By Mark Dexter
15th September 2011

Most data management professionals understand the negative impact of poor or no data architecture practices. Often, the design is completed up front and the model becomes a nice piece of art hanging on the wall with no practical value. Or, there is little or no upfront design and it becomes the Wild West. This leads to confusion, misinterpretation or unnecessary creative liberties, ultimately wasting time and effort and directly impacting the bottom line.

It’s also true that most data management professionals understand the value of data modeling and architecture. Upfront design can reveal design flaws early in the process, enabling them to be corrected during the design or development phase, rather than waiting until post-production when the cost involved skyrockets. What’s more, a blueprint of the data provides traceability for strategic initiatives like master data management, business intelligence, data governance and data warehousing while also providing impact analysis so that changes are implemented into the system faster and easier. Shared and consistent definitions provide a holistic view of the data across systems, so reuse, rather than reinvention, is promoted.

All this taken into account, it is flabbergasting that, when it comes time to look at new or better tooling or expertise to add to the team, it becomes very difficult to justify the expense. Getting executive buy-in for modeling is an ongoing challenge for many organizations, but why? Data modeling tools are not cheap – at least the good ones are not. However, when you compare their costs against the budget expenses that organizations allocate for BI, data warehousing and MDM, the expense of a data modeling tool suite is minute. This begs the question: How can and should you justify modeling to the people holding the purse strings?

Finding Executive Hot Buttons

First and foremost, do your best to work with executive management, not against them. I hear many stories from customers and prospects that really sound more like complaints. They portray an “us against them” mentality. They think the executive team just doesn’t get it. This contentious attitude is not going to sway naysayers to their side. The fact is that it’s the data management professional’s job to educate them so they do get it.

Making Data Modeling the Next Shiny Object

Many executives have a shiny object syndrome, where the latest project or technology to come in front of them completely consumes them and nothing else matters. That is, until another brighter, shinier object comes along. You need to clearly understand what the current object of affection is and vocalize how you can add value through modeling. Talk with others in senior management (heck, even take them to lunch) to stay ahead of the hot initiatives that are important in your organization and work to demonstrate how improved data architecture and modeling practices will help support those initiatives.

After all, everything starts and ends with the data. Understanding what motivates your upper management will make the justification much easier. Is it fear? Is it saving money? Is it making money? Is it good press? Is it staying out of the press? Do they like tangible results and measuring improvement? How about on-time deliverables?

The Power of Fear

Fear can be a great motivator. The promise of improved risk analysis and compliance is often an easy sell, given that most organizations these days have new initiatives that address the treatment of sensitive data – how to find it, isolate it and ensure it is protected throughout its lifecycle.

Approximately every 18 months, news of a data breach surfaces, often involving the compromise of credit card numbers or Social Security numbers. Understanding the true impact of a catastrophe such as this requires a deep knowledge of the data and its whereabouts, for which modeling plays a critical role. BI is another area where modeling is highly important. I’m familiar with an example involving an insurance company with a major BI initiative.

The team’s first step was to understand data stewardship and define the rules and processes for the flow of data. A huge undertaking of the project was discovering and documenting data and then classifying it for compliance reporting. Without a modeling tool, they estimated it would take 20 months to accomplish the first phase of discovering and documenting. With a modeling tool, they estimated it was taking about 25 minutes. Needless to say, it was an easy sell to upper management. History has a way of repeating itself. Recent news is a valuable tool to make a point and strengthen your cause.

A Matter of Simple Math

Modelers often point to things like avoiding hand-coding DDL, improving data definitions, enforcing standards and promoting reuse as key benefits of data modeling and model-driven development. While these are all helpful to the organization, many view them as soft benefits that can be difficult to quantify. Tying each of these to actual time savings and productivity gains will help demonstrate ROI. For instance, many development teams believe that data modeling gets in the way of developers and slows them down. Can you devise points and data to prove the opposite? If so, your sell will be much easier.

Begin by understanding where data modeling fits into your organization’s application lifecycle. Where are cycles being unnecessarily spent? Are developers hand-coding DDL for new tables? How are they diagnosing the impact of changes to existing systems? Are individual developers recreating tables that already exist? By knowing how much time is saved and using some elementary math, it should be relatively simple to determine how much money can be saved. And, remember to take into account the entire application lifecycle, not merely the upfront design work. Model-driven development doesn’t mean model once then forget about it. It means using the model for ongoing development and maintenance of the database. Once you’ve done your homework and your math, take care to package the information into a form that will be impactful and easy to digest, then schedule a time to get in front of the decision-makers and state your case.

Defining the Value of Business Definitions

Measuring improvement with data definitions is another area to underscore for executive management, and it’s probably the most difficult to justify value. Questions to address include: How will improved data definitions be an asset to IT and the business? Will improved definitions provide business users with a better understanding of the data for strategic initiatives like MDM and BI? How much more productive will business users be if they have the definitions at their fingertips? Measuring the data that is and is not documented is the first step. The next phase entails turning the Boolean (yes/no) metric into a rating scale (0-5) that measures the quality of each definition. Improving those metrics over time is the final step to establishing real value. Strengthen your case by conveying that a modeling tool is a valuable communication asset as well as development tool.

One final aspect to not lose sight of in your quest for executive buy-in is the cost of modeling tools. On a per-head basis, many of the high-end modeling tools are deemed to be very expensive and, often times, that is because they are thought of as development tools. Find ways to explain the value of modeling tools outside of the development process. Outline how they are metadata tools, communication tools and collaboration tools wrapped into one. Modeling is sometimes viewed as a “once in a while” activity for many types of users, but still provides value for certain tasks. Propose tools that allow shared licenses across teams because they optimize usage of a tool across casual users, which hits on the executive cost-savings hot button. Proving the value of modeling can be a daunting task, depending on the culture of your organization.

The important “ask factor” is using data to substantiate your claims and deliver tangible results that upper management can easily understand. Mapping results to hot issues on the business side will also strengthen your position. It is very easy to focus only on what you are delivering. If you can also demonstrate why it is important, you’ll find a lot more people listening to your argument and loosening the purse strings.

Article by Jason Tiret - Information Management News

Comments

Currently there are no comments. Be the first to post one!

Post Comment

*
*
*

My dream holiday…

Summer holiday time is just around the corner, the sun is shining (most of the time) and people are off jet-setting across the world. We love finding out (random) stuff about the team , so we wanted to know if money... Read More

Is technology revolutionising agriculture?

Arguably every industry in the world is having to embrace new technology, from the Internet of Things to the collecting of even more big data. One industry that is no stranger to the adaption is agriculture; the world of farming... Read More

4 reasons you should hire a coding bootcamp graduate

I’ve been working within the US space for a while now and I keep coming across candidates that have graduated from coding or data science bootcamps. Some of these candidates have previous degrees from University some do not. Many of... Read More

Why choose a career in the data industry?

It’s the time of year where students are taking their final exams and are preparing to graduate. Many students are now starting to look at their career and what industry they want to work in, my colleague recently wrote about... Read More

Where should we send our newsletter?

Close