- Is your company’s revenue & profit margins getting eroded because of inefficient data management?
- Is your company’s customer service performance below par because of incorrect or insufficient customer information in your CRM database?
- Is your operational cost sky rocketing because of wastage of resource time & process delays due to lack of good quality of data?
- Are your marketing strategies not yielding the expected outcomes as they are possibly being targeted to the wrong customers?
4 checkpoints to improve quality of your data:
Basically, any lack of diligence towards any of these key areas will lead to a compromise in data quality & eventually give rise to the dirty data. In this dirty data lurks the data monster which costs a company much more than what meets the eye.
Let’s have a look into the role these checkpoints play in improving data quality of your company:
1. Key Data Entry Points
The point from which the data enters your company is critical, as for most organizations there is a manual data entry process involved. This makes it vulnerable to a range of human & process errors like errors due to ambiguous data, errors due to value representation consistency, errors due to change induced inconsistencies and errors due to missing values.
Dealing with the Data Monster at the point of entry:
There are a couple of ways to ensure reduction in dirty data entering your database:
- Data entry automation – With the onset of machine learning, automating data entry processes is also coming up as an ideal solution to increase data accuracy. Predictive analysis and machine learning algorithms can play a vital role in increasing data accuracy as they reduce human errors, make data entry effortless and save time.
- Outsourcing your data entry processes – Many companies who have huge amount of documents/data to process usually opt to outsource their data entry process to offshore companies to reduce costs and resource time. This also increases the accuracy of their data as these outsourcing companies have data entry & data preparation experts who employ processes such as double key-in method to ensure high rate of data accuracy.
2. Enterprise Information Landscape
Data Silos, they are pockets of data created in your organization cause of which dirty data increases exponentially & in turn feeds the Data Monster.
What are Data Silos?
Data Silos are pockets of business-critical data or information stored in your organization with different departments pertaining to the same entity. For e.g. The contact details of Customer X will be available in the marketing database, but the customer relationship team would have an updated version along with information about the customer’s interests.
The data silos is created because of lack of effective customer relationship management system or ineffective organizational data flow mapping.
Dealing with the Data Monster in the enterprise information landscape:
It is the duty of the Chief Data Officer (CDO) to map the data silos in the organization, check the data quality of each silo and integrate all the systems. This task requires creating sponsors and stakeholders in the organization to map all the different department, processes and gain the perfect synchronization.
To quote from Gartner:
A chief marketing officer (CMO) looking to execute marketing campaigns across integrated, multichannel marketing platforms is likely to want to know that key marketing databases have obsolete addresses, missing phone numbers, and duplicate and inconsistent marketing preferences.
Remove data silos from
your enterprise information landscape.
3. Data Purchased from Data Sellers
Many organizations purchase data from data providers/sellers for marketing or operational purposes. The data quality provided by these sellers are of major concern as the data may be outdated, fake (created by bots or from fake social media profiles), or it may miss out on certain critical information.
Including this kind of data in your data management system can compromise the overall data quality of your organization.
Dealing with the Data Monster sent by data sellers
It is crucial to cleanse the data that is purchased from such sources. A range of data cleansing techniques are available to de-duplicate, validate and analyze such dirty data and improve its quality. This can be done by using data cleansing tools available online or with the help of a data cleansing service provider.
4. Data Management & Maintenance
Data decays overtime – this is a fact which many companies overlook. This can be seen as eggs laid by the Data Monster to ensure its survival.
71% of B2B marketers reported outdated data as their primary challenge for maintaining data quality
Just developing a system with automated data entry processes, removing data silos and cleansing data once does not mean you have done what it takes to ensure the data quality of your company is up to the mark. Data decays with time and if left unattended it would increase over the years at an exponential rate.
Dealing with the Data Monster of mismanagement
Hence, it is essential to cleanse your data at regular intervals to update, validate and monitor the quality of organizational data. Maintaining data quality through cleansing will ensure the Data Monster doesn’t appear again.
Destroying the data monster
Advanced analytics and advanced analytic tools, accompanied by the maturity of data analytics experts, make the task to collect, monitor, cleanse and analyze humongous amount of data highly convenient.
Automating data entry & employing various data cleansing tools as mentioned above can destroy this data monster & provide you with the information required to take the right business decisions.
Big picture on data quality
One thing that companies can do to solve their data quality issues is to simply get started. They can start by analyzing these four check points and reach upon a solution that fits their organizational ideals to ensure high data quality dimension. With more prospects coming on-board and new markets being discovered data is always going to grow a continuous process in itself. So waiting for that right moment to tackle data quality issues would be sheer foolishness.
Take out time to assess what data quality means to your company or enterprise, to create ripple-effect of improved customer service, better customer experience, high conversion ratio, and long lasting customer retention. Considering all these as returns on investment would not at all be wrong.
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