Data are the Foundation
for optimising marketing Processes
Combine data from several sources as required (across systems, teams and locations),
Gain a complete and coherent view of a topic (e.g. customer experience),
develop new findings and improve processes.
“struggle to manage the volume,
variety, and velocity of their data”.
‘B2B Data Activation Priority’ Report 2018,
by Forrester Research for Dun & Bradstreet.
Data from multiple source must be integrated to generate insights.
Data generated by legacy processes may no longer be relevant.
Data capture processes may lag behind current needs.
Heterogeneous systems use inconsistent formats.
Unstructured data will consume more resources before it can be re-used.
The real world evolves faster than systems can record the changes.
Inaccurate data generates inaccurate analyses (“rubbish in, rubbish out”).
for data quality include: update frequency, completeness and accuracy.
for data integration across sources and systems include: consistent definitions, formats and granularity.
Analysis is simplest when applied to data from a single system.
Data mining, predictive analytics and artificial intelligence are “hot” topics – in practice such techniques depend 100% on data quality.
If the data quality is poor, the analysis is worthless: “Garbage in, garbage out”.
If the necessary quantity of data is missing, the results are not meaningful.
In both cases, it is worth defining a data acquisition strategy.
Data collection and analysis is not a one-time project, but constant and ongoing.
Data quality is achieved most cost effectively when the required standard is built into the design of standard business processes.
Data is not a cost item, it is a corporate asset – investment generates a return.
Any analysis of personal data must fulfil the criteria of GDPR.
Conformity with GDPR means that processes must provide “Privacy by Design” .
reduce the scope – focus on the data that enables essential decisions.
integrate data across fewer systems and data sources.
Build a Business Case
Estimate the value of the benefits.
Define a target RoI when setting project scope and resources.
Design the method to reflect the scope of the project.
Do your marketing staff have the necessary skills and capacity to meet planned data quality requirements, or do you need to build-up experience In-house?
External resources – experienced experts can quickly be brought on board in the short term. This will be a more costly solution in both short and medium-term and you will not acquire the skills in-house.
Knowledge transfer – a slower start, but over the medium term , the most cost-effective way to build data quality and acquire the skills in-house.
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