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Transforming Dark Data into Actionable Insights

What is Dark Data? Big Data? Data Transformation?

Big Data is the mass amounts of data that is generated every minute of everyday by people, companies, machines ... Dark Data is a subset of that. Dark Data is the data that has been collected but you can't do anything with it. You may not be able to do anything with it because its in the wrong format, you font have the tools to convert it or it is missing or incomplete.

Source: Datumize

Dark data provides businesses with a huge opportunity to gain valuable insights which can drive their business and reduce their asset failures.

Data transformation is the process of converting the raw data into a format that users can quickly analyse. To get insights out of your data, you need to transform it into something meaningful.

There is no denying the fact that big data has been taking the world by storm for more than a decade. It started at the beginning of the 2000s, the tech biggies like Google and Yahoo began working on new methods to manage complicated and massive quantities of data. This technology is what initiated the big data revolution for the mass market. But over the years, Big data has become less scary and usable for small and medium businesses.

Multiple open-source methods have enabled organisations to process massive amounts of data cost-effectively and efficiently.

Now the question that pops up here is, what is the need to transform data? Why should you opt for data transformation to convert dark data into actionable insights?

Why is it necessary for a business to transform data?

Dark data sits there unused and unusable. Every business generates a vast amount of data daily, but it is not useful until it gets transformed into a consumable format. This transformation could be as simple as altering the data from spreadsheets to graphs.

One of the primary reasons for data transformation is that you can create different pieces of data compatible with one another, move them to another system, and then join with other data to drive useful business insights.

Businesses can also use data transformation for extracting values from different data types. Instead of choosing to overwhelm your systems with multiple and, most of the time, unnecessary records, you can use different kinds of data transformations for filtering out irrelevant data.

For instance, if you want to generate a report of all the potholes in a particular period then applying the filter transformation will prevent the destination system from getting strained unnecessarily because the analysts will access only the relevant records. Moreover, storing fewer and only pertinent documents of the destination system means a lesser memory consumption during the data processing. Thus, it reduces execution time.

Data Structure

Data transformation helps in combining unstructured data with structured one. For example, you can shift data into new storage, like a cloud data warehouse and easily change the data types. In addition, it becomes easier to add other information to the data like geo-location or timestamps. When you get your data transformed into a specific format, you can create a unified view of your business operations, allowing for result-oriented business decisions. An example of this is performing data aggregations like comparing sales data from different regions.

Different forms of data transformation

1. On-Premise Data Transformation – With on-premise transformation allows businesses to extract transformation, enabling you to load crucial data quickly. An on-premise data transformation tool also translates to increased regulatory compliance and higher security management.

2. Mutual data transformation – Mutual data transformation involves a lot of coding. Therefore, it needs you to hire a dedicated development resource for getting the job done. Additionally, manual data transformation takes up time and resources, especially when dealing with different file formats. There is also the risk of human error and accidental deletion of essential business data during manual transformation.

3. Cloud-Based data transformation – The pay-as-you-go feature of most cloud-based data transformation tools offers businesses the freedom to scale up and down when needed. This freedom is one of the reasons why cloud-based tools are so popular.

Creating Meaning

Data transformation makes data organised, and it allows organisations to bring their data from different locations and formats to meaningful information. The formatting process improves the data quality and protects the applications from errors like null values, incorrect indexing, unexpected duplicates, and incompatible formats. The proper data transformation practices help ensure compatibility with your systems, applications, and different data types.

Most businesses cannot transform their dark data on their own. itus is a cloud-based solution that unlocks insights and information of your Smart City through our artificial intelligence software.

Dark data is like unrefined gold, it is precious to businesses, but to create value from it, you have to transform the gold into something else.

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