Today the volume of data that organizations collect is overwhelming. It is streaming non stop with modern technology helping accumulate it faster and faster. Big data represents an issue in itself and it is an ongoing struggle not only to store it but to maintain it. In other words, it is the quality of data that makes the data meaningful and effective and not quantity. Only clean, quality data allows organizations to find valuable, rightful insights; set up correct business strategies and, as a result, be successful and prosperous. Having said that, the challenge is to tell the clean, good data from bad data, i.e. analyze, assess the data.

Data analysis is a complex, ongoing and, sometimes, costly process that requires discipline, talent, time and consistency. There are various data quality tools on the market but not every business can afford these tools. However, there are some steps that might help companies analyze and improve their data.

To start, find the areas of your data that may be prone to  errors, i.e. bad data. Some attributes of bad, non quality data are:

  • Incorrect data that has wrong spelling, empty fields, spaces, erroneous metrics.
  • Invalid data that contains outdated or unchecked info that is of no use.
  • Unstandardized/categorized data that lacks of standards and established formats.
  • Unprotected data that is vulnerable to cyber attacks, unauthorized access.
  • Uncontrolled data that sustains no observation and proper control, prone to ‘garbage’ accumulating.
  • Passive data that represents unusable waste.

Poor, non quality data can significantly ruin the business. To understand the importance of cleansed, quality data let’s consider some scenarios:

  • Misspelled customer name and/or incorrect address create shipment havoc, harm customer trust, damage company reputation, lose revenue.
  • Unchecked, unverified emails lead to CRM and marketing fiascos, disastrous marketing campaigns, poor response rates, negative ROIs.
  • Wrong info about suppliers disrupts timely, quality production, increases costs.
  • Outdated, useless data ‘eats up’ storage, money.

Business intelligence completely relies on the quality of data. The cleaner the data the better insights are in stock for management teams. The importance of data quality analysis can not be undermined. It is crucial that  austere data quality rules are established and strictly maintained. Data quality rules should be timely discussed and agreed on across teams in the organization. Proper documentations distribution also helps ensure stability of quality data. Data analysis require technology and resources. Saving on talent and tools can be costly. Companies that understand the value and priority of quality, cleansed data will always have an upper hand in business success.

To perform data quality analysis the data should be evaluated across all available applications, i.e. internal systems, databases, mobile, cloud, etc. It is important not only to assess but monitor data flow. It can be achieved with the help of data integration. Real time control is crucial here.

The established input rules, formats and  standards are to be maintained and checked on a regular basis. To keep data clean it is essential to control data flow. There are various build in programs features that can ensure data control. Modern IT innovations such as big data, machine learning, cloud are big assets as well.

Quality control is another not to be missed procedure in data analysis. Data should be supervised and maintained. The process includes multiple actions such as observation, restoration, deduping, scrubbing, purifying and appending. Cleansed, quality and verified data ensures correct analytics and, therefore, correct vision to teams. It is a known fact that data preparation takes most of the time in analytical field. The cleaner the data the more time is freed to results interpretations and decision discussions.

Employee productivity is strongly correlated to quality, clean data. It helps improve collaboration of IT and business units. Allegiance to clean, quality data lead to business clean metrics, insights, better customer response rate, improved marketing initiatives and increased revenue.

Analyze and clean your data timely. Make quality, clean data a strategic advantage over rivals. StrategicDB will be happy to help your organization make your data cleaner, more effective and truly beneficial. We offer a free data quality assessment to help you with your data cleansing strategy.