There’s nothing more frustrating than not being able to rely upon a business partner. There’s lots of business books about information technology that espouses the importance of Business/IT alignment and the importance of establishing business users as IT stakeholders. The whole idea of delivering business value with data and analytics is to provide business users with tools and data that can support business decision making. It’s incredibly hard to deliver business value when half of the partnership isn’t stepping up to their responsibilities.
There’s never a shortage of rationale as to why requirements haven’t been collected or recorded. In order for a relationship to be successful, both parties have to participate and cooperate. Gathering and recording requirements isn’t possible if the technologist doesn’t meet with the users to discuss their needs, pains, and priorities. Conversely, the requirements process won’t succeed if the users won’t participate. My last blog reviewed the excuses that technologists offered for explaining the lack of documented requirements; this week’s blog focuses on remarks I’ve heard from business stakeholders.
- “I’m too busy. I don’t have time to talk to developers”
- “I meet with IT every month, they should know my requirements”
- “IT isn’t asking me for requirements, they want me to approve SQL”
- “We sent an email with a list of questions. What else do they need?”
- “They have copies of reports we create. That should be enough.”
- “The IT staff has worked here longer than I have. There’s nothing I can tell them that they don’t already know”
- “I’ve discussed my reporting needs in 3 separate meetings; I seem to be educating someone else with each successive discussion”
- “I seem to answer a lot of questions. I don’t ever see anyone writing anything down”
- “I’ll meet with them again when they deliver the requirements I identified in our last discussion.
- “I’m not going to sign off on the requirements because my business priorities might change – and I’ll need to change the requirements.
Requirements gathering is really a beginning stage for negotiating a contract for the creation and delivery of new software. The contract is closed (or agreed to) when the business stakeholders agree to (or sign-off on) the requirements document. While many believe that requirements are an IT-only artifact, they’re really a tool to establish responsibilities of both parties in the relationship.
A requirements document defines the data, functions, and capabilities that the technologist needs to build to deliver business value. The requirements document also establishes the “product” that will be deployed and used by the business stakeholders to support their business decision making activities. The requirements process holds both parties accountable: technologists to build and business stakeholders to use. When two organizations can’t work together to develop requirements, it’s often a reflection of a bigger problem.
It’s not fair for business stakeholders to expect development teams to build commercial grade software if there’s no participation in the requirements process. By the same token, it’s not right for technologists to build software without business stakeholder participation. If one stakeholder doesn’t want to participate in the requirements process, they shouldn’t be allowed to offer an opinion about the resulting deliverable. If multiple stakeholders don’t want to participate in a requirements activity, the development process should be cancelled. Lack of business stakeholder participation means they have other priorities; the technologists should take a hint and work on their other priorities.
Maybe it’s time to challenge the 20 year-old paradigm of making everyone a knowledge worker. For a long time the BI community has assumed that if we give business users the right data and tools, they’ll have the necessary ammunition to do their jobs. But I’m beginning to believe that may no longer be a practical approach. At least not for everyone.
One thing that’s changed in the last dozen-or-so years is that individuals’ job responsibilities have become more complex. The breadth of these responsibilities has grown. I question whether the average business user can really keep track of all the subject area content, all the table definitions, column names, data types, definitions of columns, and locations of all the values across the 6000+ tables in the data mart.
And that’s just the data mart. I’m not even including the applications and systems the average business user interacts with on a daily basis. Not to mention all those presentations, documents, videos, and archived e-mails from customers.
I’m not arguing the value of analytics, nor am I challenging the value of the data warehouse. But is it really practical to expect everyone to generate their own reports? Look at the U.S. tax code. It’s certainly broader than a single CPA can keep track of. Now consider most companies’ Finance departments. There’s more data coming out of Finance than most people can deal with. Otherwise all those specialized applications and dedicated data analysts wouldn’t exist in the first place!
Maybe it’s not about delivering BI tools to every end-user. Maybe it’s about delivering reports in a manner that can be consumed. We’ve gotten so wound-up about detailed data that we haven’t stopped to wonder whether it’s worthwhile to push all that detail to the end-user’s desktop—and then expect him or her to learn all the rules.
One of my brokerage accounts contains 5 different equities. I don’t look at them every day. I don’t look at intra-day price changes. I really don’t need to know. All I really want to know is when I do look at the information, has the stock’s value gone up or down? And how do I get the information? I didn’t build a custom report. I didn’t do drill-down, or drill-across. I went to the web and searched on the stock price.
