In today's digital world, more data is being created and shared every single day. In fact, a report from IBM found that 90% of the world's data was created only in the last two years.
Businesses can use this wide range of information to their advantage in many ways, not least to help them make more informed and better decisions. However, the issue here is that without ensuring the data is of a high quality, you risk making the wrong choices for your business, as well as running ineffective marketing campaigns, for example.
Ultimately, having bad data in your databases and systems is very likely to end up costing you. IBM estimates that poor quality data is currently costing the US economy around $3.1 trillion every year, purely through bad decisions being made thanks to bad insights.
Then there are the losses to your employees' time, as well as the frustration of having to scour the net to replace missing or incorrect data. Research from CrowdFlower found that data scientists are spending 60% of their time cleaning and organising data.
Finally, having incorrect data in your systems could mean you end up with a false representation of how your company is currently performing, and unachievable or too attainable goals will be set as a result.
Image Source: RingLead
Using good quality data is the key here. As the old adage goes, prevention is better than cure, and this is certainly true for data. You need to ensure that the data is as accurate as possible before it gets entered into your system and ends up causing problems; not to mention the fact that carrying out a full data cleanse is expensive and time-consuming.
The Global Database platform uses only high-quality data. It provides extensive company insights based on information taken from reputable resources and then subjected to thorough and consistent accuracy checks. This is particularly important in the world of business, where numerous changes occur every single minute. Our platform deals with this challenge by automatically updating records in real-time, so we can always provide our users with data of a superior quality.
Ensuring the data is accurate and up to date is important wherever it's being used. Today, there is no type of organisation or business that is completely untouched by data. With complex and large volumes of information being used for a multitude of purposes, the role of data quality is bigger than ever.
For example, data quality in the healthcare industry is often a matter of life and death. Give a patient the wrong medication, or the wrong dose, and it can have tragic consequences. This is why it is essential that data entered on tablets or via cloud systems is done so correctly.
The effects of bad quality data can be seen in politics, too. Some parties rely heavily on predictive models to tell them how well they're doing in certain areas, and therefore where they need to focus their attentions on. However, this can have disastrous implications if the data being used for the predictions is incorrect to begin with - it could mean the difference between winning and losing an election.
For any business, regardless of size or industry, ensuring data quality in data warehouse is of vital importance. The rapidly changing nature of the business world means there are consistent alterations to companies and individuals. Having quality data in a business respect is comprised of five key components:
This is simply making sure that there is no missing data from that which was collected.
Having gaps in the data can have a negative impact on your business decisions by not giving you a clear or accurate insight. This is particularly important when it comes to things like predictive analysis. Global Database ensures how data is as complete as possible by only gathering it from authoritative sources, and a wide variety of them.
This is ensuring that the data is entered in the same format across the data set, as well as using the same type of data.
Having inconsistent data in your system, for example using different currency signs or entering numerical data in a different way, can mean you're unable to carry out proper analysis. At Global Database we leave very little room for human error, thanks to our patented web crawling technology that automatically adds and updates records.
It's essential to ensure that the data sourced is relevant and correct.
Without doing so, you could target the wrong people in a marketing campaign, or lose potential customers by sending your messages to the wrong email address. Global Database ensures accuracy by running consistent checks on all of our data, including monthly email validations.
Making sure you have valid data is largely down to the process of collection.
Global Database not only utilises intelligent software to automatically update all of our information, but we also have a team of in-house staff to manually check everything. This ensures that any invalid results are unlikely to be due to the accuracy of our data.
This revolves around the data you're using being up to date.
Having stale data in your systems can be very detrimental to your business operations. You could be sending marketing materials to the wrong people, or find that you're no longer able to reach out to a particular business contact. Global Database provides daily updates for all our datasets, to ensure our data is always kept as fresh as possible.
27% of data in the world’s leading companies is flawed, according to Gartner.
Over $6 billion dollars is lost annually by US companies as the result of poor customer data, as notes TDWI.
There are 80 new businesses set up in the UK every single hour, according to research from StartUp Britain.
Data Quality Solutions estimates that bad data costs many companies between 15 and 25 percent of their revenue.
A Forbes/KPMG study found that 84% of CEOs surveyed were concerned about the quality of the data they were using to base decisions on.
Image source: Celsius International
At Global Database we have a stringent commitment to data quality. We realise how important accurate business insights are, and how quickly things can change. This is why we have put a number of systems and processes in place to ensure that our data is as accurate as possible. These include:
Our online platform uses our own powerful web-crawling technology to update and add information every single day, and in real-time. This ensures that our data stays as current and accurate as possible, and also provides our users with new potential business leads constantly.
