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Why data analysts often cannot do what they were hired for



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May 11, 2022 | 4 min read

By Martin Brunthaler, co-founder and CTO, Adverity

By Martin Brunthaler, co-founder and CTO, Adverity

Usually, all departments of an organization are concerned with making the best possible use of collected data. But in an astonishingly large number of departments, a shirtsleeve approach prevails – even data analysts, who are supposed to be familiar with the subject matter and have to use and interpret information in the same way, have a poor data basis.

As a result, the results of their work also offer far less value than they should. This is one of the findings of the second part of Adverity’s study "Marketing Analytics 2022: State of Play". The fact that data analysts also see themselves as more competent than they actually are does not help either. In times when the use of predictive analytics is being discussed, a clear paradigm shift must therefore take place.

What data analysts need

Despite the above, there does seem to be little understanding in the market about this fundamental issue. The reality in many companies looks bleak in this respect: there is still quite a bit of time spent on data cleansing and harmonizing data from different sources. It is not uncommon for well-paid professionals to spend a large part of their time on manual data integration instead of performing analysis – in other words, what they were actually hired for. To date, only 41% of all analysts even have access to a "single source of truth," i.e., an enterprise-wide, centralized data source. And among these 41%, most of them use predictive analytics; among users without a corresponding central data repository, the figure is only 27%

Organizations must strive for a certain data maturity that allows them to also trust their data. Because the amount of data collected will continue to increase in the future, and with it data sources: even weather and traffic information to demographic information about entire economies may play a role in the future. However, more than three quarters of the analysts who say they are "data-ready" according to the study have not even taken the first step in this direction. So before they can consider advanced technologies such as predictive analytics, they need to quickly fill the gaps at the most basic level - because most respondents have planned to use them this year, as well as AI and machine learning.

What data analysts can do – or rather, have to be able to do

With this knowledge in mind, it is all the more surprising that, according to the study, 38%of respondents who want to use predictive analytics claim that they still struggle with the manual integration of data. On top of that, a whopping 67%still resort to spreadsheets to create their marketing reports. This last point in particular is of great importance, because the study also shows that above all companies that already have strong campaign reporting also have the best prerequisites in comparison to roll out predictive analytics.

Ideally, the work process of data analysts should be briefly summarized as follows: information should be collected systematically and always up to date and then visualized on a dashboard to provide the best possible basis for decision-making and to evaluate results. The basis for this lies in a good data culture as well as automated tools for an analytically mature handling of data. This includes, not least, a single source of truth – the basic building block for fully automated data integration. For analytically mature teams, automated data integration and a uniform view of data are a matter of course. Because they know that sustainable proactive modelling can only be achieved with a solid data infrastructure.

What now?

In other words, companies are still far from being sufficiently aware of the necessary steps that lead to the successful use of proactive modelling. So we have to assume that in the next few years the wheat will be separated from the chaff when it comes to data maturity and it will become clear who will remain competitive. Because only the right use of high-quality analytics and BI tools opens the door to meaningful insights and consequently to business success. As the tools evolve, the capabilities become accessible to smaller companies as well.



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