What do you do if you need to uncover valuable insights from unstructured data as a business analyst?

If we need to uncover valuable insights from unstructured data than: ?Define goals. ?Collect data. ?Clean and explore. ?Apply NLP. ?Model. ?Interpret. ?Communicate. ?Iterate.

For analysts at all levels, understanding the landscape of unstructured data begins with thorough assessment. Start by defining objectives and questions, then identify sources and formats of data. Leverage automated tools for initial sorting, followed by manual review for nuanced insights. Prioritise quality over quantity to extract actionable insights efficiently.

- Create an inventory of all data sources. This could include emails, customer reviews, social media posts, videos, and more to understand where your data comes from helps in identifying patterns and potential biases in your analysis. - Identify which data types are most likely to get insights that align with your business objectives. - Tools like IBM Watson, Google Cloud Natural Language, or open-source libraries such as NLTK and spaCy can help you analyze text data, extracting sentiment, themes, and patterns. - Utilize tools capable of handling unstructured data, such as Tableau or Power BI, which now incorporate NLP features to generate insights from textual data visually.

Keep it simple. 1-Start with what are your key probing questions 2-Where and how can you find authentic data and it's source 3- Select the appropriate tools to organize your data

The segregation of unstructured data, origin or source wise is first step to structure data for analyses and find patterns. But defining the Objective or Goal first, is the best approach before structuring data or if you further structured the data. In this way you gets the answers to your Questions based on defined Goals in more efficient and effective way.

Tool selection should also be aligned to the goal you have identified for exploration of the data along with the complexity and quality of data. Eg. Choosing tableau for a largely complex and unclean data would hamper performance while data pre-processing vs augmenting tableau with a layer of Python data processing would streamline your performance.

When selecting tools for analysing unstructured data, prioritize those with robust natural language processing (NLP) capabilities, such as sentiment analysis and entity recognition. Look for scalability and compatibility with existing systems. Consider user-friendliness and integration with visualisation tools for comprehensive analysis and reporting. Opt for platforms offering flexibility to adapt to evolving data needs.

- Ensure the tool integrates well with your existing data systems and workflows. - The tool should be user-friendly, allowing analysts to focus on analysis rather than learning complicated software. - Choose a tool that can handle growing amounts of data as your business scales. - Consider both the initial cost and the ongoing expenses. Open-source tools can be cost-effective but might require more setup time. - An open-source Python library providing easy-to-use interfaces to over 50 corpora and lexical resources. - Another Python library for NLP, known for its performance and scalability. It’s well-suited for large-scale information extraction tasks. - Google Cloud Natural Language also provides text analysis through machine learning.

Yes, I agree that the selection of Tools plays a very critical step in analysing data, and it should offer NLP ( natural language processing) capabilities for more users friendliness, as with invention of LLM in AI and GenAI most of the tools have are embedded these capabilities to understand human emotions and language. But again the first step for Settings Goals and Objectives is plays essential role in opting for right tools to get the best outcome.

Choose data analysis tools with natural language processing (NLP) and machine learning capabilities to effectively analyze unstructured data. These tools enable understanding text-based human language and identifying patterns and trends in the data, enhancing the efficiency and depth of analysis.

To uncover valuable insights from unstructured data, employ text mining, natural language processing, and machine learning techniques. Utilize tools like sentiment analysis and topic modeling to extract meaningful information. Collaborate with data scientists and subject matter experts to refine analysis methods and derive actionable insights.

Effective data cleaning is essential for analysing unstructured data. Start by identifying and removing irrelevant or redundant information. Utilise automated tools for initial cleaning, then manually review for accuracy. Standardise formats and resolve inconsistencies to ensure quality. Document cleaning processes for reproducibility and transparency. Regularly update cleaning protocols to maintain data integrity.

I embark on a journey akin to a detective solving a mystery. I start by gathering data from diverse sources like customer reviews, and internal documents. Then, armed with analytical tools and techniques, I sift through this data, searching for patterns, anomalies, and hidden correlations. Like a sculptor shaping clay, I mold raw data into meaningful insights, uncovering market trends, customer sentiments, and operational inefficiencies. Through this process, I illuminate the dark corners of information, transforming chaos into clarity. Finally, armed with these insights, I guide strategic decisions, helping my organization navigate the complex landscape of modern business with confidence and foresight.

Clean your unstructured data to remove noise, irrelevant, or redundant information, ensuring consistency and accuracy for reliable analysis. This process, akin to polishing rough diamonds, reveals the true value and brilliance of your data.

Data cleaning can be addressed in multiple ways - though what I find useful is to label and tag your data prior to cleaning make resusable processes for cleaning similar data subsets

For effective pattern recognition in unstructured data, leverage machine learning algorithms tailored to specific data types and objectives. Pre-process data to enhance signal-to-noise ratio and reduce complexity. Utilise feature engineering to extract meaningful patterns. Validate patterns through cross-validation and testing. Continuously refine models based on feedback and emerging trends. Collaborate with domain experts to interpret and contextualise identified patterns for actionable insights.

Go back to your key questions. Wear your white and black hat alternatively (Edward de Bono Six hat thinking ) Start with the most obvious data and keep on asking why. For eg. If your data shows a CSAT score for a specific region as low, ask WHY. Further, go on asking WHY for each conclusion you derive. Remember, summarizing the right data and asking the right WHY can prevent you going down the rabbit hole.

Using advanced analytics techniques and AI algorithms, I identify patterns, trends, and correlations within the data. This step is crucial for uncovering hidden insights and understanding underlying relationships.

- Incorporate your insights into the strategic planning process so they can inform everything from product development to marketing strategies. - Use insights to make informed decisions on the operational side, like adjusting inventory based on sentiment analysis of customer feedback. - Prepare presentations or reports that communicate the insights in a clear and compelling way to stakeholders, emphasizing the impact on the business. - Use storytelling method to make the insights more relatable and understandable.

Based on the patterns identified, I generate actionable insights that provide valuable information for decision-making. These insights often involve discovering market trends, customer preferences, or operational inefficiencies.

- Evaluate each insight based on its potential impact on business goals and priorities. Consider factors such as feasibility, cost, and expected ROI. - Classify insights according to their urgency and importance to tackle critical issues promptly and allocate resources effectively. - To ensure clarity in what you aim to achieve and facilitates tracking progress, set SMART goals that are directly linked to the insights. - For each goal, outline strategies that utilize the insights to achieve desired outcomes. Don't forget that strategies should align with overall business objectives and consider current market conditions.

Finally, I collaborate with relevant stakeholders to develop action plans based on the insights gained. This may involve optimizing business processes, launching targeted marketing campaigns, or enhancing product offerings.

I will utilize advanced analytics tools and techniques to extract, clean, and analyze unstructured data. I will identify patterns, trends, and correlations within the data sets. Additionally, I will collaborate with data scientists and subject matter experts to interpret the findings and translate them into actionable insights for decision-makers.

Each of these things are important to consider, but I think the most important is the initial understanding of: 1) where did this information come from, 2) what conditions were in place when the information was entered ( required fields, data rules, etc.), 3) are there other sources you must also consider, and the most critical 4) what is the goal of your analysis?

One thing I have found helpful is verifying your insights from multiple source. Confirm if your insight aligns with the ground reality. Verify your insight with the taxonomy / base theme of the business. Use AI but that should never be the final step.Get buy in of your story from the key stake holders

It's essential to continually refine and iterate the analysis process, incorporating feedback from stakeholders and staying updated on emerging technologies and industry trends.