How can you automate business reporting with natural language processing?

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Business reporting is a vital process for any organization that wants to measure and improve its performance, communicate with stakeholders, and comply with regulations. However, creating and updating reports can be time-consuming, tedious, and error-prone, especially when dealing with large and complex data sets. How can you automate business reporting with natural language processing (NLP), a branch of artificial intelligence that enables machines to understand and generate human language?

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Dr. Martin Schiele

AGI before 2027 seems totally possible

1 What is NLP and how does it work?

NLP is a technology that allows computers to analyze, interpret, and produce natural language, such as text or speech. NLP uses various techniques, such as machine learning, deep learning, natural language understanding, natural language generation, and natural language querying, to perform tasks such as sentiment analysis, text summarization, translation, speech recognition, and question answering. NLP can help you automate business reporting by extracting insights from data, generating narratives, and answering queries in natural language.

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2 How can NLP extract insights from data?

One of the main challenges of business reporting is to find and present the most relevant and meaningful information from large and diverse data sources. NLP can help you automate this process by applying techniques such as named entity recognition, topic modeling, keyword extraction, and sentiment analysis to data. These techniques can identify and classify the entities, topics, keywords, and emotions in the data, and provide you with a high-level overview of what the data is about and how it relates to your business goals.

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Dr. Martin Schiele

AGI before 2027 seems totally possible
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I would suggest using Retrival Augmented Generation RAG, in combination with a big LLM. So the whole report is part of the context and all relevant questions can be answered by the large language model.

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Ricardo L.

Linguist
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In my experience, the controlled vocabulary common to social services is ripe for exploitation (in the computing sense) for practitioners of NLP. During a stint as the sole IT support for an overwhelmed homeless outreach group, we used the fact that staff was already accustomed to using a certain set of vocabulary to describe care recipient interactions and began to benchmark the frequency of these terms as an indicator for meeting KPI’s. A combination of regular expressions and named entity extraction from libraries, like Spacy, helped this organization gain real time visibility. Their prior approach involved counting by hand and through less digitized means.

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Dawn Ruby Nishimoto

Blue Team SecOps and DevOps Engineer with knowledge in SLP looking for work or short term contracts.
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Using RAG methods in combination with LLM can help massively for things like Intent Classifications to extract data insights from given text. That allows things like few shot learning, multilingual predictions as well as faster training. In essence the LLM-based intent classifier relies on RAG, which combines benefits from retrieval-based and generation-based approaches.

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3 How can NLP generate narratives from data?

Another challenge of business reporting is to communicate the insights from data in a clear, concise, and engaging way. NLP can help you automate this process by using natural language generation (NLG) techniques to create narratives from data. NLG is a technique that converts data into natural language text or speech, using rules, templates, or neural networks. NLG can help you automate business reporting by producing summaries, headlines, captions, bullet points, or full reports from data, in a style and tone that suits your audience and purpose.

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4 How can NLP answer queries from data?

A third challenge of business reporting is to provide answers to specific questions from users or stakeholders. NLP can help you automate this process by using natural language querying (NLQ) techniques to enable users to ask questions in natural language and get answers from data. NLQ is a technique that translates natural language questions into structured queries, such as SQL, and executes them on data sources, such as databases or spreadsheets. NLQ can help you automate business reporting by allowing users to explore data, drill down into details, and discover new insights without coding or technical skills.

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5 What are the benefits of automating business reporting with NLP?

Automating business reporting with NLP can offer a variety of advantages, such as saving time and resources by reducing the manual labor and complexity of creating and updating reports. It can also improve accuracy and consistency by removing human errors and biases in data analysis and reporting. Furthermore, NLP can enhance readability and engagement by generating narratives that are easy to comprehend and tailored to the audience and context. Additionally, it increases interactivity and flexibility by allowing users to ask questions and get answers from data in natural language.

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6 What are the challenges and limitations of automating business reporting with NLP?

Automating business reporting with NLP comes with a few challenges and limitations. It's important to ensure data quality and reliability by verifying the sources, validity, and completeness of the data used for analysis and reporting. Additionally, domain knowledge and expertise should be maintained by incorporating the specific terminology, rules, and logic of the business domain into the NLP models and outputs. Furthermore, NLP performance should be evaluated and improved by measuring and optimizing the accuracy, relevance, and coherence of the NLP outputs and feedback. Lastly, ethical and legal issues should be addressed by ensuring the privacy, security, and compliance of the data and the NLP outputs, while respecting the human rights and dignity of users and stakeholders.

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Dawn Ruby Nishimoto

Blue Team SecOps and DevOps Engineer with knowledge in SLP looking for work or short term contracts.
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NLP is not perfect and you may run into false positives or wrong predictions. This can happen more or less often depending on model bias and domain knowledge. Especially things like domain level languages may throw off common NLP models if they weren't specifically trained for your business environment on existing data. Legal and Ethical issues may also arise if there is no human review and data generated from NLP is taken at face value / not reviewed.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you automate business reporting with natural language processing?

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1 What is NLP and how does it work?

2 How can NLP extract insights from data?

3 How can NLP generate narratives from data?

4 How can NLP answer queries from data?

5 What are the benefits of automating business reporting with NLP?

6 What are the challenges and limitations of automating business reporting with NLP?

7 Here’s what else to consider

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