Every organization (profit and nonprofit) is about data and information. According to PwC ‘[h]ighly data-driven organizations are [3 times] more likely to report significant improvement in decision-making’ and with more and more data being produced every hour those who will be able to manage that data will have a significant advantage. In advanced economies like the United States, United Kingdom, and Germany data-driven decision-making organizations are more than two-thirds of all companies.
Knowing is caring. With specific (and appropriate) data you will be able not only to monitor day-to-day business (that is quite obvious) but also to predict potential changes important for the organization or gather information about the next steps you should undertake as the executive or manager. Such data can relate to a company’s financial standing, clients (their behaviors), trends, sales, or internal affairs, such as executive board meetings or human resources. We can say that every part of the organization produces data that CAN be useful.
Why CAN? Because having data is not enough. Without tools and solutions that are able to ‘sift’ important data from unimportant and relevant from irrelevant. The issue with large organizations is that they have a lot of data that is ‘flying’ around, and nobody actually knows how to leverage it or how valuable it is. Sounds familiar? This is the typical situation as we are often using different templates and approaches to documents. Nothing to worry about. At least if you consider automation and AI-based tools.
[Note: if you would ‘ask’ Google about the unstructured data in organizations you will receive an answer that its consist of around 80-90% of the whole data]
Most organizations are based on unstructured data that is – in simple words – not organized data that may have a form of plain text, e-mails, photographs, and other similar content. Just about 10% of the data is organized in excel or SQL databases that are easy to read both by humans and AI models, including machine learning and natural language processing. Usually, it is financial data.
Rest is a mess or at least can be. This information can be of different nature. Some data may not include personal or sensitive data, e.g. aggregated and anonymized information about sales but some – if not the most – may include not only personal but also sensitive data. This data, nevertheless, can be used by NLP or ML tools for processing and extracting relevant information for your company. With various anonymization and pseudonymization techniques, we are able to prepare data in a manner that is in line with legal and regulatory requirements without losing the value of such data. This is important as every model has to be trained on data that will be used for the production.
