What are the most common big data challenges when it comes to your data analytics? How to use a holistic data strategy to make big data and data analytics child’s play and maximize your return on data?
Data is the gold of digital transformation. A holistic data strategy is necessary to unearth this treasure. Learn how to identify and avoid the 7 most common pitfalls of big data challenges in data management. Big data and data analytics are child’s play and you can maximize your return on data.
Data management fragmentary
Let’s be honest: In view of the growing amounts and sources of data, who can still keep a full overview of the most important key figures? However, it is precisely this quick and holistic overview that is needed today in order for a company to be able to make the right decisions. In addition, the complexity with regard to the networking of your data increases, because the full potential can only unfold in the connection of different data sources and in the holistic view of the decisive data. A real challenge, because data management often remains fragmentary. Many companies lack a holistic data strategy. So in the end you only have individual pieces of gold, but unfortunately no data treasure.
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DataLion team has been dedicated to this topic for many years and knows the most common pitfalls in big data management.
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DataLion team has been dedicated to this topic for many years and knows the most common pitfalls in big data management.
Most Common Big Data Challenges
Linking: On the one hand there is the topic of data linking. It is very important because if your data cannot “talk” to one another, it becomes difficult to make connections between metrics. However, this is of great importance because, in order to remain future-proof as a company, you not only have to know your data individually but also make it usable holistically and networked for strategic planning as well as for day-to-day business.
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Data sources: Different data sources are another important point to consider. In the rarest of cases, one has a data source from which a large part of all information can be obtained. The opposite is the case and problems quickly arise here in the daily handling of data. Different time periods, unclear names, different market definitions, and different interpretations of key figures are only a small selection of possible stumbling blocks that can occur when using different sources.
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Data quality: One of the most important topics in the area of big data is certainly that of data quality because this is usually the key to the data treasure described above. Surely you have heard the abbreviation GIGO, short for “Garbage in / Garbage out”? What do you mean by that? If the database is already faulty and therefore the input is bad, you will in all probability not be able to compensate for it with a meticulous data analysis so that the output fits in the end. Or to stick with our saying: If the input was crap, so will the output at the end of the day! The areas of data cleaning and data prep are therefore essential and are part of holistic data management at DataLion as part of our category consultancy approach.
Data quality: One of the most important topics in the area of big data is certainly that of data quality because this is usually the key to the data treasure described above. Surely you have heard the abbreviation GIGO, short for “Garbage in / Garbage out”? What do you mean by that? If the database is already faulty and therefore the input is bad, you will in all probability not be able to compensate for it with a meticulous data analysis so that the output fits in the end. Or to stick with our saying: If the input was crap, so will the output at the end of the day! The areas of data cleaning and data prep are therefore essential and are part of holistic data management at DataLion as part of our category consultancy approach.
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Blind Spots: One last exciting area, which should be given a lot more attention, is the area of the supposed “blind data spots”. Often there is information in the company that does not suggest at first glance that parts of the data map can be built or modeled with it. Do you know the saying: “Unfortunately we don’t have the data and we just have to live with that”? Is that really always the case? In the field of market modeling, DataLion has developed ways with its category builder approach, how one can develop such blind spots despite the supposed lack of data and thus turn a former “invisible share” into a “visible share”.
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Other fields: such as data ownership, data dictionaries, and data strategy complete the picture of the big data challenges and are also fundamental components of modern data management. If you are curious about specific examples and how you can tackle the topic at short notice, visit the DataLion lecture at DataLion Booth – Munich Marketing Week on 17.00 Thursday, July 1st, 2021.
Other fields: such as data ownership, data dictionaries, and data strategy complete the picture of the big data challenges and are also fundamental components of modern data management. If you are curious about specific examples and how you can tackle the topic at short notice, visit the DataLion lecture at DataLion Booth – Munich Marketing Week on 17.00 Thursday, July 1st, 2021.
Updated: Here you can listen to our CEO – Dr. Benedikt Köhler discusses the 7 most common BigData challenges and how to master them on Munich Marketing Week 2021:
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To discuss more your data management strategy, make an appointment to talk with our experts here:
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