It's time to take a fresh look at an old problem: "garbage in, garbage out." Everyone knows that information is only as good as the data it was built upon, yet according to Harvard Business Review, only 3 percent of companies' data meets basic quality standards. With data becoming the world's most valuable resource, it raises the question, "How is this possible?" I believe the answer is that traditional approaches to data accuracy and master data management simply cannot keep up with the digital transformation that is happening all around us.

Digital Transformation Is Increasing the Need for Master Data Management

Today's companies are constantly trying to gain a competitive advantage, and data is at the center of that effort. Before the advent of cloud applications, companies would make a large investment in a system such as enterprise resource planning (ERP) or customer relationship management (CRM). These solutions would be installed and delivered to their staff on internal corporate networks. This approach suffered from quite a few flaws:

• Central ERP and CRM systems required a huge up-front investment in software and hardware resources.
• Full-time employees, even entire departments, were required to manage the application and infrastructure.
• The company was at the mercy of the vendor for new features and functionality, and the vendor had very little incentive to move quickly.

Companies needed a way to move faster and put the best software in the hands of their staff; in essence, they needed to digitally transform how they operated. Realizing that a single software solution was not going to give them what they needed, they started to adopt best-of-breed applications for each functional department, leading to an explosion of applications throughout the business.

Looking for additional ways to reduce the time-to-value of new applications, many companies turned to the cloud. New Software as a Service (SaaS) offerings were constantly being launched, and with very narrow functionality. The adage "do one thing and do it well" is the mantra of today's SaaS companies.

As a result, the digital transformation that today's companies must adopt in order to stay competitive is resulting in data sprawl. Their data is everywhere, defined by a complex web of on-premises databases and SaaS solutions. Data sprawl presents a view of data that is in complete contrast to how a business sees their data. For example, an online retailer, we'll call them JustForDogs.com, does business with an individual customer named Joe Smith. To JustForDogs.com there is only one Joe Smith, and he has a long history with the company. Joe has purchased many products, is a member of the Happy Doggy loyalty program, and often shares Facebook posts from the JustForDogs page with his friends. However, according to the applications that JustForDogs.com uses, there are three different Joe Smiths. There is a Joe Smith inside the online shopping cart, another one inside the loyalty program, and yet another one connected to the JustForDogs Facebook page. The business needs a way to treat each one of these Joe Smiths as the same person, and that is where master data management comes in.

Traditional Solutions Take Too Long and Cost Too Much

The problem of multiple Joe Smiths in many different applications is a very hard problem to solve, so hard that many companies don't know how to deal with it, let alone how much it is costing them. This leaves the IT department in the difficult situation of trying to quantify the impact of these issues for the C-level and justify the cost. With traditional data accuracy and master data management systems, this cost is very high and often requires many months of work before the business sees any value. By the time they start to reap the benefits of these traditional systems, they have already spent many millions of dollars going down an unsuccessful path.

Measurable Value Is Needed Immediately

It seems to me that a large reason for not taking on data accuracy and master data management initiatives is the high up-front cost and long time-to-value. In a world where results are needed immediately, we have to find a way to remove that overhead. This is where a simple, cloud-based solution like Naveego can help. It allows you to take on these projects with an agile approach. Companies can choose a specific pain point and gain a "quick win" for the business, providing a measurable improvement that can be used to drive the data accuracy story.