Big data and analytics is not a black box- you can’t just gather and load data and it will return with valuable insights. According to a recent article published by the New York Times, loading quality data in a big data and analytics platform demands a lot of hard work. In the big data era, data scientists spend most of their time in preparing data throughout the project. Call it data janitor work, data munging or data wrangling- it needs a lot of big data IQ to prepare data with ease.
Right from the beginning, you should have definitions of what you require. Big data solutions don’t work on the approach of ‘all data in’. If you are thinking of ingesting low quality data, it will derive nothing less than meaningless insights and ‘noisy data. The big data era requires experts’ data scientists with adequate big data IQ to make effective strategies relevant to the questions that you want answers of by data wrangling.
In corporate big data and analytics projects, myriad challenges confront entrepreneurs including- combination of unstructured, semi-structured and semi-structured datasets. This requires organization of semi-structured/unstructured data from shared drive or SharePoint from master data included in structure of systems- and it requires experts with a mix of expertise in IT and big data IQ. In the big data era, with myriad big data solutions, there are many products in the market to work in tandem with advanced analytics tools and techniques and to assist data scientists.
ORGANIZE AND EXTRACT
This is the phase of the project which takes most of the time. Organizing datasets can be encapsulated in certain steps including- translating intricate codes into usable data, handling erroneous or incomplete data, mapping mutual fields, duplicating application data to transform the complex data into self-describing data. This complex process demands a lot of focus and concentration with expertise and technical skills in the big data and analytics space- and it goes without saying that people with adequate big data IQ are in the high demand in big data era. However, a further difficulty of this process is that you can’t show anything to your stakeholders when they expect nothing less than demos with fancy visualizations from you. They expect it rapidly from you, but you are stuck with the data.
You have finally reached there. After going through the above steps, you can load your data into big data solutions platform, and the astonishing work of data and analytics will start. With structured, clean and organized data, the visualization and analytics phase will escalate to next level quickly by offering real value to the stakeholders.
Right from gathering to analyzing, preparation of data demands a lot of hard work and time. It can’t be achieved by any shortcuts also. In order to achieve meaningful insights form visualizations, analytics and big data solutions- you will be required to invest time and energy to build a good quality data repository. Don’t forget to set expectations with your current and potential stakeholders so that they are prepared to invest in data wangling process.