Bad Data Introduction:
“Data is the lifeblood of a CRM system. The quality of the data directly relates to the overall health of the system.” – Rowland Dexter, Managing Director, QGate
If you have a sneaking suspicion that your data is not up to scratch, you’re not alone. A 2015 global research study from Experian Data Quality throws up a major contradiction that is itself a startling reality: 99% of companies claim to have a data quality strategy in place, but 91% of those with a strategy still struggle with issues surrounding the bad quality of their contact data.
Common complaints include incomplete data, outdated records, inaccurate records, duplicate data, and typos.
To try and prevent or eliminate these kinds of errors, some companies are using specialised software. 38% of companies surveyed use software to check data for errors at the point of capture, while 34% use software that cleans data after it has been collected.
Unfortunately many executives find the subject of data management to be boring or can’t see the value of it over other projects and pay only lip service to the need for the business to focus on it – until they realise how much is really at stake. The health of your company is revealed through the health of your data. This article will explain the five ways clean data can help your business.
Lost Income and Increased Costs:
“The era of quantity over quality data is well and truly over.” – Experian Data Quality
When put into monetary terms, the costs of bad data are alarming. The average organisation loses 12% of its income because of bad contact data through wasted marketing spend and resources as well as through lost productivity. In fact, 88% of respondents believe that they have wasted income because of bad contact data.
As an example, imagine you are sending out a catalogue to past clients and new prospects. If you send out 10,000 printed catalogues, but 10% of the addresses are incorrect and another 15% are duplicated records, 2,500 of your catalogues could be going to waste, costing you money you may never get a return on.
Data cleansing tools that identify duplicates and find errors can highlight data problems you need to resolve. However, you must be cautious when selecting one of these solutions. Spending money on a data audit may clean up your system for a while, but without examining how duplicates or other data errors make their way into your system those problems will reappear. Why spend thousands on cleaning up a mess when you know it will crop up again and you don’t know how it was made?
To save your money from going to waste because of bad data, you need to combine software tools with a data management strategy. These tools should clean your existing data and prevent new errors, such as duplicates, from entering. This will put you in the best position possible to maintain a healthy database and save your company money by minimising wasted income on things like marketing costs and stopgap data cleansing solutions.
Lowered Customer Satisfaction
“Data is the most visible part of your system to your customers, it is the element that your credibility is built on.” – Kerry Travers, former Senior Business Consultant, BBC
Bad data can cost you more than just money. It can be a major hit to your brand.
Experian asked organisations who carry out email campaigns how bounce backs from poor data affected their business. A startling 67% reported problems delivering emails that caused 26% to be unable to communicate with their customers. 28% said that their customer service suffered as a result while 21% suffered reputation problems.
It may surprise you to learn that marketing and sales professionals think more than 30% of their records are wrong. It may alarm you to know that this poor data affects how satisfied customers and prospects are with your company. Duplicated and inaccurate data affects every customer touch point from service teams’ conversations with your customers to marketing and sales messaging with new prospects.
Imagine how your company looks to Andrew Smith, who received three versions of your marketing materials addressed to Andy Smith, Andrew Smith and Drew Smith. Do you think he sees your company as a competent and professional one?
The Experian study found that loyalty and customer engagement programmes suffer because of bad contact data. 70% of organisations reported problems with inaccurate customer information (34%) and not having enough data (24%) as among the top causes.
By neglecting the health of your customer contact data, you are neglecting the health of your business. Customers are sources of existing business, new business and referrals. By improving the state of their data, you are maximising the effectiveness of your communications with them and building the perception of your brand.
Inefficiency and Lack of Productivity
“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency” – Bill Gates
In 2014 organisations collected contact data for customers and prospects from an average of 3.4 sources. These included websites, call centres and face to face sales. It is more important than ever for organisations to implement a data quality strategy that takes into account this wide variety of data sources.
Differing data sources are sometimes the reason for inaccuracies or duplicates inside of CRM systems. For example, if two salespeople talk to new lead Antonio Andreas independently of one another and enter this new lead into the CRM without checking to see if it already exists, you get two versions of Mr Andreas. If other staff members engage with Mr Andreas, they are then faced with a choice of which instance of the lead they should add notes to. This creates a problem when trying to find out information about Mr Andreas’ engagement with the brand because his activities are not centralised in one record.
All this confusion makes it more difficult for staff to work efficiently with customers and prospect record, compounded by the fact that Mr Andreas is not the only record with a duplicate in the CRM.
If CRM is supposed to provide a 360° view of your customers, allowing bad data to infect your CRM is undermining its purpose. Where CRM normally allows your staff a more efficient way of reviewing data about customers and prospects, they are now in doubt of all the information in the database, undermining confidence in the system and slowing productivity.
Decreased User Adoption
“As confidence in the system drops, users take less care in the data they enter, and the situation spirals out of control. In the end users stop using it and implement their own systems that they feel they can rely on.” – Rowland Dexter, Managing Director, QGate
Data confidence plays a key role in how company employees use business systems like CRM. Poor quality data is one of the most common causes of low user adoption. Your CRM and other data management systems could be the best around with user friendly and intuitive functionality, but if the data is not current, correct and duplicate free then users will see that very quickly.
User adoption is such an important performance indicator that it is commonly used as an indicator of system performance. According to a Gleansight study on Business Intelligence, 79% of top companies use the number of active users as a common metric for measuring ROI on BI solution investments.
As Rowland Dexter noted in the above quote, low system confidence leads to mistakes and mistakes lead to lower system confidence. With an average of 60% of companies in the Experian study citing human error as a reason for bad data quality, the importance of preventing this downward spiral could not be clearer.
Employees need to be using systems correctly and taking care to prevent mistakes for your business systems to be reliable at producing insights and tracking the state of your business.
Less Informed Decisions
“The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.
Having a lot of data about your business is the best way to make informed decisions right? The problem comes in when your data is inaccurate, incomplete or duplicated.
More than 80% of organisations who use their data for business intelligence had problems generating meaningful analytics. 40% blamed inaccurate information while 29% blamed insufficient data.
If you’re planning on using your data to craft future strategies or make decisions about future developments, you need to make sure that your data is correct. Decisions made based on inaccurate data are as ill-fated as decisions made based on guesses and assumptions.
By cluttering up your databases with false leads and statistics, bad quality data is derailing your ability to make informed judgements and decisions about the state and future of your business.
There are four key steps to cleaning up your bad data.
- Identify – use a data profiling or audit tool to identify types and locations of data defects
- Resolve – use a cleansing tool to clean data, remove errors and fix basic problems
- Prevent – use real-time safeguards to prevent new errors from entering the system
- Maintain – appoint a data steward to be responsible for long-term monitoring, measurement and management of data quality, only 30% of companies do, and you should be one of them
Studies show that fewer organisations in 2014, compared to 2013, used automated methods to check data at the point of capture or clean it after submission and the number of errors or inaccuracies rose accordingly.
Don’t let bad data ruin your business. By crafting a data management strategy that covers all four of these areas, you will set the best course for clean data and business success.