Business intelligence is critical to any organization’s success since it is the base for correct and timely business decisions and strategies. Effective analytics can not be imagined without reliable, precise, faultless and clean data. Clean data is precious but it does not happen on its own. Quality data requires complex and multiplex preparation that includes:
- Data acquisition: data comes from various sources and not always in the best of formats. It can be unstandardized, with missing information and can have data points that are unformatted which will break your data processes.
- Multiple Data Points: Browse through data fields and get to know your data well, i.e. its volume, its attributes and their meanings, values, types, ranges, etc. Learn how data attributes relate to each other, perform some metrics analysis to understand your data better. Do not be surprised that the same data is stored in multiple locations which is one of the reasons it is being ignored on day to day analysis.
- Data Cleansing: It may be the most difficult, time consuming and complex process. Clean data does not tolerate missing attributes or blanks. It should be well formatted and have correct spellings. Duplicates create noise and should be removed by the deduping process. Outliers need to be recognized and dealt with. Another part of data cleansing procedure is data validation. Records tend to be changing with time and only updated information deserves attention. Data should be standardized to avoid errors. Sensitive information should be properly addressed.
- Data transformation: To get complete and concise insights it is necessary to enhance the records with additional data sources and therefore complement your data. Formatting needs to be adjusted accordingly. Therefore, transforming your data in a way that makes it easy for you to do your analysis is key. This can be done in your business intelligence tool or directly in your data sources.
- Establishing Data Hierarchy for Multiple Sources: It is not uncommon to gather data from multiple sources for the same user or unique ID. Therefore, having a hierarchy on what data sources takes priority is key, you may need to establish the hierarchy not just by data source but also by identifying different fields from different sources which take priority.
Data preparation takes time and requires skills and knowledge. The result is clean, quality data but what are the real advantages and benefits of data preparation? Let’s consider just some of them:
- Improved data quality. Cleansing process allows to exclude faulty records, remove duplicate accounts, validate and update the records. Cleansed data provide clean metrics that help derive more accurate insights, refine business strategies, define soft spots and find proper solutions to the issues.
- Mistakes corrected. Process of data preparation speeds up finding datasets’ errors. It allows not only to find some bugs and wrong records but helps avoid future inaccuracies. In return it saves time and money. It improves workforce productivity and frees a tremendous amount of time to be spent on other important tasks.
Data preparation does require a lot of time, effort and skills and maybe the task that is missing in your day to day operations. Often businesses lack resources to conduct proper data audits on their CRMs and Data-sets. While, analysts are pressured by deadlines, simple exclude potentially key data points from their analysis due to lack of time on data cleansing or data standardization. StrategicDB can help, we are a full service data cleansing company with the focus on data preparation specifically for marketing and sales analysis. Contact us today!