There has been a substantial stress in the current landscape of data analytics. The massive volume of data these days demands tough analysis tools, which can manage varied data structures at scale and can also help reduce IT expenses. The intensified pace of business necessitates self-service products intended for business executives. Organizations making use of full-stack legacy solutions are realizing that they are no more capable of keeping up with the pace of a recently evolving data environment. Simultaneously, the upcoming generation of data scientists and analysts are moving away from the legacy technologies altogether, giving preferentiality to open-source options. As of today, organizations leading the data innovation are supplementing their legacy systems with powerhouse analytics tools, which are better at management of the latest challenges faced in the preparation and analysis of data.
The Challenges in Data Preparation
The preparation of data is certainly the most time taking, prone to errors, and expensive step in the process of data analytics. Some of the businesses are until now making use of suboptimal techniques such as unending Excel commands, manually programmed authentication scripts, and tools such as SAS, which are developed for analytics and are not capable of data preparation. All of these tools are potent but they tend to struggle under the load of outsized data files, data from diverse systems and in a wide range of formats, and an amplified pace of the industry, which has absolutely no leniency for human error.
World before the Advent of Data Wrangling Solutions
Prior to the advent of data wrangling software solutions, businesses were dependent on the Excel sheets, manually programmed authentication scripts or ETL (extract, transform, and load) techniques to perform the preparation of data. As the cloud computing technology and big data gain prevalence, it usually requires all of these three techniques to fulfill the data preparation task. Although businesses were utilizing 80% of their time for the preparation of data, they never invested in the data wrangling technology because the idea was not born yet.
A large number organizations make use of SAS, a market leader in business intelligence and statistical analysis for almost past four decades, to accomplish the task of data preparation as well as analysis. SAS is certainly a powerful statistical tool for data scientists. However, over the years, organizations using SAS find themselves in the overabundance of SAS codes build up over the years, making it expensive to write and maintain, challenging to track lineage from and almost impossible to share with non-technical business executives. This made these organizations realize that they need to look for an advance tool specially designed to perform the task of data preparation, which will eventually put their investment and time to its best use instead of remaining focused toward tiresome data preparation scripts.
The Growth of Data Wrangling Solutions
Among all other steps needs in the precise analysis of big data, nothing is more time-consuming than the preparation of data. The 80% of the entire analysis procedure consists of data preparation before performing any type of analysis. Efforts to address the issues of data preparation initially started in the education sector. This further paved the way for business ventures to build specialized data wrangling solutions and connect with an increasingly developing assortment of data sources. With the aggregate prevalence of big data, technology analysts are more focused on the trends of the data preparation market.
Optimizing SAS with Data Wrangling
The reason behind the dominance of SAS as a market leader in business intelligence and statistical analysis for the past four decades is that it empowers data scientists to perform the optimal statistical analysis. However, it lacks the capability of data preparation. Contemporary and effective data preparation solutions now empower business to accelerate the process of data wrangling, which eventually allows SAS to be leveraged to conduct the refined analytics for which it has been developed for in the first place.
By incorporating data wrangling software solution into analytics setting prior to the SAS analysis, businesses have been able to explore and prepare 100% of the complicated consumer data gathered through online resources. Insights from such data enable businesses to better recognize and address the requirement of their consumers. This substantially helps business to save an estimated cost of 4 – 5 million dollars over a year. Data wrangling software solutions make the process of preparation of data more accurate, proficient and instinctive. You can augment your SAS investments with data wrangling software solutions.
Revolutionize Future Analytics with Data Wrangling
An increasing number of businesses are embracing self-service techniques for the preparation of date in order to leverage their SAS investment by letting them focus on their best capabilities, which is analysis. Businesses get to save a great deal of money previously spend on IT, democratize data analysis, and eventually accelerate analytic outcome, a move that is apparently practical for a large number of organizations. Data wrangling software solutions have been established specifically for this reason. They not only benefit business users but also play a major part in revolutionizing their analytics tools.