Streamwise Blog Datasplaining: Data Cleaning

Today on Datasplaining - a recurring series where we demystify different data topics and discuss their importance and relevance to you (condescension and frustration-free) - we dive into Data Cleaning and how it can drastically improve your business's workflow and ROI. So what exactly is Data Cleaning and why should you care about how clean your data is anyways? Data Cleaning (along with other key Data Science activities such as data preparation, data modeling, reporting and analysis) is a major contributor to data quality (DQ). Since you are reading this article we will assume that you want to up your data operations and care about the quality of your data, especially considering the following eye-opening findings: Research firm Vanson Bourne found that 91% of IT decision-makers think their organization needs to improve the quality of their data.Forrester Research found that less than 0.5% of all data is ever analyzed and used.IDC forecasts that the amount of data created over the next three years will be more than all the data created over the past 30 years.According to a 2021 study by Gartner, every year, poor data costs companies an average of $12.9 million.So if there is more data than ever, why is so little of it being used to drive business decisions?A major reason for such low data utilization is low quality of data aka "dirty" or "polluted" data. Without high quality data, your company's ability to make data-driven decisions is severely impacted. So, what is the solution to messy data? Cue data cleaning.What exactly is data cleaning?Data Cleaning is the process of standardizing your data into a uniform dataset by culling through the data and correcting or removing any incorrect, corrupted, erroneously formatted, duplicate or incomplete data, leaving you with fresh, "clean" and useful data. When data is not clean, outcomes and algorithms become unreliable and automation is impossible. In many companies, Data Cleaning is still a largely manual process (i.e. neverending spreadsheet rows, lots of complex formulas, lots of copying and pasting) because the data is decentralized and non-standardized. In fact, Anaconda's '2021 State of Data Science' report, found that on average data scientists spend an estimated 39% of their time on data prep and cleaning.So, how do you go about cleaning your data?Generally speaking there are three approaches to data cleaning:Work with your development team to create custom tools to help automate and standardize as many data cleaning tasks as possible.Attempt to manually clean your data using common business apps such as Excel.Partner with a company whose expertise lies in cleaning your data.Let's explore these three approaches:1. Leveraging your development team for data cleaningIf you have access to developers, you may partner with your team to engineer a series of tools and scripts to process your cleaning workflows with the hope of automation and unlimited scale. You will need to make sure your development team truly understands your data and business needs, commits to hands-on management and support of all your business data and is staffed to grow with your needs. Your development team will need to be ready to address issues such as your data source's file formatting regularly changing, lack of unique IDs needed to scale automation, and data availability outages. Going this route inevitably introduces a long-term need for ongoing, complex maintenance by expensive, specialized engineers, otherwise your business will end up with blocks of critical missing data, hurting downstream financial and analytics workflows.2. Manually cleaning your data using common business appsMost companies can't spare the development resources, or don't have the technical expertise, and end up using Excel or Google Sheets to clean their data. Manual data cleaning in Excel is not only tedious and frustrating work most employees hate to do, it is also extremely time consuming and error prone. Laptop hardware and Excel are not designed to handle millions of rows of data quickly. The process can be painstakingly slow, even causing your application to crash, losing your recent work. And while Google Sheets at least moves the processing power requirements off your local machine and into the cloud, it can handle even less rows of data than Excel. Finally, with so much repetition, it is easy to make errors, causing you to have to repeat steps of the workflow, if you catch the errors!Despite these challenges, most companies proceed with Excel-based manual cleaning using an array of Excel formulas and lots of manual intervention along the way. Excel is used to extract useful information from the many "raw" file reports and move that data into a standardized format using a combination of VLOOKUP and Fuzzy Lookup formulas, copy and pasting, and column remapping. Issues along the way such as incorrectly formatted data are manually corrected, extra data not needed for the specific workflow is deleted, missing data attributions that are needed are manually added, data is mapped to internal identifiers, and you finally end up with an export of the cleaned data you need. You then repeat the process each time a new raw file arrives, which can be every quarter, every month, every week, or even every day in some cases, multiplied by the number of data sources you have.The final step is to validate your data through quality assurance. Does your data make sense? Does it provide any insights? Can you identify trends from your data? If not, is that because of a data quality issue? False conclusions derived from incorrect or "dirty" data can hurt business performance and lead to poor decision-making.3. Partnering with a company to clean your dataAnother option for a busy business team is to find a trusted partner to take over the data cleaning duties and become the data custodians for the business, eliminating time-consuming tasks and unlocking critical information. When selecting a company to partner with, businesses must consider a variety of questions based on their needs. Does the company you are hiring have real-world experience cleaning and understanding your data? The quality of your data will be directly affected by your partner's ability to understand and match your standards of cleanliness. Once cleaned, will the data be easy to access? Will the data be available to everyone in your organization, in the format they are able to consume and derive the most value from? What will happen to your clean data? How and where will it be stored? Will this partnership be easy to get started with? What should be done with your existing (historical) data? All of these questions, as well as many others should be addressed by your data cleaning partner of choice.Now that we've covered how to clean data, can you tell me more about "dirty" data in the film and tv industry?In our last post, we discussed ad-supported streaming pain points, which is where you can find some of the most problematic data in the entertainment industry. Due to inconsistencies in the data and reporting formats each ad-supported channel provides its content suppliers, it's difficult to get a full picture across your business or for one title's overall performance across different channels. This results in the inability to make informed decisions based on your data.How can Streamwise help you with data cleaning?With Streamwise, you can finally say goodbye to endless Excel spreadsheets and having to manually (and painstakingly) unify your data. Our highly scalable 'data spa' cleans your data in order to provide you with the best quality data possible. All of your clean data is stored in our secure data warehouse making it accessible to everyone in the company, across all orgs, unlocking powerful insights so that you and your team can make smarter, data-driven decisions.Yours data-fully, Team StreamwiseP.S. Request a demo and we'll work with you to find a solution.