Picture Perfect Customer Data

In today’s data-driven world, with so many points of interaction with customers and so many data points about customers, pointing to a consistent and comprehensive representation of customer data is a complex challenge. This is why maintaining a customer relationship management (CRM) system with a current and complete picture of every customer is more important than ever. Your business needs every customer interaction to have the benefit of accurate, up-to-date data, such as name, postal address, telephone number, and email address. Unfortunately, even when you achieve accurate, up-to-date data it doesn’t stay that way because data decays. Customers get married, move, change phone numbers, and switch their primary email address. Snapshots of customer data are taken from different sources, which do not change at the same time, and not all customer data attributes within a source stay up-to-date. For example, a call center might be the best source for telephone number, while a billing system could be the best source for postal address, and confirmations sent by a self-service web portal may verify the best email address. External reference databases can also provide alternative […]

Data Cleansing and Enhancement

If you follow the DQ Global blog, you’ll be aware that we often write articles on data decay: the rate at which data becomes unusable. This is one aspect of the challenge businesses face as they build contact databases. While data quality is partly affected by the method of capture, data is also subject to natural decay that will cause a database to become less useful over time – even if data is captured correctly on day one. Data decay is a costly problem that no business can escape; the cost of fixing broken databases increases as the data becomes more decayed. Additionally, the cost increases if the record is left unchecked. According to DQ Global estimates, 20 to 30 % of business data decays each year, which means most business databases will unavoidably suffer without robust data quality initiatives. Our numbers relate specifically to B2B data, including details of companies and employees. When tackling data decay, there are two key processes that will heal the dataset and […]

Data Quality – who’s responsible?

The answer is – everyone in the business.  Unfortunately, all too often no one seems to take responsibility or realise its value. So just why is data quality so undervalued?  The underlying reason is that there is a denial mindset regarding ownership of the data content.  In many organisations, IT builds and owns the database container and the data users take ownership of the content, or perhaps not.  It’s the classic ‘Everybody, Somebody, Anybody, Nobody’ no ownership story: Data quality management is an important job and Everybody is sure that Somebody will do it.  Anybody can do it, but Nobody does.  Somebody got angry about that because it was Everybody’s job. Everybody thinks that Anybody can do it, but Nobody realises that Everybody won’t do it. In the end, Everybody blames Somebody when Nobody does what Anybody could do. Accurate data is undoubtedly the cornerstone of industry, but a lack of standardised data prevents efficient information exchange between departments and subsidiaries and impedes decision-making and understanding of business problems.  Paradoxically, many businesses have masses […]

Why Data Should Be a Business Asset – The 1-10-100 Rule

If you run a business, or even just run a household, you’ll understand that chaos creates waste. How many times have you rushed to find an important document, such as a birth certificate, only to have to pay out for an emergency replacement? How many hours have you wasted searching for lost keys, lost passports and lost letters? How many times have you wasted money on an emergency callout for a problem that could have been located and fixed? Just as chaos at home is disruptive, disorganisation and chaos can have huge effects on business profitability. If employees don’t work to conventions, they risk creating problems for themselves, and for other employees down the line. Use of IT systems is strictly controlled for precisely this reason, and businesses work hard to ensure that data is stored in a controlled way. Otherwise, the chaos spreads like a virus. But what happens when data is captured correctly, then naturally decays? Even the best laid plans cannot protect against this inevitable, and costly, data […]

What Good Data Means to a Busy Marketer

When discussing data quality, marketers have an interesting point of view. They don’t view information in terms of mass trends, even though that’s essentially what a market is.         Every marketer has five key concerns: How will I retain customers? How will I build trust with clients? How will I meet targets? How will I meet deadlines? How will I ensure profitable outcomes for the people that pay my salary? The marketer has a single, personalised view of prospects and wants to deliver the right message at exactly the right time. In order to close the loop, the marketer needs to ensure they get meaningful data as a foundation of a marketing campaign. They are utterly reliant on data quality to produce accurate reports and improve customer satisfaction. Planning, Not Fixing At DQ Global®, we often hear data being blamed for poor marketing campaigns once they’ve failed. Despite this, businesses frequently fail to address data quality challenges before the campaign begins. For marketers, quality data is important. They need information that is: Fit for purpose Relevant Trusted Absolute Accuracy in Data Marketers need to know that data is reliable before the campaign begins. To […]

