According to AnalyticsWeek.com, “Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.” But no enterprise can remain competitive and profitable in the longer run without utilizing the large amount of data from collected diverse sources optimally. In addition to using the right data collection methods, enterprises need to focus on accessing and analyzing the large datasets rapidly and seamlessly.

Data wrangling helps enterprises to leverage big data by cleaning, simplifying and restructuring large amount of raw data collected from diverse sources. The process adds value to the data by converting the messy and unstructured information into a more usable format. The wangled data make it easier for enterprises to gain informational insights and take information-based decisions by accessing and analyzing the big data effectively. There are a number of reasons why a business cannot leverage big data fully without focusing on data wrangling.

6 Ways Data Wrangling Helps Enterprises to Leverage Big Data Fully

1. Link Diverse Information

Each organization these days collects information from various sources in varying formats. Hence, it becomes essential to convert the structured and unstructured information into actionable data. The features provided by data wrangling tools help businesses to connect information of varied types and formats. The connection helps decision makers to get a complete view of the big data and generate informational insights.

2. Make Data Accessible

An enterprise cannot accelerate data-driven decision making process without enabling various stakeholders to access big data quickly and seamlessly. The data wrangling solutions make complex and messy datasets accessible to various users. An enterprise can use the right data wrangling solution to enable employees to access and understand the information regardless of their technical skills.

3. Add Credibility to Big Data

The quality and reliability of information vary across sources and channels. A business cannot take the right decisions at the right time without identifying and separating trustworthy data. In addition to cleaning and simplifying big data, the data wrangling solutions help enterprises to add credibility to the information. Hence, it becomes easier for decision makers to take decisions using only authentic and credible information.

4. Perform Cross-Dataset Analysis

As noted earlier, data wrangling enables businesses to connect information collected from diverse sources in a standard format. An enterprise also has the option to plan and implement data wrangling as a standardized process. It can repeat and reuse the standardized process to conform diverse datasets to conform to a single format. The single format makes it easier for businesses to take better decisions by using cross-dataset analytics.

5. Spend Time only on Data Analysis

The decision makers cannot boost customer experience, improve operational efficiency, and gain competitive advantage without focusing only on big data analysis. The data wrangling process enables decision makers focus only on analyzing data and gain informational insights generating organized and actionable data. Hence, the decision makers need to allocate all their time to data exploration and analysis.

6. Implement Machine Learning Algorithms

As an application of artificial intelligence (AI), machine learning makes computer programs learn and improve on their own by accessing data. An enterprise needs to ensure that the computer programs learns, understands, and processes the data precisely while implementing machine learning algorithms. Data wrangling helps businesses to implement machine learning algorithms by making the computer program learn both historical and new information accurately.

7. Facilitate Employee Collaboration

A business cannot leverage big data fully without enabling each employee to access actionable information regardless of her technical skills. The data wrangling solutions make complex data sets accessible and understandable for employees who are not data experts. The easy-to-understand data format helps businesses to take better data-driven decision making process by facilitating open collaboration between employees.

A business can get actionable data by following six important steps in data wrangling – discovering, structuring, cleaning, enriching, validating and publishing. Also, it can clean, organize and simplify large datasets efficiently using three distinct data wrangling methods – manual, semi-automated and fully-automated. But the decision makers must remember that data wrangling is an ongoing process. Like other data analytics processes, data wrangling requires businesses to repeat the important steps seamlessly to accomplish the desired results.