Business Insights on Democratizing Text Analytics from Unstructured Data

Contributing Author
8 min read
Jan 15, 2024
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The need to democratize data cannot be overemphasized. Both big and small businesses need to maximize data for its numerous benefits. From marketing campaigns to gaining a competitive advantage, a data-driven business is always ahead of other brands. However, not all kinds of data are useful for customer success.

Data access from the data warehouse is the foundational part of analyzing data. You want to ensure data literacy to leverage it and improve your customer experiences. However, data always comes in an unstructured format. While big companies might have data teams to manage their data, small businesses can employ analytics tools to make informed decisions from their gathered data. Hence, there is a need for data democratization.

Data democratization is an ongoing process that allows everyone in organizations to confidently and comfortably work with data to make quality decisions and improve their customer experience. The analysis process begins with effort from data experts before becoming valuable details for the sales teams (or product teams). 

Follow closely to learn about democratizing access and its artificial intelligence.

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Strategic importance of unstructured and raw data

Before data analysis and product analytics, most organizations use unstructured data to make decisions toward their business goals. Thanks to modern technologies and the right tools, we can employ them to analyze data. However, the unstructured data has its strategic importance. 

Businesses can use unstructured data to determine brand perception and customer purchasing trends. A major advantage of raw data is sentiment analysis. One can examine forum discussions and social media posts to measure sales and marketing performance.

Using sentiment analysis, one can employ automated chatbots to provide customer services. Unstructured data can provide insights in the right context. You can then use these insights to improve products or services.

CRM platforms have algorithms that focus on data to identify important information the business needs. Using analytic technology can provide knowledge about a customer's needs. This foresight will allow you to evaluate and take the right strategy to stay ahead of others.

Selecting data for business impact

If you want to use data for business impact, follow these steps:

  1. Have tailored needs for the selected data.

There must be clear ambition about the value of data to one's brand. First, identify the data you need to make an informed decision. To do this, you should answer the question, why does my company need this data? The answer will determine the kind of business solution to employ.

  1. Democratize the data into one source.

After selecting your data, you need to democratize it into one source. You can set up a data model to narrow your data to a single source or format. That way, you can ensure timely and consistent data delivery, no matter the data source.

  1. Design a plan for the data programs.

If you can afford it, get data experts who specialize in high-quality data integration. If you cannot, you can check out a training that will address problems around data programs. The challenge is that you can only simplify data by deciding its back end and enabling the front end. With that, you can have a design that integrates analytics into daily activities.

Successful data democratization methods

Data democratization will only lead to growth if it is well-executed. You will need a proper strategy that gives insight into your needs. Here are the methods you can employ for successful data democratization:

Data democratization tools are for a data-driven organization. You will need to invest in training and self-service analytics tools. Once there is leadership commitment from the top, you can prioritize the need for the data.

As your business expands, dealing with analyzed data becomes more challenging. But, if the data is not accessed, one may never know its immeasurable value. So, you should ensure your data processes take stock of all data to make them accessible.

Data legacy is easier said than done because it involves past and present data. The better approach is to budget architectural design and data integration tools. With this approach, there will be interoperability between data management platforms, legacy databases, and cloud-based systems.

Creating a self-service system is not about using a tool or integrating data from a survey. Rather, it is making data accessible for both technical and non-technical users. They should be able to use data analytics in their daily tasks. 

The profitability of clean data

Clean data has greater relevance than unstructured data. Before we address the profitability of clean data, it is necessary to identify the challenges associated with unstructured data.

Identifying and navigating typical business challenges associated with unstructured data

Common challenges of unstructured data are:

The solution

The solution to the challenges above include:

Resources for improving data skills in strategic decision-making processes

Are there business-focused resources companies can employ to have a competitive advantage over others and make strategic decisions? Yes. You would agree that a craftsperson is only as good as their tool. Here are some resources you can consider:

How to integrate unstructured data to maximize business performance and efficiency

Unstructured data can be large, heterogeneous, and complex. However, you can integrate unstructured data to maximize performance and efficiency via these techniques: data partitioning, compression, indexing, parallelization, and streaming.

  1. Data partitioning

This technique helps to democratize large data into smaller and manageable pieces. It could use category, date, or location to reduce the analyzed data. It also improves scalability to ensure proper distribution across different servers. However, the process involves careful planning to avoid inconsistency.

  1. Data compression

This technique removes irrelevant information and reduces the size of the data. Compression can be done by saving up bandwidth, storage space, and memory to ensure data transfer, processing, and loading. There is always a trade-off that calls for attention as it may be hard to recover some data.

  1. Data indexing

You can design and maintain a data structure with data indexing to map values in their data set. This technique ensures more accurate data. It also improves scalability. However, be careful of the selection during optimization to preserve data relevance.

  1. Data parallelization

This process divides processing tasks into independent tasks for multiple processors. It lowers the execution period and increases the output. You can handle complex data quickly. Its major benefits include the reliability and scalability of data pipelines.

  1. Data streaming

Streaming processes data immediately after they are received. It ensures real-time data analysis and informed decision-making. Also, it can improve the agility of the data applications to handle high-volume data.

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Implementing adjustments based on validated insights to improve operations

The role of data in business is clear. You can either use it and improve operations or stay behind the trendy technologies. The major benefits include improving customer service and delivering quality products to satisfy shoppers. 

Integrate this into your business, and you can get more insights to be the best in the game. Get started now. 

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