Common CRM Data Problems

When setting up a CRM, businesses make the mistake of thinking their data is evergreen. But data that is fit for purpose today will not stay that way forever.           When was the last time you checked your data for C.R.A.P? Is it Corrupt, or is it Correct? Is it Rubbish, or has it been Re-purposed? Is it Abortive, or is it Appropriate? Is it Poor, or is it Perfect? Data quality research reveals some startling statistics about, well, C.R.A.P. In the average business contact database, up to one third of the data could be of poor quality. That means a relatively small database with 50,000 customer records could contain 15,000 that are flawed in some way. But it’s important not to be downhearted by the challenge. Many of these flaws are caused by extremely common problems. If your business is suffering from data decay, it’s in the majority; most area. Once you realise the problem, take ownership and look positively at […]
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    Data is the future and our aim is to make your data fit for business

Data is the future and our aim is to make your data fit for business

Data is the future…and our aim is to make your ‘Data Fit or Business’ At DQ Global® we have been developing our Data Quality Software for over 20 years and we have gathered a wealth of knowledge and experience in that time. Working with DQ Global®, clients across a variety of countries and industries have capitalised on their investment in business data and CRM systems. Armed with accurate and reliable data our customers have been able to achieve a single customer view, improve operational efficiency, make better informed decisions and improve customer engagement. . Take a look at our new video and contact us for more information on our Data Quality Software.

What is the Difference Between Data and Information?

When discussing data quality, we need to understand exactly what we mean by the word data. Often, the words information and data are used interchangeably, yet they are not the same thing.   Data is, or are (depending on your knowledge of Latin), fundamental to business intelligence. But how do we recognise data as data – and why is bad data such a pernicious entity? First Things First: Data vs Information There’s a really simple way to understand the difference between data and information. When we understand the primary function of the item we are looking at, we quickly see the distinction between the two. Here’s a simple way to tell one from the other: Computers need data. Humans need information. Data is a building block. Information gives meaning and context. In essence, data is raw. It has not been shaped, processed or interpreted. It is a series of 1s and […]

The Ethics and Benefits of Record Matching

Everything we do generates data. When you found this blog, your search query was recorded; your click from social media saved to a log file. Your location may be saved alongside it. Each one of us is connected to the internet via a machine with a unique MAC address code and a non-unique, but traceable, IP address; we collaborate using specific credentials like email addresses, and the times and dates of communications are relatively easy to compile. Businesses are increasingly looking towards big data for answers to their big questions. By drawing on massive amounts of data, leaders hope that they will unlock the data secrets and turn it into information about consumer behavior and reveal more opportunities to profit. Of course, this is not a foolproof process. But the rewards are potentially massive. McKinsey says that the US healthcare industry could save $300bn a year, that’s 1,000 per American per year by making better use of big data. Companies like Google have tracked the spread of disease by collating […]

Data Quality Through the Johari Window

The Johari Window is a well known model developed by Joseph Luft and Harry Ingham in 1955 (‘Joseph’ and ‘Harry’ combine to create the rather exotic-sounding ‘Johari’). This model used for a variety of purposes. From personal improvement to team building, the Johari Window illustrates a common problem: a lack of knowledge. More specifically, it helps us to realise our ‘unknown unknowns’. For the purposes of this blog, the Johari model will help us understand how data quality becomes neglected so that we can identify a plan of action. What is the Johari Window? The aim of the Johari Window is to allow the subject to see what they cannot already see. Then, they take steps to increase their knowledge. At the end of the analysis, the subject knows more and has reduced the amount of knowledge that is lacking. Without a diagram, it is difficult to visualise exactly what this means. This simple […]