Selling database projects internally: business needs trump tech specs

Get advice from consultant Rick Sherman on how to build a business case for database projects, from creating a database project plan to getting executive approval and funding.

Businesses are awash with ever-increasing amounts of data created in their transaction systems and then used by business executives and workers striving to analyze all of the information. And behind every enterprise application, and every business intelligence (BI) dashboard and report, is a database.

Forrester Research Inc. predicted last year that the worldwide database market, including software license fees plus technical support and services costs, would grow to $32 billion by 2013, up from $27 billion at the time. But despite the ongoing growth, and the status of databases as the foundation enabling information access and analytics within organizations, IT can often be frustrated in its attempts to get approval for database projects from business executives.

That raises two questions: What database technologies or strategies should IT managers consider for use, and how should they sell database investments to the business?

In developing a database strategy, it’s always best to start with the basics. The primary consideration with the relational databases that power most enterprise applications and data warehouses is assessing how they will scale on performance with increasing amounts of data and growing numbers of users looking to access the information. The IT group then must determine the amount of hardware, storage and database administration resources that will be needed to support the databases. Once those estimates are in hand, IT can create and present a database project plan to business executives.

Do the math: pitching database projects by the numbers
Justifying database projects and getting business approval and executive sponsorship is a quantifiable exercise. For example, IT's business case for proposed investments in database system software should provide information on cost vs. performance trade-offs. Too often, IT doesn’t link metrics such as database tuning requirements to the value that business users will be able to get from, say, a BI dashboard. Analytics applications are only as good as the query performance supported by the databases that are associated with them.

Along with focusing on the technology basics, one of the key success criteria that I recommend for the database planning process is to avoid the standardization trap. A common IT tactic to reduce costs and improve efficiency is to standardize on specific technologies and vendors. That can be an excellent idea on a going-forward basis with new database projects and purchases. But standardization can be more trouble than it’s worth if a company looks to migrate existing databases to the chosen product.

Although it is absolutely true that standardizing on a single database will save money in the long run, the reality is that database migrations take a significant amount of time, money and resources. And with the ever-increasing demands for actionable information from end users, a migration approach is a loser because of the opportunity time that is lost: The end result for the business user is no new data or analytic capabilities. It’s more important in today's economic climate that resources and available budget dollars are devoted to providing greater amounts of information to your users.

While relational software is the usual technology of choice for database projects, there are other technologies that might help organizations meet end-user demands for high-performance analytics or that can be used to address information types beyond structured data. For example, it’s common for enterprises to have already adopted the first wave of databases specialized for analytics: online analytical processing (OLAP) software.

Non-relational technologies have now scaled beyond simple OLAP cubes into databases that can handle analytics processing for substantial data volumes and large numbers of concurrent users – for example, columnar and massively parallel processing databases. Other technologies that offer alternatives to conventional relational software include NoSQL databases and approaches such as in-memory analytics and in-database analytics.

Thinking outside the relational box on database projects
Many enterprises may be able to continue meeting business needs by scaling up their relational databases, but others will tax the limits of relational technology – or their budgets. In such cases, the IT group should investigate some of the emerging database technologies as potential alternatives to relational software for new database projects.

IT has the means to succeed in getting approval for database projects. It only needs the will to proceed.

 

Of course, as with any technology, IT needs to determine how valid vendor claims are with respect to functionality, performance, ease of implementation and total cost of ownership. Some of that due diligence can come from industry analysts and consultants, but there’s no substitute for hands-on use. With many business executives looking for better BI and analytics capabilities, IT managers should be able to secure seed money for a proof of concept (POC) project or a more substantial pilot deployment.

If the POC or pilot project is successful, the next step is obtaining business sponsorship and funding. This is where many IT groups fail because their database project proposal focuses too much on the “how” aspects of technology instead of the “what.” To go back to the earlier point about starting with the basics, business users want to know what the technology is going to do for them, not how it works. In selling any database projects internally, IT needs to understand key business initiatives, determine how database technology can best support them and shape the proposed database strategy accordingly.

The bottom line is that IT has the means to succeed in getting approval for database projects and investments. It only needs the will to proceed.

About the author: Rick Sherman is the founder of Athena IT Solutions, a Stow, Mass.-based firm that provides data warehouse and business intelligence consulting, training and vendor services. In addition to having more than 20 years of experience in the IT business, Sherman is a published author of more than 50 articles, a frequent industry speaker, an Information Management Innovative Solution Awards judge and an expert contributor to both SearchBusinessAnalytics.com and SearchDataManagement.com. He blogs at The Data Doghouse and can be reached at rsherman@athena-solutions.com.

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