You can request to be notified of any changes to a particular record, and we’ll let you know via email whenever the company information is altered. This allows you to react in a much more timely manner and means you can update your own database or alter your sales strategy accordingly.
Regardless of how advanced our technology is, we appreciate that it still can't compare to manual checks from human eyes. This is why we have a team of over 100 staff members, who manually verify all of our information on a daily basis.
They ensure that our data is collected and stored correctly, and perform checks on all of our phone numbers and email addresses to make sure they're still being used, and reach the right person. Of course, this is a very time-consuming process, so our team are able to take on the task on your behalf, enabling yours to focus on more pressing issues like closing deals.
At Global Database we value our good reputation and know that our users are putting their trust in our data quality. With this in mind, we only ever partner with respectable data providers. The types of data we usually obtain through these partnerships are things like which technologies a company is currently using, how much traffic their website is getting and which keywords they're ranking for.
This information is often hard to come by, but extremely useful, whether you're qualifying leads, comparing competitors or performing background checks. With our platform, you have it all at your fingertips for thousands of organisations, and you can rest assured it's always as accurate and up to date as possible.
While data quality may seem to many like something that only really affects large businesses that have enormous and very complex datasets, the reality is that having low quality data can also have an extremely detrimental effect on small businesses. Firstly, if you're basing your decisions on bad data, things obviously aren't going to end well. This can mean failed marketing campaigns, unrealistic targets and even entering the wrong markets for your products or services.
Knowing your target market is essential no matter how large or small your company is, but in order to do so, you need the right data. Small businesses often get nonchalant when it comes to keeping records of their previous customers, but without doing so you lose out on possible opportunities to upsell or get the greatest possible lifetime value out of a client.
Having bad data can also be as simple as contacting the wrong people thanks to errors in contact information. This not only means you miss out on a possible sale, but also all of these undelivered emails will add to your expenses and even cause you problems with being marked as a spammer.
Global Database provides a wide range of business intelligence which can be extremely useful for small businesses.
Firstly, there's the accurate contact data we offer; thousands of phone numbers and email addresses for key contacts within every sector and across 195 countries.
Being a small business with limited funds and only a few employees means that it can often take a considerable amount of time to build a sufficient mailing list organically, and manually looking up all of these details just takes up too much of your precious resources. Having this contact data at your fingertips in an instant saves you time and money in the long run.
We've also made much of our company data publicly-available, so even if you don't currently have room in your budget to buy a subscription, you're still able to access and use a wide range of business insights for organisations in countries like the UK and Singapore. This data includes things like financial information, technology insights, employee details and much more, allowing you to fuel your business decisions and grow your company quicker. You can also rest assured that our open source data quality is just as high as our paid data.
Data quality management is the process of checking your data to ensure that it is accurate enough to be effective. It involves making sure that the right data types, values and formats are being used, as well as the right codes. DQM can also be used to correct these potential errors by formatting dates and numbers and checking for default values. It is used from the moment the data is collected to when it is stored and eventually processed.
In order to implement the process of data quality management, you need to first set DQM rules. These rules can carry out multiple different operations on specific data values, whether it be preparing or putting things in a standardised format, for example. Ultimately the goals that you put in place are based on your own business needs and aims, so where one company might decide that 5% wrong phone numbers is acceptable, a different organisation might put the limit at 10%.
While data quality tools were once limited to those that scoured the dataset for simple mistakes like incorrect spellings, there are now a wide range of tools available, with several different aims and purposes. These include:
Standardisation - These tools ensure that all of the values entered into your database or system follow the same formatting rules, taking into account things like standards for your industry, geographical standards, e.g. how addresses are laid out in your particular region, as well as the rules that you've set for the data in order to ensure it all flows and follows the same patterns.
Matching - Putting records and entries that follow the same rules and match together, or simply identifying them to the user. This can either be done in one dataset or even across multiple sets.
Profiling - These tools analyse the data in order to find statistics which give an insight into how clean the data is in the set, and also help to identify any problems related to the quality of the data.
Monitoring - These tools keep track of the data in terms of ensuring that it is consistently adhering to the business rules which have been set out by the company to keep the data as high quality as possible.
General Cleansing - These tools are for the alteration of specific values of data in order to make them compliant with certain restrictions in the domain, or other business data quality rules which the user has determined are needed in order for the data to be considered properly clean.
Enrichment - These tools are designed to help you gain more insights from your own internally generated data by providing more in-depth data or putting it into context.
In order to ensure that your business is committed to effective data quality management, we advise following these four best practices:
Having good quality data undoubtedly means keeping track of the information you hold, and checking it every now and again to ensure accuracy. Of course, you should be making sure that you're only taking data from reliable sources, and checking it before it gets put into the system, but there's still the issue of change. You need to know when someone gets a new email address or an important business contact gets promoted.
