Top 10 data management tips for all organisations

05 October 2021
Data Management
3 reading minutes

Top 10 data management tips for all organisations

05 October 2021

Businesses need data°like plants need water. Data is here to stay. More and more data is becoming available, not just within organisations but also beyond them, such as weather data, data from loyalty programmes and data via sensors. However, more data does not always equal better data. This is exactly why Data Management is becoming ever more important.

Drawn from our experience and market knowledge, here is a list of the top ten most common data management challenges, and tips to overcome these challenges. You will undoubtedly recognise some aspects that you are aware of within your own organisation. Depending on the maturity of your data management policy, certain items will be present to a greater or lesser extent.

The 10 most common data management challenges

1.     Duplicate data

When there is duplicate data about customers, products or suppliers, it is very difficult to work unambiguously and to follow your processes. A simple example: customer Jansen Houtfabriek appears as a debtor three times in the ERP system. Jansen Houtfabriek places a new sales order; against which debtor should the internal Sales Department process this order?

It is absolutely essential to work with a single gold standard record; also known as the single version of the truth.

2.     Data located in different systems

How often do you hear someone say: “I need to look that up in another system.”
For example, when you call customer service, they can’t see all your customer data at a glance. So they have to consult two or more systems to check the status of your help desk ticket or query.

The lack of a 360° view of customer data (or product data, or supplier data) is highly impractical and causes delays in making the right decisions. Once you sort out your data and start working with a good data management system, this problem is easily solved.

3.     Wrong decisions based on incorrect data

Of course, the pithy statement “Shit in = Shit out” is really at the heart of many data issues. When data is input incorrectly, BI reporting, analytics and insights will not be much use. In fact, they will likely lead to wrong decisions that may cause a loss of turnover and margin, or worse: damage to your reputation.°°

An undesirable side effect is that departments create their own variants of these reports, resulting in inconsistent data. Here too, creating a single version of the truth helps to impose order on the data.°

4.     No data ownership

When data doesn’t have an owner, in most organisations nobody really feels responsible for data quality and keeping it up-to-date. Monitoring mutual data quality agreements will also occur rarely, if ever.

Appointing data stewards is one possible solution. This ensures that data is collected, managed and catalogued correctly.

5.     Inefficient data distribution

How often do you receive requests for data to be provided to customers, dealers, production partners or internally to management?

“Please supply data records for the last 80 sales orders from the customer Jansen Houtfabriek, where we need these 14 fields and column structure XYZ.”

In particular, poor availability, lack of a uniform format and their ad hoc character make data requests like this very difficult, and above all inefficient.

6.     Lack of data standards

Do you mean red, or burgundy red? Do you want stainless steel, or brushed stainless steel?

These may seem like details, but working with data standards (ETIM and GS1 are examples of industry-specific standards) is necessary so that everyone speaks the same language. If you do not have proper agreements on this in place with suppliers, dealers, production partners and above all internally, these types of questions will be asked over and over again. Working with data standards, such as codes for colours, materials and finishes, is essential.

7.     Outdated data

Cleaning up and deleting old data is an important process and often a major task. This process is known as data life-cycle management. For example, there should be clear rules and agreements about products that may be End Of Life (EOL).
In that case, what do you do about support for EOL products?
Where do you securely store the old FAQs that were still online for them?

Another example of outdated data is notification of a change of address. This is often done online using a form. If this change is only processed in CRM, but for example not in a cash register or loyalty card system, it can have negative consequences for the customer.

8.     Process errors caused by faulty data

There are so many examples of this:

  • Returns due to incomplete product data (wrong dimensions) on the website.
  • Incorrect purchase orders as a result of a blank field: Minimum Order Quantity.
  • Incorrect invoices due to incorrect customer data.

9.     Lack of awareness of cause and effect

A lack of specific process knowledge can cause data modifications that have unintended consequences. A simple example: in an organisation, customer data is held in four systems. When you change a data object, you must do so consistently in the other three systems too. For example, when an email address changes, you run the risk that the next mailing will be sent to the old email address.

10. Lack of tools for data maintenance

A common comment is: “I do this as an extra job, alongside all my other tasks.”
Often there is not enough time or capacity available to maintain the data. This often results in overtime, or bringing in external workers.°
Ultimately, this makes the costs much higher, and the risk of making mistakes much greater. Partly due to high pressure of work, and partly because the external people brought in do not have the right knowledge and skills.

By making an employee responsible for data, such as a Chief Data Officer, you free up other employees to work on their core business. The CDO manages the data, reducing errors and therefore costs.

Makes sense, but where do I start?

Many organisations do fully understand the importance of data management. However, they do not always act accordingly. A lack of priority, capacity and a clear approach ensure that the old (usually inefficient) working methods remain intact.

Where are the pain points for your organisation, and how important are they? The first step you could take is to work out how well you score in the various core areas of data management (data strategy,, data operations, data quality, data architecture, data competence and data governance).

Interested in an exploratory discussion? Email or call +31 (0)6 27 06 42 82


Written by Niek Nendels
Senior New Business Manager Data Management at Ctac
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