Enterprises globally are now looking at data as a strategic tool for driving informed decisions to oversee revenue growth. Gone are the days when enterprise would rely on business acumen or experience to drive their business. Deeper insights into the customer behavior and the metrics this vast amount of data helps the organization significantly address the market needs as well as identify new opportunities for revenue generation.
Data flows into an organization from multiple sources and managing this vast data influx is one of the most challenging tasks that an organization faces. Enterprises globally have implemented data warehouse as part of their data management strategy to allow them to derive meaningful insights needed for their business. Three different processes need to be run on this incoming data to utilize it: Extraction, Transformation, and Loading (ETL). ETL allows organizations to gather data from various sources into a centralized location and combine different types of data into a common format. This data can then be analyzed for remarkable business insights. But before it can be analyzed it has to be extracted as part of the ETL process.
Data integration or ETL process gives organizations access to all the data that had previously been invisible or inaccessible. A more complete data set increases the business insights and can be used for the in-depth analysis required across the various departments/projects in an organization. Such projects need an automated data extraction process to find and prepare data they need for their use.
Data extraction is a process in which data is read and analyzed in order to retrieve relevant information in a specific pattern. After this step, some metadata may also be added to this data.
Data can come from a structured database or many unstructured sources in various formats. It can be in the form of tables, indices and so on or from emails, social media, and so on.
The data extraction steps can be summarized as:
Unstructured data is data that cannot fit neatly into a typical database structure. It comes from disparate sources and is poorly organized, almost freeform. Some typical examples of unstructured data are data that is mined from emails, social media posts, notes made during customer support calls, or conversations with customers on social media. While this is a really useful source of relevant information, it cannot be handled by traditional models of data storage and analysis. A different process of data extraction must be applied to consolidate, process, and refine it so that it can be stored and transformed. And combined with existing structured data.
Unstructured data poses several challenges, some of which we cover here.
When data is extracted, it is moved to another system and analyzed. For analysis purposes, ETL is done in a prior step, so that data from multiple sources are pulled together and analyzed together. The challenge is to ensure that you join data from one source with that from other sources in such a way that they connect well together. This requires a lot of design and planning, especially if the sources are a mix of structured and unstructured data.
The relevance of a particular piece of data cannot be inferred at all in this process. For example, a keyword search which was done only once has no significance later on. Sometimes machine learning applied to such data sets may report correlations as causation.
Organization are collecting data without being entirely aware of it, most of it is unstructured and growing at a huge rate. This poses a challenge regarding the security of the information, and especially if it is information that needs privacy controls. This volume also puts a huge demand on the data warehouse and storage infrastructure.
If data is coming from social media, there is no way to verify its correctness or authenticity. Organizations cannot base their decisions on such data: for example, you could end up suggesting a life insurance plan based on the unstructured, weak data obtained from a Facebook birthday post where the date could itself be a suspect.
As mentioned earlier, to make unstructured data usable, organizations will have to locate, extract, organize, and store the data in entirely new types of databases.
To extract data present in unstructured sources like emails, customer calls, scanned documents, etc., certain data extraction tools are required. Such tools may have optical character recognition (OCR) capabilities, text parsing or report mining capabilities, which automatically identify and extract meaningful information from this kind of unhelpful sources. The challenge of integrating this unstructured data with existing structured data from other systems remains. After extraction of unstructured data, it needs to be integrated with structured data sources to draw conclusions. For example, a sales process may be better analyzed if structured data like invoices, purchase orders, and so on is tied to product and customer data from other sources.
Organizations need appropriate and well-designed data extraction tools to automatically extract unstructured data and integrate it with databases, applications, and visualization tools. In this way, they can get control over unstructured data and use it to make better decisions.
Designing and implementing a data extraction solution that is ideal for your business will take a lot of design and efforts, but it is essential to consolidate all types of data, both structured and unstructured in order to derive insights that make your organization fully customer-focused.