Having a good data quality culture starts with having the right people on board. As well as hiring someone to oversee your whole data strategy, it's important that each member of your team knows their own role in ensuring data quality. It's vital that everyone enters data into the database in the same format, for example, as well as things like knowing which resources are acceptable to use, and the process of reporting a data breach.
Businesses who are wanting to set up a real commitment to data quality management might want to consider formulating a data governance board. This group of employees should be taken from across the whole business, and it will be their responsibility to ensure that the data quality strategy is set, and targets are realistic and regularly monitored.
Creating an efficient virtual firewall will help to identify bad data, as well as making sure it is stopped from entering the system. Any data that is labelled unclean or incorrect will be pinged back to where it originally came from, or altered so that it is made complete or error-free. Only then will it be allowed to enter the system, protecting your database as a result.
Enterprise Data Management (EDM) is the process of removing any problems in the managing of data through the implementation of a rigid data delivery strategy. This involves being able to securely and efficiently send data to partners, apps, and consumers, among others. In order to be implemented successfully, EDM requires several departments in a business to be involved, including finance and IT.
The Global Database platform implements effective enterprise data management, with a complex and variety-filled number of processes and technologies on hand in order to ensure that data is handled effectively. This means that we are able to keep our data safe, and clean, throughout every stage of its journey; from its collection and processing to the moment it is downloaded by our users.
While to some, data quality might be defined as having absolutely no errors in a dataset, the reality is that is virtually impossible. However, what is important is that the data you're collecting and using adheres to the standards that your business has set. With this in mind, you should first determine who in your organisation will set these requirements, how they decide on them, and how much leeway there is in meeting them.
When you've established that there is an error in your dataset, you can approach it in several different ways:
Accept - If the error isn't so big and isn't going to have an undesirable impact on your system or the insights it generates, you can simply choose to ignore it.
Reject - If, on the other hand, it's a big error wherein the value entered doesn't make sense and cannot be corrected, it's a good idea to bin it instead.
Fix - If it's a simple misspelling or incorrectly entered value, you can fix the error there and then.
Data profiling is the process of looking at the data in your dataset in order to establish whether or not it is accurate, and if there is any information missing. If so, you would then decide what to do about these data quality issues. While it's a simple enough process to ensure that your own internally generated data is present and correct, for example, a list of your current products, doing the same for third-party data can be much more challenging.
Using data profiling you are able to determine how 'clean' your data is by looking at things like uniformity. For example, are dates all entered using the same format? It can also help you to identify duplicate records and entries or other problems in your newly-collected dataset. Therefore, data profiling is a great place to start when it comes to ensuring that you're working with the best possible data.
When you're sure that the data that you have collected is of a good standard, you can use it effectively in developing customer analytics. This process allows you to find the perfect customers to target, and to ensure you're sending them relevant offers and the products for their needs.
You should start by segmenting your customers. This is simply dividing the customer base into specific groups based on certain criteria, for example their age or salary. In order to complete this process successfully, you should make sure that each segment is big enough to bring in a sufficient revenue, so don't make the groups too narrow.
The criteria you choose could be based on demographic details, e.g. gender, age and job, behavioural, e.g. online activity or use of products, or psychographics, e.g. their lifestyle and interests and hobbies.
Number of empty values - If you have a large number of empty fields within a dataset, it will probably need to be added to before it can be put to good use.
Email bounce rates - An unusually large number of bounces in an email campaign is likely to be a sign that the contact data you’re working with needs a closer look.
Data to errors ratio - This metric enables you to monitor how many errors there are in relation to how big the dataset is.
If you’re in the process of improving data quality within your organisation, you have two options. You can perform a manual cleanse, or an electronic cleanse.
A manual cleanse involves a member of staff looking through the dataset by eye in order to look for duplicates, errors or missing information. This type of check is potentially very thorough, but can be extremely time-consuming depending on how much data you're holding.
An electronic cleanse on the other hand is much more time-efficient, but it is limited to things that can be checked by a machine. Ideally, a combination of both should be employed to get the best results, which is why we utilise both patented technology and a team of in-house researchers at Global Database.
Having access to quality data is integral to a wide range of business functions. Ultimately, without it business intelligence is meaningless; it's clean, accurate data that provides useful insights.
Global Database has a strict line on data quality; we carry out consistent verifications, collect data from multiple sources to ensure completeness and accuracy, and only use reputable resources. This is why users from all industries and organisation types have come to rely on the business intelligence that we provide.
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