 2: This is a typical data warehouse implementation.

 

The data warehouse contains the cleansed, consolidated, conformed data from all of your source systems. It always contains the most detailed level of information that is available. This is the "master collection" of data/information. Very few, if any, reports will be produced directly from the data warehouse.

 

 A data mart is a table, or more commonly, a set of related tables that have been built for a specific reporting purpose. For example, you might design a data mart to support marketing reports, financial reports, or an executive dashboard. A multi-dimensional data structure used by an OLAP tool can also be considered to be a data mart. These are not usually implemented via tables but as a proprietary structure specific to the OLAP tool vendor.

 

You may ask, why do we need the data warehouse at all, if the marts are used for reporting? Why not just build the marts directly from the operational data? The answer is simple. Data marts are often at a summary level. They may contain derived values and calculations, and they eventually will contain many years of history. When the business requirements change (and they will) and you need, for example, to re-organize five divisions into four or change the calculation for net sales, you'll need to rebuild the affected data marts. Without the base detail level tables in the data warehouse, your only option is to go back to the operational data to perform the rebuild. If that data is no longer available, you're sunk. For this reason, it's imperative that you maintain detail-level data in a data warehouse as a means of rebuilding data marts (or creating new ones).

 

Metadata is an often-neglected aspect of BI infrastructure. This is the roadmap to the information stored in the data warehouse. End users can only be empowered by the warehouse if they understand the information it contains, and this can come only from the metadata. This should be a structured data repository that provides navigation, drill-down links, etc. to allow end users to explore the data warehouse structure. Business rules, descriptive text, and data lineage must be visible to the users, not buried in the extract, transform, and load (ETL) code.

 

Extract, transform, and load (ETL) is the technology that takes the raw, operational data and transforms it into the cleansed, conformed data in the data warehouse.

 

In a typical scenario, the data warehouse is built on a separate server, so none of the source data is local. If we're lucky, all of the data resides on another IBM i server or LPAR. Most likely, though, you'll also need to bring in data from other platforms or from text/delimited files, etc.

 

We also must consider what set or subset of data we want to extract. For transactional data, the requirement is usually to pull all new transactions since the previous extract. However, for non-transactional data (e.g., customer or product information), we often want to select only the changed records. Journal entries can provide this information for journaled files, using Change Data Capture technology. Another consideration is staging or moving the data to the target system, especially when dealing with non–IBM i data. Should a temporary copy of the data be staged on the target system as the input to the transform/load steps? Often this is a simpler approach (with fewer potential points of failure) and simpler recovery in the event of a network failure or other error during the load.

 

Business rules determine the transformations and logic required to translate the raw data from the data sources into meaningful information in the target database. Some data is simply mapped to the target column unchanged, but often simple transformations, such as changes from numeric to character, concatenation, substring processes, and arithmetic calculations need to be applied. And in some cases, complex conditional logic may also be required.

 

You must also expect to encounter errors or omissions in the source data. Therefore, the business rules must also specify data-quality criteria. This implies a supporting infrastructure to report and manage data errors. The business rules must either correct errors where possible (e.g., by substituting a default value for missing or invalid values) or set aside those errors that cannot be fixed automatically for later manual correction.

 

The final load outputs the transformed data to the target table(s) in the data warehouse. While in some cases this is a simple insert, it's often more involved. For changes to existing rows, the row must be updated: This could require numeric values to be aggregated rather than replaced and so forth, so more rules may be required.

Do It Right the First Time

While all this may sound complex and challenging, it's essential for long-term success with BI. You will find that there are good ETL tools available that dramatically simplify implementing ETL, as well as building the metadata repository. Those organizations that forego an architected data warehouse and ETL almost always end up building independent data marts, with quick-and-dirty load processes. This usually results in multiple versions of the truth, duplicated effort, no metadata, and no error management, which in turn leads to a maintenance nightmare and eventual failure.

