Data science has been a part of insight generation and targeting for years. Today, the new challenge in hand is to make a better use of a vast amount of what we call “unrequested” or "real-world evidence" data that is collected for purposes other than market research.
The journey to mastering these new data sources to generate insight offers exciting new opportunities, but there are pitfalls along the way. These can be avoided, though – if you know where to look.
It has become clear that Big Data does not automatically translate into Big Insights. Data quality is the key problem that data scientists must grapple with. And more complex questions require not only more data, but more diverse, comprehensive data, bringing yet more quality problems.
There is no such thing as a "magic algorithm". If the data is bad or the coverage is poor, even the best AI algorithms are useless. Even worse, without knowledge and consideration, the most powerful algorithms can produce the most misleading results.
In this paper, find practical solutions for using Big Data including eight rules for successfully integrating data from multiple sources.
Ipsos’ view is that Big Data and data science will continue to disrupt market insights. But they will not be able to address all industry information needs. There remains a firm need for primary research and a greater combination of data sources, including qualitative research, as we will always need someone who can tell the human story behind the data.
[Webinar] Nachhaltigkeit: Was wir wissen und wie Sie handeln können
Die Erwartungen der Konsumenten an die Nachhaltigkeit steigen, aber in der Tat ist dies komplex und manchmal nicht einfach, sich nicht nur der Nachhaltigkeit zu verpflichten, sondern auch schrittweise Massnahmen zu setzen, um wirklich im gesamten Prozess nachhaltiger zu sein.