Cleaning? Wiping? Tidying up? This is not necessarily one of the most popular occupations for companies either. Thus, they often leave collected data in their original “dirty” state. Without a clue, they lose billions in revenue every year. In view of current developments, data cleanliness becomes a critical success factor more than ever.
No AI can be operated without data
Artificial intelligence (AI) is future technology at its most. Both large and smaller companies are using algorithmic, machine learning-based tools for a wide range of applications, from analytics to cybersecurity to customer service – and far beyond.
In light of this presence and significance, it is essential to focus on the most important foundation: the data, heart and soul of the AI. Without the right data, the best algorithm is useless. They are available to companies and organizations in rough quantities. Customer and financial data, sensor and machine data, historical company data as well as data from numerous sources more. The knowledge needed to make better decisions is available – but is often not used.
It is due to two main factors, however, that this data is often not easy to use. Number 1: Different data formats lead to highly heterogeneous data sets. A big challenge, it seems, but the solution is relatively simple. Because the data can be merged in a Data Lake, consolidated and converted into a uniform format for further processing.
Data is often not clean enough for AI
More important is factor number 2: the so-called contamination of the data. This includes, for example, missing information, inconsistent data, or simply errors. If the data is not cleaned before it is used by an AI, the consequences can be serious.
Accenture analyses show that as many as 79 percent of companies base critical decisions on data without investing in its verification and thus risking immense losses. As Gartner confirms, this results in 15 billion dollars in losses worldwide every year.
The reason is as simple as it is serious: Data cleansing costs time, effort and money. Compared to the threatening result based on uncleaned data, though, it is far less expensive.
What exactly is Data Cleansing?
Data cleansing is the process of modifying data in a particular storage resource to ensure that it is accurate and correct. This does not necessarily mean deleting inappropriate data. Rather, the process serves to maximize the accuracy of the data and the associated quality of its statements.
Similarly, data cleansing does not imply achieving some kind of data perfection. Because data is a means to an end. Rather, a quality level should be striven for at which data can be used and processed, which enables efficient processes and helps to achieve optimal results. Data cleansing should therefore be performed by experts who are familiar with the processes – the data scientists. In fact, it accounts for the most of their work.
Industrial AI is coming and needs Data Cleansing
The market and tasks of artificial intelligence and machine learning continue to grow disproportionately. Half of all German companies are already actively involved in these technologies, and 22 percent are already using them productively, as our current study on the subject shows.
The most recent edition of the Gartner “CIO Survey” confirms that there is an enormous international demand for artificial intelligence. Over the past four years, the number of companies using AI has increased by 270 percent. Last year alone, the proportion tripled. The analysts cite the enormous further development of corresponding technologies and necessary computer performance as the reason for this. At present, AI solutions are still being trained for special applications, but data-based decisions have long influenced them.
At the latest with the next development step, the third wave of AI and the associated contextual capabilities, the phase of AI industrialization begins. Then the processes will be so complex and critical to success that AI and its accurate data will be indispensable in business operations.
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