There exists a great paradox in the world of analytics. The least glamorous – and perhaps most boring – step in the analytics process is also the most vital: data scrubbing, cleansing and preprocessing. It would be hard to overstate the importance of data cleansing in the analysis process. It's a step that takes time and patience, reminiscent of this quote by Abraham Lincoln: "Give me six hours to chop down a tree and I will spend the first four sharpening the axe." When working with analytics software, before sentiment or clustering analysis can be performed and successfully yield results, data must be cleansed and preprocessed. In other words, it must be presented in such a way that is easily processed by the analytics software. Unfortunately, the acts of scrubbing, cleansing and preprocessing are tedious and laborious endeavors. Businesses that opt to use Software as a Service (SaaS) analytics tool run the danger of devoting an excess of time and talent working on data cleanup before they even begin the ongoing challenge of utilizing the data analytics software. Once they begin the dive into the software's nuances and intricacies with a team that is wholly unfamiliar with the software product, an entirely new world of headaches may begin. The struggles these companies face when utilizing SaaS analytics products can seem insurmountable, though a ready solution lies in the wings. What Does a Data Scientist Have to Do With Data Cleanup? To fully understand the necessity of having a well-trained data scientist working with your analytics software, it's important to better fathom the nuances of their role. It's overly simplistic to assume that a data scientist merely gathers and reports on data that's been processed through analytics software. A competent data scientist must first wrangle and preprocess the data that will be analyzed, sifting through incoming results and attempting to ascertain new insights – unearthing fundamental realizations that can bring industry advantage to a business or address a problem a company is facing. To do this, the data scientist must ask targeted, industry-specific questions that belie existing beliefs or processes. Once they have obtained the results being sought, they must then communicate their findings and recommendations in a way that resonates throughout a business's hierarchy. Understanding the intricacies of complicated software is necessary, as is having an understanding of a business's or industry's nuances and pain points. These skills are invaluable because data analysis isn't a one-off task. It's an ongoing art that requires constant manipulation and adjustments. Why is Data Cleanup a Necessity? People outside the world of data analysis often fail to realize that the vast majority of time spent on any analytics project is actually spent cleaning up data. In fact, many data scientists estimate anywhere from 80% to 95% of their time is spent on wrangling, scrubbing, cleaning and preparing data. Data clean up is such an integral feat in large part because raw data cannot be sent through an algorithm and yield any sort of quality results. When data is being gathered, it's possible that it's streaming in from thousands – even millions – of smartphones, tablets, websites, surveys, social network comments, and so on. Depending on a given industry, there is a world of considerations to be made as data pours in and is sifted. For example, within the world of aviation, airport names may appear as their full name or code (i.e., John F. Kennedy International Airport or JFK). Scores of minutiae must be accounted for during data analysis; think of analytics like a pipeline. First the data must be cleaned, and then you feed those results through an algorithm and preprocess the data. Next, you feed the result of that step to more advanced algorithms to perform, such as topic extraction. Next stop could be sentiment analysis, and then you refine that result by passing it through additional algorithms. Finally, the result is ready to be shared. The Problem: Talent Scarcity The reality businesses face when moving to implement and manipulate analytics software is that there is a true scarcity of talent capable of doing the job. This is in part because few people will have a profound knowledge of the particular software a company has chosen to use. The skillset of data scientists is so broad and unique that it is not frequently met by people in the job market, and certainly not within the confines of a particular company. When someone is neither attuned to the intricacies of data wrangling, nor intimately aware of the software it poses a risk. The situation can breed a landscape rife with costly and time-consuming errors, which can then lead to talent retention troubles. How Solution as a Service (SolaaS) Can Help Rather than allocate valuable in-house time and talent to deal with the enormous task of data cleanup, businesses have an alternative at their fingertips: Solution as a Service (SolaaS). This groundbreaking model joins the extraordinary skillset of highly trained data scientists with powerful data analytics software. As opposed to a singular SaaS product, or the systems integrator model in which the integrator does not own the software it is implementing, SolaaS guarantees that the data scientists employed will have intimate knowledge of the software being used. This means results will always be predictable and repeatable. SolaaS directly addresses the challenges faced by businesses in a variety of industries, and eliminates the woes faced when a company's in-house team has to learn about the software and properly wrangle data. Of course, fears of talent retention are also alleviated, and pricey consulting costs do not spring up unexpectedly. Finally, the results gathered will be presented in a way that benefits a given company and its leadership, paving the way for smarter, informed decision making. Summary The data scientists employed by the Solution as a Service (SolaaS) model, are expert at every step of the analytics pipeline relevant to a business's needs. They capably target, sift through and analyze data, and then run it through powerful software, exploiting results to a company's benefits. You company's resources and talents can be left to focus on making data-driven decisions and moving the company forward.