Maybe instead of buying of a copy of a [name the BI vendor software] tool, we simply build a set of standard reports for key business areas (Sales, Marketing, Finance), and publish them. You can publish these reports to a drive, to a server, to a website, to a portal—it shouldn’t matter. People should find the information with a browser. Reports can be stored and indexed and accessed via an enterprise search engine. Of course, as with everything else, you still need to define terms and metadata so that people understand what they’re reading.
Whenever people talk about enterprise search functionality they’re usually obsessing about unstructured data. But enterprise search can deliver enormous value for structured data. IT departments could be leading the charge if the definition of success weren’t large infrastructure and technology implementation projects and instead data delivery and usage.
The executive doesn’t ask, “What tool did you use to solve this problem?” Instead, she wants to know if the problem has in fact been solved.
The issue of Total Cost of Ownership (TCO) seems to come and go every few years. The need for it tends to ebb and flow with corporate budget cycles. TCO is perfectly fine for well-understood commodity functions or defined business processes. If I have to replace a server or a printer, or change a business process, TCO is a perfectly rational metric for comparing different alternatives.
When TCO calculations work, they tend to roll up within a single organization or manager. The hardware, the software, the installation, and the maintenance are under the domain of a single organization that covers the direct cost.
The problem with TCO arises when it’s used as a metric for justifying cross-functional or analytical systems. With these systems, the value isn’t delivering commodity processing but rather supporting decision making. TCO focuses on construction and maintenance costs. But for analytical systems, usage occurs across different organizations and varies with business value and need. TCO can in fact be misapplied.
At a simple level, TCO is often limited to processing hardware, storage, software, and IT resources necessary to configure and manage the platform on an ongoing basis. But this is usually limited to IT staff focused on system development and maintenance. Unfortunately the most expensive cost—not normally included in TCO calculations—is the business user’s time. While TCO quantifies costs for a data warehouse developer, there is no clear way to calculate costs for the dozens or hundreds of business users who are actually analyzing data and creating reports every day. The reality of analytical systems is that development continues every day on the business side.
Nevertheless it’s common for TCO calculations to be reduced to the cost of processing or storage, rather than reflecting the exponential costs of users circumventing slow-running queries and inaccurate data. At the end of the day, TCO shouldn’t only be about the cost of hardware and software installation and maintenance. It should be about the cost of continued business usage.
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Unless you’ve been hiding in a cave in the past year, you’ve probably heard of CEP (Complex Event Processing) or data stream analysis. Because a lot of real-time analysis focuses on discrete data elements rather than data sets, this technology allows users to query and manipulate discrete pieces of information, like events and messages, in real-time—without being encumbered by a traditional database management system.
The analogy here is that if you can’t bring Mohammed to the mountain, bring the mountain to Mohammed: why bother loading data into a database with a bunch of other records when I only need to manipulate a single record? Furthermore, this lets me analyze the data right after its time of creation! Since one of the biggest obstacles to query performance is disk I/O, why not bypass the I/O problem altogether?
I’m not challenging data warehousing and historical analysis. But the time has come to apply complex analytics and data manipulation against discrete records more efficiently. Some of the more common applications of this technology include fraud/transaction approval, event pattern recognition, and brokerage trading systems.
When it comes to ETL (Extract, Transform, and Load) processing, particularly in a real-time or so-called “trickle-feed” environment, CEP may actually provide a better approach to traditional ETL. CEP provides complex data manipulation directly against the individual record. There is no intermediary database. The architecture is inherently storage-efficient: if a second, third, or fourth application needs access to a particular data element, it doesn’t get its own copy. Instead, each application applies its own process. This prevents the unnecessary or reckless copying of source application content.
There are many industries need a real-time view of customer activities. For instance in the gaming industry when a customer inserts her card into a slot machine, the casino wants to provide a custom offer. Using traditional data warehouse technology, a significant amount of processing is required to capture the data, to transform and standardize it, to load it into a table, only to make it available to a query to identify the best offer. In the world of CEP we’d simply query the initial message and make the best offer.
Many ETL tools already use query language constructs and operators to manipulate data. They typically require the data to be loaded into a database. The major vendors have evolved to an “ELT” architecture: to leverage the underlying database engine to address performance. Why not simply tackle the performance problem directly and bypass the database altogether?
