Feb 09

UK Data Protection Rules Still to be Clarified Following Brexit

Many issues have emerged from the Brexit vote last summer and possible changes to the UK’s data protection rules is one point that needs to be clarified.

Well, UK digital minister Matt Hancock has recently spoken to a House of Lords Home Affairs sub-committee on this very matter. Hancock said during the session that the country’s laws on this subject are most likely going to closely reflect the existing European Union data protection legislation.

This would mean that Brexit wouldn’t really result in any great changes in the way that information is able to move back and forward between the UK and EU countries. In fact, the minister said that the British government is keen to “ensure unhindered data flows” in the aftermath of the referendum last summer.

He also confirmed that these data flows should also be “uninterrupted”. When asked directly whether this meant that there would be no big data protection cut-off date from Europe he replaced succinctly with the word “exactly”.

Hancock was reluctant to get into specific details on the matter, quoting the Primer Minister’s view that they don’t want to undermine the country’s negotiations with the EU. However, it is expected that British politicians will look to maintain the data flow with those other countries that the European Union has agreements in place with, as well as with the EU.

For instance, the EU-US Privacy Shield deal will have to be discussed, as Britain will no longer be covered by this agreement following the conclusion of Brexit arrangements.

So, it appears that data protection rules and laws in this country will be largely in line with those of our European counterparts. However, it remains unclear which agency will be responsible for arbitrating any issues or conflicts that arise, as the UK will no longer be under the jurisdiction of the European Court of Justice.

Despite being urged to provide some more details on the various options being considered in terms of implementing UK data protection rules, Hancock would not be drawn on the “pros and cons of various different arrangements” that could be put in place eventually. Neither would be comment on what could happen if they are unable to get a suitable agreement in place with the EU countries.

Of course, no matter how this issue is eventually resolved, keeping their data under control should be one of the main concerns of any company. One way of ensuring a better use of data is by using trustworthy protection tools to keep your databases relevant and up to date all the time.

By allying data matching and data cleansing tools to a rigorous staff training process is it possible to feel confident about handling any changes to the data protection rules that come along. With a manageable and controlled customer database there is no longer any need to worry about what information you might hold and not be fully aware of.

Take the first steps towards a better quality of data by looking at the current data cleansing software that we offer.

Mar 01

The Benefits of Data Matching

data matchingScrubbing the data on mailing lists, sales lists, etc. is another tool to keep duplicate records from choking your business. The process to accomplish this is called Data Matching. The simple definition is to compare two or more sets of collected data (excel spreadsheets for example) to remove duplicated information and consolidate records used by a business. Removing duplicated data can streamline business practices and save money. By eliminating multiple lists, telephone sales personnel will not waste time on duplicating cold calls to potential clients. This also eliminates sales people who make only cold calls interfering with more senior personnel engaged in follow-up calls.

Data Matching For Efficiency

This same consolidated list can also save thousands of dollars every year in mail-outs. By providing the marketing department with a streamlined list of clients and potential clients, fliers and other advertisements can go out to only one per household instead of three or four. When multiple cards arrive, the person who gathers the mail will read one and then throw away the rest.

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It’s not just a matter of saving money however. Data matching of client records is important for companies seeking to provide exceptional customer service. On most occasions, a customer that places a call to an insurance company or a bank will speak to multiple departments and multiple personnel. For example, if the correct address does not match between what the auto insurance department has with what the life insurance department has on file, customer service can be delayed and information can be sent to multiple addresses. This can be even more important to a customer seeking a loan or trying to conduct other banking business over the phone.

Data matching programs can be run on a routine basis to provide this financial and customer support. A simple program can take this data from multiple records with lists maintained by various departments to ensure the most up-to-date data is shared. Not every piece of data can match 100% however. This is where Fuzzy Matching comes into play.

Fuzzy Matching

Fuzzy matching operates on a similar principle as data matching, but does not require an exact match to consolidate data; rather, it works on the probability of a match. For example, a fuzzy matching algorithm used to consolidate data can draw matches for a particular company from outside sources by using recognized abbreviations for the company and even misspellings of the name. The requirements can be adjusted by the user of the algorithm depending on the requirement. Gathering sales data could be a lower match; say a 75 percent probability of a match. Consolidating client data might be higher; beginning at 90% probability.

Data matching is a necessity for a business of any size. Conserving budget dollars and increasing customer support is important for every company from a national call centre to a local hospital. Using both data matching programs and fuzzy matching algorithms ensures that each can provide the best service possible. Download a free demo of our award-winning WinPure Clean & Match software and try out its powerful data and fuzzy matching features today.

Feb 29

What is the Difference Between Data and Information?

difference between data and informationMany people hear the words “data” and “information”, and think the terms can be used interchangeably. In fact, data and information aren’t the same at all, there is a distinct difference between the two words.

In order to correctly recognize and use either one, it’s important to know the difference between data and information.

Data vs Information

Data

Data is a collection of figures and facts, and is raw, unprocessed, and unorganized. The Latin root of the word “data” means “something given”, which is a good way to look at it. Individuals and organizations can’t do much with unprocessed data because it’s so random. Once data is given structure, organized in a cohesive way, and is able to be interpreted or communicated, it becomes information.

Example of Data

J,Smith,123 King St, London, UK, 0202656788

Information

Information isn’t just data that’s been neatly filed away, it has to be ordered in a way that gives meaning and context. This is what allows people to use data for reasoning, calculations, and other processes. With that said, data’s importance lies in the fact that it’s a building block. Without it, information can’t be created.

Example of Information

John Smith
123 King Street
London, United Kingdom
(020) 2656788

To simplify this concept think of it like this:

Data has no meaning until it’s turned into information. In order for people to interpret data or make any use of it, it must be understood. For instance, a company’s sales figure for one month is a piece of data that’s meaningless because it has no context. It tells nothing, and there’s little that anyone can do with it as is. However, if one were to take a business’s sales figures from three months and average that number, we’d be able to derive many bits of information from that data. When one has incomplete data, it’s highly likely that it will be misinterpreted and lead to the development of misinformation. For example, suppose someone saw that his business’s sales were up by 4%, and he drew the conclusion that his current marketing campaign was working well. However, if he found out that a competitor who sold the same products had a sales increase of 16% during the same time period, he’d start to question just how well his campaign really performed and would want to gather more facts (data) to analyze the situation again.

Data Quality

Data quality refers to whether data is useful to make decisions, calculations, or plans. Basically, good quality data is reliable, accurate, relevant, consistent, and appropriate for a given context. Using a good data quality software tool such as WinPure Clean & Match are designed to ensure that business data is as reliable as possible so that it can provide a solid basis for effective decision-making.

 

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