The fact that the topic of data, its quantity and importance as well as everything around it is increasing, I will spare in this post. This should be more than known by now ???? Nevertheless, I see every day in projects that the topic of data is still treated very shabbily in many companies – sometimes unknowingly.
Big Data Analytics replaces intuition in decision making
Big Data has long since replaced gut feeling and instinct. This makes it even more important that big data and advanced analytics are used correctly in companies and thus support data-driven decision-making. It should be briefly mentioned here that the term big data does not actually apply to most companies anymore and that it is not the quantity of data but its heterogeneity that is usually one of the greater challenges in data processing and preparation.
A brief digression: In contrast to business intelligence, advanced analytics falls into the area of data science and goes beyond data analytics with its methods. Advanced analytics is usually based on advanced programming and modeling, which in most cases requires very large amounts of data. At its core, it involves the use of various forms of artificial intelligence, such as in data mining or process mining. In comparison to business intelligence, advanced analytics is thus not only related to historical events and their evaluation, but lives through the prediction of future events supported by modeling.
For example, advanced analytics can support e-commerce to personalize the customer approach and improve the customer journey by using existing customer data for detailed segmentation.
Big data and advanced analytics are all about asking the right questions. These questions should not only be known to data scientists or experts – but every employee should also know where to find information relevant to them and be able to interpret it correctly. After all, the results of analyses are only as good as the questions that are asked. Simply collating data will not achieve the goal. The questions should also integrate individual business problems and goals to determine what the analyses are intended to achieve. These questions can, of course, evolve or change according to the results during the big data analysis. Data literacy, the ability of employees to work with, understand and properly interpret the data, also plays an important role here.
Starting Big Data and Advanced Analytics
It is also important that the right data sources are evaluated and brought together accordingly so that the data can be combined and analyzed in a targeted manner. These sources should be defined in advance together with subject matter experts. The quality of the data also plays a role here: cleansing is often necessary here to correct any errors that may have occurred over years of manual data maintenance and entry. Especially to be able to clearly recognize conspicuous correlations and patterns in the company data, a clean linkage of the correct data is crucial.
In addition, big data analytics should be meaningfully integrated into the company’s daily processes. This includes, among other things, the right dashboard design to use relevant key figures in a targeted manner for data-based decision-making in the company. Structure, consistency, relevance, uniformity, visual perception, and content conception are important cornerstones here, which significantly determine the clarity of the data and its use. Collecting big data alone does not lead to the goal. The purpose and the various use cases of the collected data should therefore be defined in advance. Additionally, the topic of data governance plays an important role in clarifying the responsibilities and roles for the data as well as its organization within the company.
Closing Thoughts
Data helps determine the right decisions in companies. They also enable proactive responses to risks and opportunities and thus also influence the company’s success. For this purpose, the analysis of big data should follow certain objectives and incorporate empirical data. Advanced analytics methods differ from business intelligence through a future-oriented perspective and the use of elaborate technologies, especially artificial intelligence. By drawing conclusions from historical data, predicting events and developments, recommendations for action can be derived and processes and performance in the company can be improved.
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