The promise of CEP a new set of business applications and capabilities. I’m also starting to believe that CEP could actually replace traditional ETL tools as a higher performance and easier-to-use alternative. The interesting part will be seeing how long before companies emerge from their caves and adopt it.
photo by Orin Zebest via Flickr (Creative Commons license)
Back when I was applying to college, I’d read over college catalogs. Inevitably, each university would mention the number of books it had in its library. When I finally went to college, I realized that this metric was fairly meaningless. A dozen volumes on Grecian pottery did me no good when I was in search of a book on polymers for my mechanical engineering class.
Clients will often ask us to scope a “data inventory” project, inevitably focused on identifying and describing all the data elements contained across their different application systems. Recently a new CIO asked us to head up a “tiger team” to inventory his company’s data. He was surprised at the quantity of information needs that had been sent his way. As expected, he inquired about systems of record and data dictionaries. As you can imagine, he received multiple and conflicting answers which only exacerbated his confusion.
As a point of reference, well-known ERP systems can have in excess of 50,000 discrete data elements in their databases (never mind that some aren’t in English). As I’ve written in the past, many of these data elements have no use outside of the application itself.
Having terabyte upon terabyte of information is equally irrelevant if that data is unrelated to current business issues. The problem with a data inventory activity is that identifying and counting data elements in different systems and applications won’t necessarily solve any problems. Why? Because data across applications and packages is inconsistent: there are different names, definitions, and values, and there is no practical means of determining which data they actually have in common. This is like going to the hardware store and looking for a specific screw, but all the different screws are in one big barrel—you end up having to pick through each screw, one at time. When you find the screw, you just throw all the other screws back into the barrel.
The point of a data inventory isn’t to pick through data because it exists, but to inventory the data people actually need. If you’re going to undertake a data inventory, your output should be structured so that the next person doesn’t have to repeat your work. Identify the data that is moving across various systems, as this indicates key information that’s being shared. Categorize this data by subject area. You’ll inevitably find that there are inconsistent versions of the data, enabling you to identify data disparities. You can then begin to develop a catalog of key corporate data that will form the basis of your data dictionary.
Inventorying the data that moves between systems accomplishes two things: it identifies the most valuable data elements in use, and it will also help identify data that’s not high-value, as it’s not being shared or used. This approach also provides a way to tackle initial data quality efforts by identifying the most “active” data used by the business. It ultimately helps the data management team understand where to focus its efforts, and prioritize accordingly.
So next time someone suggests a data inventory without context or objectives, consider sending them to college to study Grecian urns.
As I wrote in last week’s blog post, a data warehouse appliance simplifies platform and system resource administration. It doesn’t simplify the traditional time-intensive efforts of managing and integrating disparate data and addressing performance and tuning of various applications that contend for the same resources.
Many data warehouse appliance vendors offer sophisticated parallel processing environments, query optimization, and specialized storage structures to improve query processing (e.g., columnar-based engines). It’s naïve to think that taking data from an SMP (Symmetric Multi-Processing) relational database and moving it into a parallel processing environment will effectively scale without any adjustments or changes. Moving onto an appliance can be likened to moving into a new house. When you move into a new, larger house, you quickly learn that it’s not as simple as dumping all of your stuff into the new house. The different dimensions of the new rooms cause you realize that some of your old furniture or rugs simple don’t fit. You inevitably have to make adjustments if you want to truly enjoy your new home. The same goes with a data warehouse appliance; it likely has numerous features to support growth and scalability; you have to make adjustments to leverage their benefits.
Companies that expect to simply dump their data from a few legacy data marts over to a new appliance should expect to confront some adjustments or their likely to experience some unpleasant surprises. Here are some that we’ve already seen.
Everyone agrees that the biggest cost issue behind building a data warehouse is ETL design and development. Hoping to migrate existing ETL jobs into a new hardware and processing environment without expecting rework is short-sighted. While you can probably force fit your existing job streams, you’ll inevitably misuse the new system, waste system resources, and dramatically reduce the lifespan of the appliance. Each appliance has its own way of handling the intensive resource requirements of data loading – in much the same way that each incumbent database product addresses these same situations. If you’ve justified an appliance through the benefits of consolidating multiple data marts (that contain duplicate data), it only makes sense to consolidate and integrate the ETL processes to prevent processing duplication and waste.
To assume that because you’ve built your ETL architecture leveraging the latest and greatest ETL software technology that you won’t have to review the underlying ETL architecture is also misguided. While there’s no question that migrating tool-based ETL jobs to a new platform can be much easier than lower-level code, the issue at hand isn’t the source and destination– it’s the underlying table structures. Not every table will change in definition on a new platform, but the largest (and most used) table content is the most likely candidate for review and redesign. Each appliance handles data distribution and database design differently. Consequently, since the underlying table structures are likely to require adjustment, plan on a redesign of the actual ETL process too.
