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 In Data Cleansing

These days, the amount of data we produce is growing exponentially. To put it short, every two years the overall quantity of data doubles. The huge amount of data is one thing — there’s the reason we call it big data. The big issue is bad data quality. The old truism “garbage in, garbage out” is more actual now than ever in this era of complex and immense data. As a result, inaccurate, outdated or duplicated data is leading to inaccurate analytics and misguided decisions. Also, dirty data could expose companies to compliance issues since many of them are subject to laws that require that the data is accurate and current. Simply put, data cleansing leads to smarter decisions. It enables cost cutting, reduced timelines, optimized product offerings.

With the continuously growing volume of data and the increased speed and diversity of data that is changed between applications, many consider data cleansing  as one of the main challenges in the era of big data. Let’s think about marketing. To devise a sound strategy, you need a consistent 360-degree view on who your customers are. But this is impossible to achieve if you are using sets of data with errors and inconsistencies. Just think about the fact that, as a general rule, business data decays at around 40% per year. By using incomplete or obsolete data, or data with errors and inconsistencies, will not only loose money but you could also lose important leads. Also, incorrectly formatted addresses could negatively impact your company’s image in front of the customers.

Moreover, as DevOps and big data are becoming “business as usual”, the need for data cleansing tools that can automate processes is ever more pressing.

As you may already know, data cleansing is not an easy process and it involves multiple steps:

  • identifying incomplete, mislabeled or duplicate elements,
  • removing duplicates (de-duplication),
  • appending data where applicable (enrichment),
  • deleting and/or correcting dirty data.

Data cleansing requires resources, time and commitment from key stakeholders in your organization. Let’s suppose you managed to have your data cleansed, de-duplicated, matched, enriched and loaded into your database. You could say that you successfully completed your data cleansing project.

But data is continuously in flux and errors will surely arise.

For example, users could often mistake their zip codes when filling in a web form. If you load the data into your database, all your previous work will bring no benefit. In no time there will be a mix of cleansed and dirty data and to make sense out of it you will have to start the process from the beginning.

But if you use a data cleansing application to continuously validate the data before it is loaded into the database you will avoid re-cleansing the entire data collection just for a small change. The process will be applied only to the data that has changed. In our example, the data cleansing software will verify that the zip code matches the street address, and even correct it. The biggest advantage of continuously performing data cleansing is that you will have closer to real-time analysis of incoming data. In turn, this will produce actionable information.

Conclusion

Businesses measure the success of big data by its capacity to timely allow the extraction of valuable insights. But, as we have seen, you can rarely use data as is. Quite often you need to cleanse it before using it. The activity can be complex and resource consuming.  Fortunately, with a robust data cleansing software and a consistent process, you can be confident that your big data assets will help you make correct and timely decisions. We created WinPure to provide the entire set of features you need to design a consistent data cleansing process for your big data. Here are its main features:

  • Import/export from a multitude of sources, ranging from Text/CSV and Excel to relational databases like SQL Server, MS Access, MySQL and Oracle
  • Data profiling, that allows you to quickly see what problems exist and what actions need to be taken
  • Clean, Correct, Standardize and Transform your data
  • Sophisticated data matching engine, including fuzzy and phonetic match algorithms
  • Automate, enabling you to schedule a time when it’s suitable to automatically clean & match all your data.  You can schedule tasks daily, weekly and monthly and you can execute them without having to open the application.

You can download your 30-day free trial here.

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