Here’s How Bad Data Can Have Bad Consequences for Your Business
You wouldn’t drink milk if it was five days past its sell-by date. You wouldn’t buy a computer in 2017 running Windows 98. Would you use data that you know is bad, incomplete or outdated? Rishi Dave, CMO at Dun & Bradstreet talks to Finance Monthly about the impact of using bad data, and what […]
You wouldn’t drink milk if it was five days past its sell-by date. You wouldn’t buy a computer in 2017 running Windows 98. Would you use data that you know is bad, incomplete or outdated? Rishi Dave, CMO at Dun & Bradstreet talks to Finance Monthly about the impact of using bad data, and what makes it bad.
Clearly, the answer here is a resounding no. Yet it seems this is common practice for many enterprises; in 2016, poor quality data alone cost the Unites States $3.1 trillion. Most companies know how important data is – managers, financial decision makers, data scientists and so many others use it every day at work. Due to the constraints of time, some employees simply have no choice but to accept the data they’re given and use it for financial contracts, supply chain management or prospecting new customers.
But this is risky business. A company can have all the data in the world at its fingertips, but realistically, how much of that data is accurate? And how is it being processed? Only by having the right tools and analytics can the consequences of bad data be avoided.
What’s the worst that could happen?
Bad data can mean many things; the data itself could be outdated, poorly formatted or inconsistent.
For sales and marketing teams, they rely heavily on the most-up-to-date, real-time data to allow them to effectively do their job properly. It’s no use calling up the MD of a company, only to find out they no longer work there or now have a different title. This can be incredibly timewasting and fundamentally limits a salesperson’s ability to sell; the average sales rep spends 64% of his or her time on non-selling activities. Wasted time leads to wasted revenue, which means bad data is directly impacting the company’s bottom line.
A vital ingredient to growth
Bad data isn’t just a timewaster, but a growth-stopper. For companies to grow, they need the right data for the right business function. Marketers need to ensure their contact database is up to date, or face stultified growth opportunities. Nowadays, businesses are demanding more intelligent, data-driven, real-time insights to realise higher return; 80% of marketers see data quality as critical to sales and marketing teams and more than half are investing to address persistent data challenges.
Incorrect names or job roles, outdated phone numbers and inconsistent & badly recycled data will actively prevent a company from reaching desired prospects. The Databerg report in 2015 found that medium sized companies were spending £435,000 on redundant, obsolete or trivial data. For SMEs, growth via data could certainly be the difference between black and red. And therefore making sure they have the right data is paramount. After all, if you water a plant with seawater, it won’t grow. Feed it with normal water and watch it flourish.
Data is an opportunity
Data has the power to transform businesses – but feed bad data in to a machine (or company), and you’ll only get bad results. From losing customers, a damaged reputation and decreased revenue, everything is at stake. Of course, no company is immune to human error. But what a company can control is its flow of data and how it uses it.
Most businesses know that they have to act to improve the quality of their data. But the way they do this is flawed; most batch cleanse, but they do this once a year at most. In the current age where data flow is constant and new information about customers, partners, suppliers and the economy is available all the time, data insight is only ever as accurate as the data feeding it.
What’s the answer?
What businesses really need to do with their data is to integrate, clean, link, and supplement it so they have an accurate database on which to build their algorithms. This starts with foundational master data.
Master data is the foundational information on customers, vendors and prospects that must be shared across all internal systems, applications, and processes in order for your commercial data, transactional reporting and business activity to be cleaned, linked, optimized and made accurate. It’s essentially the foundation of your enterprise and without it not only does your AI infrastructure breakdown, but so does your business.
Whether it’s a hospital, a financial institution or a marketing agency, ensuring you have the right quality data must be top of every agenda. Data is an opportunity; don’t waste it.