I’m also surprised by the casual attitude regarding technical training. After all, it’s just a SQL database, right? But application developers and data warehouse development staff need to understand the differences of the appliance product (after all, it’s a different database version or product). While most of this knowledge can be gained through reading the manuals – when was the last time the DBAs or database developers actually had a full-set of manuals—much less the time required to read them? The investment in training isn’t significant—usually just a few days of classes. If you’re going to provide your developers with a product that claims to bigger, better, and faster than its competitors, doesn’t it make sense to prepare them adequately to use it?
There’s also an assumption that—since most data warehouse appliance vendors are software-only—that there are no hardware implications. On the contrary, you should expect to change your existing hardware. The way memory and storage are configured on a data warehouse appliance can differ from a general-purpose server, but it’s still rare that the hardware costs are factored into the development plan. And believing that older servers can be re-purposed has turned out to be a myth. If you ‘re attempting to support more storage, more processing, and more users, how can using older equipment (with the related higher maintenance costs) make financial sense?
You could certainly fork-lift your data, leave all the ETL jobs alone, and not change any processing. Then again, you could save a fortune on a new data warehouse appliance and simply do nothing. After all, no one argues with the savings associated with doing nothing—except, of course, the users that need the data to run your business.
photo by Bien Stephenson via Flickr (Creative Commons License)
Many of our clients have asked us about whether it’s time to consider replacing their aging data warehouses with data warehouse appliance technologies. I chock up this emerging interest to the reality that data warehouse life spans are 3 to 4 years and platforms need to be refreshed. Given the recent crop of announcements by vendors like Oracle and Teradata along with the high visibility of newer players like Netezza, Paraccel, and Vertica.
The benefit of a data warehouse appliance is that includes all of the hardware and software in a preconfigured solution that dramatically simplifies running and managing a data warehouse. (Some of the vendors have taken that one step further and actually sell software that is setup to work with specially defined commodity hardware configurations). Given the price/performance differences between the established data warehouse products and the newer data warehouse appliances, it only makes sense that these products be considered as alternatives to simply upgrading the hardware.
The data warehouse appliance market is arguably not new. In the 1980s companies like Britton-Lee and Teradata argued that database processing was different and would perform better with purpose-designed hardware and software. Many have also forgotten these pioneers argued that the power of commodity microprocessors vastly exceeded the price/performance of their mainframe processor competitors.
The current-generation appliance vendors have been invited to the table because of the enormous costs that have evolved in managing the enormous data volumes and operational access associated with today’s data warehouses. Most IT shops have learned that database scalability doesn’t just mean throwing more hardware and storage at the problem. The challenge in managing these larger environments is understand the dynamics of the data content and the associated processing. That’s why partitioning the data across multiple servers or simply removing history doesn’t work – for every shortcut taken to reduce the data quantity, there’s an equal impact to user access and the single version of truth. This approach also makes data manipulation and even system support dramatically more complicated.
It’s no surprise that these venture capital backed firms would focus on delivering a solution that was simpler to configure and manage. The glossy sales message of data warehouse appliance vendors comes down’ to something like: “We’ve reduced the complexity of running a data warehouse.. Just install our appliance like a toaster, and watch it go!” There’s no question that many of these appliance vendors have delivered when it comes to simplifying platform management and configuration; the real challenge is addressing the management and configuration issues that impact a growing data warehouse: scalable load processing, a flexible data architecture, and manageable query processing.
We’ve already run into several early-adopters that think all that is necessary is to simply fork-lift their existing data warehouse structures onto their new appliance. While this approach may work initially, the actual longevity of the appliance – or its price/performance rationale will soon evaporate. These new products can’t work around bad data, poor design habits, and the limitations of duplicate data; their power is providing scalability across enormous data and processing volumes. An appliance removes the complexities of platform administration. But no matter what appliance you purchase, and no matter how much horsepower it has, data architecture and data administration are still required.
In order to leverage the true power of an appliance, you have to expect to focus effort towards integrating data in a structure that leverages the scalability strengths of the product. While the appliances are SQL-based, the way they process loads, organize data, and handle queries can be dramatically different than their incumbent data marts and data warehouses. It’s naïve to think that a new appliance can provide processing scalability without any adjustments. If it was that simple, the incumbent vendors would have already packaged that in their existing products.
In Part 2 of this post, I’ll elaborate on the faulty assumptions of many companies that acquire data warehouse appliances, and warn you against making these same mistakes.
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