 

Properly implemented BI has the capability to transform your business, with increased efficiency, reduced costs, and improved profitability. Your IBM i server is perfectly well-suited to hosting a data warehouse, so there is no need to take the workload off the platform you know and love.

as/400, os/400, iseries, system i, i5/os, ibm i, power systems, 6.1, 7.1, V7, V6R1

Alan Jordan
Alan Jordan has been involved in System 38/AS400/iSeries/IBM i development for his entire IT career and is a 21-year veteran with Coglin Mill. A native of Australia, he has been living in Rochester, Minnesota, since 1998, when the company introduced RODIN to the U.S. market.  Alan oversees product direction and development and also manages the customer support team. He has extensive knowledge and experience in data warehousing technologies on IBM i and is a regular speaker at COMMON and other events.

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  • Expert Tips for IBM i Security: Beyond the Basics

    SB PowerTech WC GenericIn this session, IBM i security expert Robin Tatam provides a quick recap of IBM i security basics and guides you through some advanced cybersecurity techniques that can help you take data protection to the next level. Robin will cover:

    • Reducing the risk posed by special authorities
    • Establishing object-level security
    • Overseeing user actions and data access

    Don't miss this chance to take your knowledge of IBM i security beyond the basics.

     

     

  • 5 IBM i Security Quick Wins

    SB PowerTech WC GenericIn today’s threat landscape, upper management is laser-focused on cybersecurity. You need to make progress in securing your systems—and make it fast.
    There’s no shortage of actions you could take, but what tactics will actually deliver the results you need? And how can you find a security strategy that fits your budget and time constraints?
    Join top IBM i security expert Robin Tatam as he outlines the five fastest and most impactful changes you can make to strengthen IBM i security this year.
    Your system didn’t become unsecure overnight and you won’t be able to turn it around overnight either. But quick wins are possible with IBM i security, and Robin Tatam will show you how to achieve them.

  • How to Meet the Newest Encryption Requirements on IBM i

    SB PowerTech WC GenericA growing number of compliance mandates require sensitive data to be encrypted. But what kind of encryption solution will satisfy an auditor and how can you implement encryption on IBM i? Watch this on-demand webinar to find out how to meet today’s most common encryption requirements on IBM i. You’ll also learn:

    • Why disk encryption isn’t enough
    • What sets strong encryption apart from other solutions
    • Important considerations before implementing encryption

     

     

  • Security Bulletin: Malware Infection Discovered on IBM i Server!

    SB PowerTech WC GenericMalicious programs can bring entire businesses to their knees—and IBM i shops are not immune. It’s critical to grasp the true impact malware can have on IBM i and the network that connects to it. Attend this webinar to gain a thorough understanding of the relationships between:

    • Viruses, native objects, and the integrated file system (IFS)
    • Power Systems and Windows-based viruses and malware
    • PC-based anti-virus scanning versus native IBM i scanning

    There are a number of ways you can minimize your exposure to viruses. IBM i security expert Sandi Moore explains the facts, including how to ensure you're fully protected and compliant with regulations such as PCI.

     

     

  • Fight Cyber Threats with IBM i Encryption

    SB PowerTech WC GenericCyber attacks often target mission-critical servers, and those attack strategies are constantly changing. To stay on top of these threats, your cybersecurity strategies must evolve, too. In this session, IBM i security expert Robin Tatam provides a quick recap of IBM i security basics and guides you through some advanced cybersecurity techniques that can help you take data protection to the next level. Robin will cover:

    • Reducing the risk posed by special authorities
    • Establishing object-level security
    • Overseeing user actions and data access

     

     

     

  • 10 Practical IBM i Security Tips for Surviving Covid-19 and Working From Home

    SB PowerTech WC GenericNow that many organizations have moved to a work from home model, security concerns have risen.

    During this session Carol Woodbury will discuss the issues that the world is currently seeing such as increased malware attacks and then provide practical actions you can take to both monitor and protect your IBM i during this challenging time.

