The 4 Pillars of Big Data Management

As a data-driven company, you need functioning data management to use the opportunities that arise from the ever-advancing digitization for business process optimization in an agile and customer-centric manner.

First, you should understand that big data management is the entirety of measures at a technical, conceptual, and organizational level to collect, store and evaluate data and make it available so that it optimally supports the company processes. It is the basis for all elements of the information lifecycle and takes aspects of data quality, data protection, and data storage into account, among other things.

Here are the 4 pillars of big data management to look at:

1. Gaining knowledge: which customer insights can you gain?

That’s the job. That’s what we’re on the road for. As a data-driven company, you practice data management to gain customer insights to better serve and inspire customers through intelligent business process optimization. If you want to achieve customer loyalty for tomorrow and the day after by understanding today’s customer behavior, you have to open up the potential from all relevant data sources and use customer attributes, scoring, and control parameters to control business and marketing communication.

However, the strategic approach is also important here: big data often fail because of the wrong approach – and so the knowledge gained all too often falls by the wayside or, in the worst case, wrong conclusions are drawn from the data evaluations. Data analytics only brings real customer insights if the data quality and data consistency are right.

2. Administration & responsibility: where is the data located, and who can do what with it?

Your data and system architecture must also ensure that the data is applicable. Transparency and cataloging, as well as consolidation of all decision-relevant data in one place, are a prerequisite. Data governance – who owns the data, who can work with it, who can overwrite it – is also a critical point. So remember: The administration and regulation of responsibility also influence data quality.

Especially if you want to store data in different places as part of a multi-cloud strategy, you need comprehensive data governance that defines the rules and guidelines for handling data in the company and beyond (!). This also includes the precise regulation of responsibilities in the company and the clarification of the question of which resources may be stored and processed on which server or cloud.

3. Availability: is there any decision-relevant data at all?

That sounds easy. Make all data available that are important for decisions. However, the devil is in the details. It’s not just about a functioning data and system architecture but also about legal conformity (the GDPR sends regards) and tailor-made data delivery to departments such as CRM. Because your customer has not given their consent to all evaluations within the scope of data management in all cases.

The collection of data is part of the maturity of a data-driven organization – at all your touchpoints, up-to-date and reliable. But the issue of availability and provision has a technical and a legal side. Therefore, you should ensure that your company can continue to process the data you need in the future and that all relevant units, including your service providers, can work with it. Companies should pay particular attention to data sources collected outside their borders. There is a high degree of legal uncertainty here as far as targeting is concerned.

4. Efficiency: what about automation, historicization, and processes?

Thanks to digitization, data is streaming into the house. And that through more and more channels. A significant challenge for data management due to the ever-growing amount of data. And how should one deal with the data from previous years? Automated data processing is almost a necessity. Efficient data storage is crucial for profitability, controllability, and knowledge generation.

What do the interfaces in your company look like? What about the many processes? Every business process optimization should also take the data flows into account and analyze them (or have them examined) accordingly. In principle, API interfaces are available for all relevant sources, which enable import into a data warehouse or a comparable platform for big data management. Data science experts and data analytics specialists can ensure the necessary data consistency here and help you draw the correct conclusions from your data in the context of big data analysis as part of business intelligence.


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