Purchasing a Top-in-line Cleansing Solution: Notice I mention top-in-line. Not to mention, it takes months and years to test and try algorithms that work on complex data structures. Even then teams struggle to achieve accuracy in data deduping and data cleansing. Although this may “seem” like an effective strategy (privacy, control, security), in the long run, it becomes an expensive endeavor costing companies at least $250+K per year just in hiring and retaining talent. The struggles and frustration remain.Ĭreating In-House Solutions: When hiring a data scientist is not enough, companies begin hiring development experts in the hope of launching their in-house solutions. Dozens of organizations spend millions of dollars hiring experienced data scientists, only to end up making them do mundane cleaning tasks. “Most data scientists spend only 20 percent of their time on actual data analysis and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data, which is an inefficient data strategy.”Īnd we are all too aware of this fact. These analysts spend almost 80% of their time fixing bad data. Unfortunately, most organizations hire data scientists to clean and fix data instead. Data scientists, by definition, are experts who study data, derive key insights, and helps organizations capitalize on those insights. Hiring Data Scientists: This is usually the first solution companies choose. Investing in a Data Scrubbing Tool Vs Hiring Data Analysts, Vs, Creating In-House Solutionsīefore we talk about the tool itself, it is important to discuss the two other options that companies commonly use to solve data quality problems. Let me walk you through how a data scrubbing tool helps and why you should consider investing in one. With the emergence of apps, metadata collected via devices, multiple third-party platforms like social media and marketing platforms – organizations are literally drowning in data. While data formats and structures a few decades ago were quite simple, they are now extremely complex. If you’ve been handling data, you would know that dirty, duplicated data is a problem that organizations have been struggling to manage for ages. Data scrubbing, also commonly known as data cleaning is a process that refines your data by removing duplicates and fixing unstructured content.
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