     

  • How to Transfer IBM i Data to Microsoft Excel

    SB_HelpSystems_WC_Generic3 easy ways to get IBM i data into Excel every time
    There’s an easy, more reliable way to import your IBM i data to Excel? It’s called Sequel. During this webinar, our data access experts demonstrate how you can simplify the process of getting data from multiple sources—including Db2 for i—into Excel. Watch to learn how to:

    • Download your IBM i data to Excel in a single step
    • Deliver data to business users in Excel via email or a scheduled job
    • Access IBM i data directly using the Excel add-in in Sequel

    Make 2020 the year you finally see your data clearly, quickly, and securely. Start by giving business users the ability to access crucial business data from IBM i the way they want it—in Microsoft Excel.

     

     

  • HA Alternatives: MIMIX Is Not Your Only Option on IBM i

    SB_HelpSystems_WC_GenericIn this recorded webinar, our experts introduce you to the new HA transition technology available with our Robot HA software. You’ll learn how to:

    • Transition your rules from MIMIX (if you’re happy with them)
    • Simplify your day-to-day activities around high availability
    • Gain back time in your work week
    • Make your CEO happy about reducing IT costs

    Don’t stick with a legacy high availability solution that makes you uncomfortable when transitioning to something better can be simple, safe, and cost-effective.

     

     

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  • Backup and Recovery on IBM i: Your Strategy for the Unexpected

    SB HelpSystems SC 5413Robot automates the routine tasks of iSeries backup and recovery, saving you time and money and making the process safer and more reliable. Automate your backups with the Robot Backup and Recovery Solution. Key features include:
    - Simplified backup procedures
    - Easy data encryption
    - Save media management
    - Guided restoration
    - Seamless product integration
    Make sure your data survives when catastrophe hits. Try the Robot Backup and Recovery Solution FREE for 30 days.

  • Manage IBM i Messages by Exception with Robot

    SB HelpSystems SC 5413Managing messages on your IBM i can be more than a full-time job if you have to do it manually. How can you be sure you won’t miss important system events?
    Automate your message center with the Robot Message Management Solution. Key features include:
    - Automated message management
    - Tailored notifications and automatic escalation
    - System-wide control of your IBM i partitions
    - Two-way system notifications from your mobile device
    - Seamless product integration
    Try the Robot Message Management Solution FREE for 30 days.

  • Easiest Way to Save Money? Stop Printing IBM i Reports

    SB HelpSystems SC 5413Robot automates report bursting, distribution, bundling, and archiving, and offers secure, selective online report viewing.
    Manage your reports with the Robot Report Management Solution. Key features include:

    - Automated report distribution
    - View online without delay
    - Browser interface to make notes
    - Custom retention capabilities
    - Seamless product integration
    Rerun another report? Never again. Try the Robot Report Management Solution FREE for 30 days.

  • Hassle-Free IBM i Operations around the Clock

    SB HelpSystems SC 5413For over 30 years, Robot has been a leader in systems management for IBM i.
    Manage your job schedule with the Robot Job Scheduling Solution. Key features include:
    - Automated batch, interactive, and cross-platform scheduling
    - Event-driven dependency processing
    - Centralized monitoring and reporting
    - Audit log and ready-to-use reports
    - Seamless product integration
    Scale your software, not your staff. Try the Robot Job Scheduling Solution FREE for 30 days.

  • ACO MONITOR Manages your IBM i 24/7 and Notifies You When Your IBM i Needs Assistance!

    SB DDL Systems 5429More than a paging system - ACO MONITOR is a complete systems management solution for your Power Systems running IBM i. ACO MONITOR manages your Power System 24/7, uses advanced technology (like two-way messaging) to notify on-duty support personnel, and responds to complex problems before they reach critical status.

    ACO MONITOR is proven technology and is capable of processing thousands of mission-critical events daily. The software is pre-configured, easy to install, scalable, and greatly improves data center efficiency.