Introduction

Last week, Wikibon’s David Vellante released the results of one of Wikbon’s latest research efforts. Entitled Data: THE Critical Enabler and Inhibitor for the Enterprise, the research results include a number of important items of which CIOs should take very close note. As a former CIO with significant data responsibilities and challenges, I identified with the findings immediately.

First, I fully identify with the title. Data is truly both an enabler and an inhibitor in many organizations for different reasons. On the positive side of the equation, data, in the right conditions, can be used to make major improvements in the business and even chart whole new courses. On the other hand, in the conditions, data can inhibit those some opportunities.

Data: Enabler and Inhibitor

How does this happen? It all depends on what you’re attempting to achieve, but in general, a number of factors are at play.

Lack of data quality

A few months ago, I wrote an article for the Wikibon blog entitled Data quality is too important to ignore. In that article, I outlined several reasons why it is increasingly important for organizations to clean up their data act and leverage that data as much as possible to improve decision-making as well as improve overall operations of the organization.

The thrust of that article is all about data quality and why it’s so important that organizations make great effort to ensure that their data assets remain in top operational condition. For obvious reasons, when data quality is high, it can be an enabler.

When data quality is low, it results in poor decision-making and slower operations as people struggle to overcome data issues in their processes. Poor data quality directly affects the bottom line due to these issues.

Lack of data consistency

Many see little difference between data quality and data consistency. However, it depends on how you look at data. If you view data as purely an operational need, ongoing long-term consistency may not be critical. After all, if the sole purpose of the data is to be used to close a process, you may not pay a lot of attention to how consistent it is. I’ve seen this thought process in the real world.

However, this view of data results in the loss of what can be the most important aspect of data — the ability to leverage that data longitudinally, or historically. xcBy viewing every individual data element as a one-off and not ensuring that data over time is consistent, an organization loses the ability to gauge trends, which can be powerful when it comes to making strategic decisions.

Organizations must make great efforts to ensure that data elements remain consistent over time, even as operational requirements change.

Lack of a warehouse

Some organizations make the mistake of trying to use their operational databases for reporting that belongs in a data warehouse. While the information might be accessible, this has several downsides, including:

• Negative impact on operations: Because data in an operational database is laid out in a way that suits operations and not historical reporting, it can take significant processing in queries to pull together all of the pieces of information needed to satisfy the needs of a report.
• Inability to target data elements: The operational database is nothing more than a current business snapshot. While some operational databases may have historical tracking for some elements, these are often used for operational reasons in some way and may not be perfectly suited to long-term reporting use.

When a data warehouse is put into place, the data structure is created with reporting in mind, so reports run more quickly and, since the warehouse is generally on a different server, the impact on operations is zero.

Moreover, since you can pick exactly which data elements should be included in the data warehouse, you also get to choose how those elements operate in the data warehouse so that you can make sure that reporting works exactly as required for every data element. Without a data warehouse, your reports are really just moving targets. Items necessarily change on a regular basis as people do their jobs.

Again, when the right structures are in place and data quality and consistency are high, data can be an enabler and help unlock organizational secrets. On the other hand, when data quality is low, consistency is low and there is no warehouse, data can be an inhibitor as organizations struggle to make sense of what they have.

The onslaught continues

For organizations that are already struggling with data management, the continual accrual of yet more data is an increasingly complex challenge. This is particularly true for those organizations that wish to transform themselves to make significant ongoing use of data. As more and more data is accumulated without a plan for how to use it:

• Costs increase as the need to store data increases.
• Risks increase as the loss of new data opens the organization to additional breaches.

Action Item: CIOs need to tame this monster now. It’s beyond time for organizations to deploy data warehouses to tame the data morass. CIOs should appoint a “data czar” responsible for cataloging all data assets and working with groups throughout the organization to begin to understand how data can be used to improve organizations both in individual data units as well as across the organization.

Simply put, data is a goldmine. When it’s possible to use data in the right way, organizations can begin to undertaken much more comprehensive endeavors that can have a much higher return while at the same time having more confidence in the ultimate outcome. In these kinds of business, data is an enabler.

With poor data, the opposite is the case. These are organizations where “hope” is a strategy because the data can’t be used to prove or disprove any decision due to quality, consistency or organization. In these data-deficient organizations, data is certainly an inhibitor.

Footnotes: