As organizations look to adopt the new wave of coming technologies, like automation, artificial intelligence and the Internet of Things, their success in doing so and their ability to differentiate themselves in those spaces will be dependent upon their ability to get data management right. This will become increasingly important as connected devices and sensors proliferate, causing an exponential growth in data – and a commensurate growth in opportunity to exploit the data.
Those that position their organizations to manage data correctly and understand its inherent value will have the advantage. In fact, we may see leaders pull so far in front that it will make the market very difficult for slow adopters and new entrants.
A recent Forbes Insights report, “The Data Differentiator: How Improving Data Quality Improves Business,” sponsored by Pitney Bowes, examines the key role of data quality.
Good-quality data has several beneficial impacts on organizations:
Decision making: The better the data quality, the more confidence users will have in the outputs they produce, lowering risk in the outcomes and increasing efficiency. The old “garbage in, garbage out” adage is true, as is its inverse. And when outputs are reliable, guesswork and risk in decision making can be mitigated.
Productivity: Good-quality data allows staff to be more productive. Instead of spending time validating and fixing data errors, they can focus on their core mission.
Compliance: In industries where regulations govern relationships or trade with certain customers, especially in finance, maintaining good-quality data can be the difference between compliance and millions of dollars in fines. Compliance must be an ongoing focus as new regulations continue to evolve in regions around the world and wherever a company conducts business. Graph databases are emerging as an important tool for finance firms to understand the complex relationships among their customers and comply with anti-money laundering regulations.
Marketing: Better data enables more accurate targeting and communications, especially in the omnichannel environments many organizations are striving toward.
Negative impacts of poor-quality data can include:
Undermining confidence: 84% of CEOs are concerned about the quality of the data they’re basing decisions on, according to KPMG’s “2016 Global CEO Outlook.” When there’s a lack of trust in data quality, confidence in the results it provides is quickly eroded. That can cause obstacles to gaining executive buy-in, dampening enthusiasm for further investment in data and quality improvement initiatives.
Missed opportunities: If your competitors are gaining more insights from data than you are, they will have insights you don’t. That might mean a company misses a critical opportunity for new product development or customer need that a competitor with a more mature understanding of data may capitalize upon. Companies should treat data as an asset and manage it to maintain quality in order to derive insights that can lead to competitive advantage.
Lost revenue: Poor data can lead to lost revenue in many ways – communications that fail to convert to sales because the underlying customer data is incorrect, for example. In insurance, bad property information could cause revenue to be lost on premiums if they are set too low because of the data. One example is where property locations are estimated, instead of precisely specified. In most cases, that might not matter, but where the difference is a property – or a whole neighborhood – located inside or outside of a flood zone, revenue losses could be significant.
Reputational damage: Reputational costs range from the small, everyday damage that organizations may never be aware of to large public relations disasters. As an example, recall Apple’s widely panned Maps rollout in 2012. At the time, it quickly became clear that much of the underlying data was inaccurate or missing, resulting in a product that TechCrunch later called “barely usable.” Efforts to improve customer experience may also be undermined by bad data resulting in an incorrect spelling of a customer’s name, or obliviously sending communications to a deceased customer. On the larger end, poor data in banking, for example, could lead to inadvertent trade with sanctioned governments or suspected terrorist financiers if institutions don’t have accurate enough information about the customers they’re trading with, resulting in PR fallout on top of punitive fines.