is a term that describes a fast-growing amount of data. New and particularly powerful IT solutions are necessary to manage the data volumes, to convert unstructured data into useful information. The data is created in increasingly complex business processes. A prerequisite for the analysis of all this data is specialists who have a deep understanding of how the data is read and how it can be used for profit. Today, it is no longer a secret that professional processing of the data gives companies a decisive competitive advantage.
In-house employees often not sufficiently qualified
Most businesses like to carry out most of their activities internally. However, the range of well-trained data analytic experts is very limited. Often, employees have to familiarize themselves with the broad field of work, with the corresponding consequences. The "data handlers" quickly reach their limits because the necessary background knowledge is lacking. Ultimately, it makes sense to use a professional data science company or to make your own employees fit in the field with further training. It is not just a matter of being familiar with the technology. Knowledge of the company's market situation is equally important, best paired with strong communication skills.
The objective of data analysis
When it comes to data analytics, one thing is very important: which business goal should be achieved? The type of analysis depends on the answer. According to the statistics, every third company already uses big data analyzes for production planning and project execution. There are two options for analysis:
* Ad hoc analysis
Ad hoc analysis is a static process that companies do not use productively. For example, it can be used to make customer segmentation based on the current state at a specific point in time. From this, the companies derive recommendations for action and implement them. However, this does not happen continuously.
* Analytical application
In the analytical application, segmentation also takes place, but it is application-specific. For example, categorizing the visitors to a website by country of origin in order to be able to create country-specific offers. The data processing is often done in real-time.
Draw the right conclusions
In order to draw the correct conclusions from the data analyzes, application-specific evaluations are necessary. To do this, it may be necessary to centralize the data analyzes and to develop a specialization within the framework of a data science center. Analysts need to understand business areas, prepare data, develop models, and be able to program software. This makes it easy to see that only well-trained specialists are up to the task, simply because the range of tasks is so extensive.
Companies have to rethink
Without a doubt, data is the best way to find out what customers want today. However, collecting and evaluating data in a targeted manner is not always easy in traditional companies. A rethink is necessary to adapt to new needs or to rethink existing corporate strategies.
Speed is the key
In today's world, the speed at which business processes run is often decisive for competition. Operational business intelligence enables companies to make better decisions in day-to-day business. You can identify difficulties more quickly and take appropriate corrective action earlier.
The complexity increases
The development of new data analysis methods has brought a multitude of different possibilities onto the market. It is sometimes difficult to decide which solution is suitable for the problem at hand in the company. Heterogeneous database management systems are usually the solution that combines different technologies from different providers. There are more and more specialized solutions for a specific problem.
This poses a major challenge for companies. The market is becoming more complex, and new technologies and products are developing faster and faster. More and more data sources can be integrated through cloud solutions. These developments don't exactly make work easier.
There is not just one complete solution, but the best possible solution for the various areas of application. An overall solution from one manufacturer often does not solve all the problems in the company satisfactorily. Companies should do good research and test alternatives on-site to find exactly the right solution.
* Timely implementation
Applications and systems should be quickly and easily integrated into the existing data ecosystem. It is important to implement projects promptly. If you are too slow, you already have outdated data after the analysis, which can no longer be used because the requirements change so quickly. Easy-to-use solutions are, therefore, preferable.
* Stay agile and flexible
The commitment to certain suppliers or technologies means that companies cannot react to market requirements. Here it is important to remain flexible so that the introduction of new solutions can be implemented quickly. Companies should take data analysis very seriously. It can bring decisive competitive advantages in the future.
The stumbling blocks in the introduction of big data
Failure in the introduction of big data is often linked to internal processes or unresolved responsibilities. The data quality is also poor in many cases because the data is incomplete, or databases cannot be evaluated automatically. Another important point is the complexity. The employees' responsible need time to ensure that data analysis runs smoothly. You have to prepare and prepare for the new requirements. In companies, however, the information usually gets to employees late. Exact expectations and overarching goals are often unknown, and IT only receives a list of requirements. Additional risks due to misunderstandings or lack of support make work more difficult.