When it comes to data, more is not always merrier

Data cleansing is rapidly becoming more difficult and expensive. The growth of non-traditional data platforms like search engines, AI, machine learning, sensor information in the internet of things (IoT), social media, mobile devices and a myriad of other sources has led to a ‘data explosion’. The challenge of managing all this is a big one.

And then there’s in-house platforms

Adding to the data overload are the multiple platforms, databases, applications and legacy systems used within organisations. These can include:

  • Enterprise Resource Planning (ERP)
  • Customer Relationship Management (CRM)
  • Marketing Automation Platforms
  • Supply Chain Solutions
  • Online shopfronts
  • Databases, files and many other systems to help businesses run

As the number and combination of systems grow and data continues to pour in, so does the volume of incorrect data records and the cost of inaccurate data grows proportionately.

Here’s a typical data dilemma

Let’s look at a motor vehicle company and how they manage their customer record. Normally, they would have an ERP system, a dealer management system, a CRM, a marketing automation tool, a warranty system, an online registration tool and a number of other systems.

A salesperson enters a potential buyer’s contact information into their web based online tool. Eventually this may end up in a:

  • CRM managed by another person
  • If there’s a vehicle sale, the invoice may be produced by the EPR
  • The contact information keyed into this system will probably be different to the other two systems
  • Post-sale, the service management, warranty and finance systems will contain the customer contact information

Can you spot the problem?

Data, data entry processes, lack of data governance and limited ways of keeping data in sync mean over time the customer record will differ across systems. This problem extends to other records too.

System Flow Diagram

The Golden Record

Fixing the issue of varied and multiple data records comes down to working with a powerful master data accuracy platform that will provide:

  • Automated processes of identifying and updating inaccurate data
  • Integration processes taking and writing back data
  • Tools that provide proper rules and data governance methods
  • Reduction of human transposition errors
  • Simple tools for fast and easy deployment
  • Strong internal audit trails to validate data output
  • Value for money
Bubble Diagram-2

By bringing all sources of data into a single view, businesses can proactively achieve comprehensive data health, in one location, ensuring:

  • Data accuracy
  • Consistency across the enterprise
  • Accessibility for the entire business and key decision makers

What kind of shape is your data in?

From major system upgrades to dealing with inconsistencies in your data warehouse, to integration of data from a variety of sources, different organisations face different data challenges. To check your data quality, click through to take our free Data Health Check and find out if Mondelio can help get your data in line.