Business leaders deem data and analytic proficiency to be a top investment priority because of the role it plays in enabling future technologies like the Internet of Things and Artificial Intelligence. However, maintaining data quality is an ongoing problem for many businesses.
Confidence in data is often low, particularly when organisations have vast amounts of legacy data or if their information is held in silos. The challenges of improving data quality can be formidable, but the cost of bad data is high and not just a financial one — it can result in under-reporting, reputational damage, missed opportunities and high-risk decision making.
The consequences of bad data
Companies rely on accurate data to assist marketing, sales and customer service activities and inaccurate data can undermine good customer experience. If companies have the wrong information on their customers, they will waste time chasing leads that don’t exist.
A key problem of bad data is that you are making important business decisions based on that very same bad data. Decision-making is only as good as the information on which it is based, and errors in data mean any analysis run can be completely wrong. For example, a report telling you that all sales leads are coming from sales reps means you will make hiring and process decisions based on that information without knowing the data is incorrect.
You might be missing vital opportunities for new product development or customer needs that your competitor with better data management is capitalising on.
Loss revenue occurs in numerous ways as a result of bad data, including communication fails that don’t convert to sales if underlying customer data is incorrect or in inefficiencies in business processes which depend on data. All of which includes errors in reporting, product ordering and nearly anything else that relies on quality data.
Inefficiencies can result in expensive rework efforts to fix problems and improve data management, to meet the requirements of the various business processes.
Poor data management can be responsible for reputational damage that can range from small, everyday harm to large public relations disasters due to data breaches.
The benefits of good data management
Data is the lifeblood of any organisation. It is potentially every company’s greatest asset. With the right data, proper business decisions can be made, customers and employees are better supported, and all functional areas will run more efficiently.
Without high-quality data management, it is impossible to know exactly what is happening across sales, marketing and procurement teams. When sales reps don’t enter accurate data into CRM systems, sales managers have no idea where, how or when the majority of calls are made. Without this data, business leaders are unsure of who sold the most product, which customers converted quickly and where any lost opportunities occurred.
Bad data also leads to bad analysis, which can lead to poor business decisions and even worse, poor business results.
Improving data management
So, while bad data management is not good for your business, poor data quality may also mean you are not analysing the right data in the first place. In today’s competitive, data-driven business environment, any organisation that isn’t using quality data to make decisions is at risk of falling behind.
Never under-estimate the power of your data, it is crucial for managing projects, avoiding fraud, assessing performance, controlling finances and delivering services efficiently. Bad data is equivalent to presenting research based on very little fact.
Topics: clean data, data accuracy, data hygiene