Technology as a driver | Business Intelligence / Big Data

SMEs usually focus heavily on their operational business and leave the topic of “data analytics” in the background for now. At the same time, they are already collecting endless amounts of data through various channels. Even if there is a willingness to work with data, there is a lack of strategic knowledge and, above all, technological knowledge and motivation to decisively advance data initiatives. With a confident practical mindset and a mind open to new technologies, data projects can be developed in four steps.

Step 1: Just do it

Successful data projects start small. “Think smart” instead of “think bigger” is the motto. It is often enough to visualize a simple Excel spreadsheet with a tool and then create a small dashboard. This requires neither extensive technical knowledge nor assistance from the IT department. With Microsoft Power BI or Tableau, for example, small amounts of data can usually be processed within your unified software environment to produce fast results. Such approaches to interpreting an individual’s data are important because they release initial AHA effects and show how data can become insights. The tools are intuitive and easy to use. All you need is an idea of ​​what exactly to imagine.

Step 2: Find, collect and enrich data

Everyday business life is constantly producing new variants of data that only add value when interpreted relationally. Therefore, basic knowledge is essential about what exactly characterizes data as such, where it occurs and in what context it can be placed. In order to be able to later work with this data in a narrative form, it must be collected. Initially, companies do this collectively and without liquidation simply because they lack the technological capabilities of centralized and organized management. However, particularly volatile data, such as device data, requires a permanent storage solution. The data lake is suitable for huge amounts of data, the data store is logical for organized aggregation, and hybrid models can also be visualized. Future-oriented data management solutions rely on the cloud because they can be flexibly adapted, highly scalable, and can be used cost-effectively.

Step 3: Make the data globally available

In order to understand and use all these communications at the company level, data across all areas must be central and easily accessible – to all employees. Technical obstacles must be removed and legal issues resolved. Compliance requirements must be understood and taken into account at all times. All this assumes that the core skills and knowledge of the employees are trained and up to date.

Step 4: Prepare the protective technology for the future

As a technical basis for centralized data storage, analytics professionals like Taod Consulting recommend building a modern data stack. It is a complex, multi-layered system of automated services that collect, collect, analyze and evaluate data. Modern Data Stack establishes the connection between raw data on the one hand and data analytics on the other. Cloud-based tools, which can be replaced, extended or renewed at any time due to their modularity, ensure maximum flexibility. Tech newcomers in particular can rely on comprehensive, readily available, and cost-effective data management tools that can be used and learned quickly.

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