Today we all hear that data rules, data is critical, data is core, data is the most important, essential and vital. However, to get clean, accurate, flawless data is not an easy task. Despite the fact that modern technologies, tools and programs allow companies to collect tons of data, human input still remains a substantial part of data collection. Computer bugs and glitches play their role of incorrect information. Technological innovations sometimes make it hard to cope with. And it is no wonder that companies are struggling to have faultless, actionable, reliable and thus, insightful and effective data.
Nobody can swim in a pool without water as well as companies are vulnerable and powerless without data. Swimming in a pool full of contaminated, dirty water is not only very unpleasant but extremely dangerous. Same with the data. Inaccurate, wrong data leads to inaccurate numbers, reports and then we get the snowball effect: dirty data (layer one) creates wrong numbers (layer one), incorrect initial data generates invalid reporting, dashboards (layer two), incorrect analysis trigger imprecise decisions (layer three), erroneous conclusions accompany faulty future business strategies (layer four), misleading approaches may damage and ruin company success and prosperity (layer five).
So data is the base. Its quality and integrity are extremely critical and in a majority of cases are vital to organizations of any type.
Data, as our bodies, requires hygiene and data cleansing becomes a mandatory, needed and ongoing procedure that maintains business success.
There are many definitions of data cleansing or data cleaning nowadays. The process is being called data cleaning, data cleansing, data scrubbing, data massaging, data hygiene, etc.. Don’t be confused by so many descriptions. In the end, it is an action of purifying the data making it free of any imperfections and impurities, like the water in the pool. Though it may sound easy but data cleansing is a pretty complex succession of actions. Every process starts with setting up an objective. In our case, the end goal is the clean, accurate, effective data set.
Definition of data cleansing or data cleaning?
Data cleansing is the process that helps reveal, repair, validate and fix dirty data. The following are just some examples of dirty data:
– There are many data management tools that you can use to stop duplicates from coming in the system or processes that make sure the format of your data capture is standardized. However, duplicates are sometimes a necessity in your database.
– Not properly standardized or formatted data makes it hard to run analysis or have any segmentation.
– Any kind of missing information (for instance: titles, phone numbers, zip codes, email addresses, etc) is just another example of incomplete data.
– Some data is well formatted, correctly spelled but simply invalid. It makes it being not only inaccurate but useless, misleading and ineffective. The inaccurate data is caused by two reasons: outdated information, typically data that is greater than 18 months may no longer be relevant and bogus/invalid data being created.
The task of data cleansing is to repair incorrect input, standardize and normalize data formats, data deduplication which is the process of removing duplicates, fill in missing information, verify and validate data. Prior to starting data cleansing it is advisable to conduct a data audit to understand what data hygiene issues your database has. StrategicDB offers a free data audit to help you with your data cleansing strategy.
Any modification of the data set or database(in our case data cleansing) is a serious and important process that require definite skills and knowledge. Professionalism is never enough and always pays off. StrategicDB helped many companies to clean their data sets, improve their data quality and integrity, thus assisted in refining companies’ strategies and improving business performance . We are happy and ready to assist you with cleansing your data and making your data work for the prosperity of your business. Don’t hesitate to contact us at email@example.com or +1-877-332-4923.