Developing Better Data Quality - Analytics Angels

Developing Better Data Quality

Initial Steps Towards Data Quality

Did you know 97% of U.S. Companies feel driven to turn data into insight? That means most of those companies look to data to understand their customer needs, find new clients, and increase value. However, 92% of companies think they have a data quality issue. Knowing that you have a (data) problem is the first step to finding a solution.

Here are some key takeaways from Gartner Magic Quadrant Challenger, Experian’s, whitepaper on data quality in 2015:

  1. Reduce human error. Identify where information is exposed to this error and find technology that is lowers this risk.
  2. Appoint a central data owner and invest in staff. Someone to handle all aspects of data and is an expert.
  3. Conduct audits. Be proactive and look for common errors, maybe even invest in detection software.
  4. Make it an organizational concern. Use tools consistently across departments and ensure data is complete as collected.
  5. Track metrics that are beneficial. Make a case for data quality by tracking key metrics impacted by data and how those affect business outcomes.


Why Companies Should Care

Let’s back up a little bit from last week’s tips on initial steps towards data quality and talk about why companies should care. Based on Experian’s report, as marketers continue to automate processes and do so within much tighter timeframes to keep up with the consumer, maintaining and analyzing accurate data will continue to be a critical component. Top drivers are:  understanding customer needs, finding new customers, increasing value of each customer, and securing future budgets.


Here are some trending reasons on why companies care about data quality in 2015:

  • Increase marketing program efficiency, enhance customer satisfaction, and enable for more informed decisions
  • 87% of companies are using predictive analytics* across their business in one way or another and those who use it have significantly increased profits in the last 12 months, this requires clean data to work properly
  • Customer Experience Management (CEM) is a hot topic for companies today, an increasingly important part of this management is done through data because you need data to develop better personalization


Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns (Source Wikipedia)


Improve data with progressive profiling

As a marketer, you want to always be improving the customer experience so they keep coming back. One particularly popular topic for companies right now is what it takes to have a well-oiled Customer Experience Management (CEM) operation. How are successful companies leveraging data to improve this experience and be profitable?


For starters, they have a more sophisticated data management strategy. Disorganized and low quality data is simply expensive background noise. Actually investing in some tools to mitigate your data integrity issues would be a great step in the right direction. In comes progressive profiling.


Salesforce’s Pardot explains progressive profiling as the automation of displaying new form fields to prospects based on data points previously collected. By using conditional fields, you can use less form fields to maintain high conversion rates and still capture new information.


ReachForce, MediaMath, Turn, and Rocket Fuel are some vendors that offer this service. They all offer some sort of interaction optimization and automated data cleansing across digital marketing channels. By using this technology, companies can start augmenting databases and hone in on relevant data.

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