Data Quality | The 3 most commonly made mistakes | Improve KPI's

Improve your data quality and you improve your business performance

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Passionned Group is a leading analyst and consultancy firm specialized in Business Analytics and Business Intelligence. Our passionate advisors assist many organizations in selecting the best Business Analytics Software and applications. Every two years we organize the election of the smartest company.

Data quality is paramount to success

The return on investments in CRM, Business Intelligence, Analytics, performance management or data integration hinges on your data quality. How do you increase the data quality of regular data, but also that of Big Data, and what tools, methods and measures work?

It is a known fact: garbage in, garbage out. Despite the broad recognition of this idea, it is difficult for most organisations to continuously improve the quality of the data. This not only has to do with inadequate IT. The attitude and behavior of your employees and managers also play an important role in this. The specialists from Passionned Group would love to assist you with the improvement of your data quality.

Not a vague concept, but well measurable

Not a vague concept, but well measurable

Data quality is not a vague concept. It’s factually measurable, however not simple. Data quality is a term that is widely discussed, also when it concerns customer data. It is often seen as an abstract concept, which is difficult to measure. Difficult however does not make it impossible.

There are different ways to express the outcome of a data quality measurement:

  • Accuracy: think for example of outdated addresses or spelling mistakes in names and addresses.
  • Completeness: is all data entered correctly? This applies specifically to data that is relevant to the proper execution of the process.
  • Conformity: Does the content of the data comply with the standard patterns which are agreed on within the organisation? Think for instance of capturing phone numbers or bank account numbers.
  • Consistency: Does the data correlate? For example, is the same type of data recorded for certain types of companies.
  • Duplicates: customers are often duplicated in customer files. The information in the various records is often different.
  • Integrity: missing links between data elements, such as codes that are used incorrectly. An example of this is the gender of a customer, there is sometimes a blank space in the database.

Invest in high quality customer data and conduct regular checks on the quality aspects of various data. There are many external (paid) resources and tools available that can be used. Think for instance of postal code tables or systems to verify the spelling of customer names.

When data quality is measurable, it can also be improved

Our approach focuses primarily on teaching your employees the skills necessary to continuously improve data quality. And on making the proper tools available to them. So that they can achieve better results, as individual, but also as a team. So they have continuous insight into what data quality is, and how and where to improve it. Concerning your customer data, but also various kinds of product and process data. One of the tools offered by the Passionned Group is the well-known Deming cycle: plan do check act.

Data quality objectives are formulated in an action plan. This is done in a SMART way, in order to make it measurable. Key performance indicators (KPI’s) are often used and a dashboard is set up for this purpose. Then you set standards on the indicators, the so-called target values. For example, 98% of the contact details of your customers are up to date and accurate.

Through analyses and relevant insights in the implementation, you can see which data is not yet accurate and how this can be corrected. Either once-off or periodically, manually or automated. You compare the indicators with the standards and the results are analysed and discussed in the PDCA check phase. In the last phase of the improvement cycle you update the standards and targets for data quality and adjust the implementation.

Data quality has many aspects

Data quality has many aspects which are all extremely different in nature. What they have in common is that improper use leads to loss of reputation and a large number of customers walking away. It is an asset that requires excellent protection.

  • Privacy is an important aspect. Customers entrust their data to you, as organization, and implicitly assume that this will be treated confidentially. How does an organisation deal with data and what is it being used for? An example of where this was used incorrectly is when some of the larger banks announced that they wanted to sell customer information to external parties as an additional source if income. This created an uproar.
  • Security is another very important aspect. Every day there are news reports on hacks and customer data being stolen from a database of an (online) company. This leads to major reputation damage of the company and even puts the survival of the company at risk.
  • Transparency is becoming increasingly important. Which customer data do we store and which data is shared with external parties. Google is probably the best known company that collects enormous amounts of information about customers. It is often unclear exactly which data is stored and what it is used for.

These aspects are becoming increasingly important and are therefore constantly on the agendas of all managements and executive boards of organisations.

The three types of data: zero data, open data and big data

Data comes in many forms. Big data is the most important and well-known trend. This is data that isThree types of data: zero data, open data and big data actually too big or too complex for a regular database management system.

The definition is certainly not unambiguous, but we believe it consists of the following three important factors: volume, velocity and variety. We consider it Big data if at least two of these conditions are met.

  • Big data: is a very important trend in the current data-driven economy. The purpose of big data is to get new insights into, among other things, customer data, through statistical analysis. It is figuratively like looking for a needle in a haystack. If you have big data then you probably have a big data quality problem! This certainly applies to human generated content.
  • Open data: is a term by which data is referred to as freely available data. With open data the aim is to reuse this data as much as possible. Open data is often ‘produced’ by governments and research institutes. Through linking this open data to current files within organisations the customer data will be enhanced and more insights are possible.
  • Zero data: many organisations focus on big data, but they often forget that a lot of data is also available internally. This is however rarely used. A lot of knowledge and information can be accrued by specifically looking at data that is not available. This is in fact called zero data. A good example of this is a newspaper company that had many subscribers in the age group of 45+, but was hardly represented in the age group below 45. Due to the lack of this target group, it became apparent that they had to add another proposition to prevent the subscriber base from ageing.

The three most commonly made mistakes

  1. Data quality is a once-off exercise and not a continuous improvement process. When the acute need for good data has been met it is removed from the agenda. This however still gets you nowhere.
  2. Data is not yet seen as a strategic asset. This leads to ad hoc attention to it, but is never structural. Continuously monitoring data quality and reporting on this could lead to change.
  3. Data quality is seen as something that IT should control. The responsibility is not invested properly, and consequently, the continuous improvement of data quality can never really take off.

Actually increasing your data quality?

We have more than 25 years of experience in implementing data quality programs. With our no nonsense approach at implementing solutions, we work with you to increase data quality, which benefits you and your customer.

If you would like to exchange ideas or talk about the challenges you face regarding data quality, make an appointment for an orientation conversation. We look forward to explaining our approach and the results you can expect.

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