As scientists gain greater access to immense amounts of information, they need more efficient ways to round up and organize that data.To learn more how scientists are using data wrangling to do so, checkout this infograhpic created by Rutgers University’s Online Master of Information program.
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Growth of Data, Internet Users, Mobile and Wearable Technology
Digital technologies have enabled us to generate an incredible amount of data about virtually every aspect of life. Various sources have contributed to this staggering abundance that can quickly overwhelm those who are not used to such amounts. Fortunately, scientists have developed ways to mine the data to extract discoveries and insights. They can also be used to drive smarter decision-making in different fields of endeavor. Businesses can utilize them to analyze their performance in product sales, marketing campaigns, customer satisfaction, employee retention, and the like. The end result is the ability to make more informed decisions in the future.
The number of Internet users worldwide has grown tremendously since the turn of the millennium. From 400 million in 2000, the figure has ballooned to 3.2 billion in 2015. Global Internet penetration grew from 6.5%to 43% or nearly half of the entire world. The rise has been quite phenomenal thanks to improvement of infrastructure and improvements in technology. Mobile penetration has also contributed to this as many browse the web through their phones. In 2000, 63.2 million Americans owned a smart phone. In 2015, the number jumped to 212.6 million which represents about two-thirds of the US population. Wearable devices are gaining traction as well with 21% of people owning one in 2014.
Data Scientists and Data Wrangling
Data wrangling is the most time-consuming task for scientists who are working in this field. About 66.7% of their time is devoted to this process in which they painstakingly clean and unify complex data sets. Scientists go about sorting and organizing the information they gathered into a manageable structure for storage or immediate use. There are plenty of skills and tasks that are involved in this line of work. They have to do preliminary research, develop computer programs, acquire knowledge about each subject, make computations, do objective analysis, and communicate their results. All of these take a lot of time and effort.
Strategies to Improve Data Wrangling Efforts
The sheer volume of information that requires study can easily overwhelm in terms of work load. There is a need to train and hire more scientists to address the shortage in the field. Almost 80% of practitioners say that they are understaffed and require additional help. Clearly, the amount of data being produced is outpacing the rate at which new experts are entering this realm. There have been increases in training efforts to bridge the gap. IT leaders identify development and realignment of existing staff as their leading approach to address big data skill gaps. Time will tell if these are enough.
Another strategy is to empower existing data science teams within companies. There has been a 125% increase in the number of organizations that implemented data-driven projects recently. This is a multi-pronged approach. The teams are provided with additional resources to acquire all necessary tools. Work is hard when understaffed and new members are hard to come by but at least the teams are given all of the support that they need to improve efficiency. There should also be clear goals and objectives on projects so that efforts are highly focused and never wasted. Whenever possible, the headcount must be increased along with continuous investment in training.
Data Can Provide Insights for Organizations
There are several challenges that organizations need to prioritize if they wish to make meaningful progress. One is the analysis of large volumes of data. They need to be able to tame the beast and make it sensible. The type of analysis being conducted should be scrutinized as well for areas of improvement. Teams have to be on the lookout for new possibilities at all times. More sophisticated predictive models could also aid in the process, addressing blind spots and improving accuracy. The analysis of information from different data sources is another big challenge since they have different formats and cannot be easily mixed. A lot of people are also calling for faster delivery of data to initiate the process.
All of these challenges seem like a lot to take on but organizations push through with their investments knowing that their efforts will ultimately be rewarded. There are plenty of advantages for those that implement data initiatives. Foremost of these is the enhanced ability to make strategic decisions. Another is the capacity to steer operational processes in a better matter. The company’s insight into their customers will also be improved thanks to the information collected. The investments also make financial sense if one considers the cost reductions that they are able to bring about. Decisions are not only better; they can also be made faster which leaves the competition behind.
Impact of Data Analytics on an Organization
The significant effects of data analytics on companies that use it cannot be denied. It is definitely making waves all across these organizations. About 41% of them reported that they got new hires to bring in new skills for this specific purpose. It is, after all, a fairly new branch of science that has relatively few practitioners. They need to acquire these new talents and continue to develop them to reach their full potential. More than one-third had measurable increases in their return of investments. Clearly, the time, money, and effort spent on these kinds of projects are proving to be worth it.
Analytics is also responsible for freeing up employees from administrative tasks in favor of more productive endeavors according to 30% of the respondents. Roughly a quarter of those surveyed were able to automate the ability to collect insights from their data while 15% used the same insights to change their organizational structure. This shows the immense faith that the people have in the process. It also points to the profound difference analytics can make. It is an effective eye-opener which backs up its claims with a load of evidence. It is up to each organization to act on the insights provided.