Is Good Data Quality Part of Your Big Data Strategy?

By Manan Joshi on October 12, 2016

Investments in big data are increasing, and Gartner predicts 75% of businesses will invest in big data by 2017. Big data analytics is one of the top priorities for almost every enterprise today. According to a new study by Experian, 97% of US businesses are looking to achieve a complete view of their customer data. This will help them drive customer loyalty, increase sales and improve strategic decision making. From manufacturing, public services and healthcare, to supply chain, finance and sales and marketing – analyzing large data and getting important insights is becoming standard practice.

However, with rapid data growth at an all time high, a major question comes to mind – How much of this data is actually GOOD?

It’s estimated that bad data costs US businesses approximately $600 billion dollars annually. Bad data can be defined as data with errors or incomplete and inconsistent information, or duplicate data that isn’t reliable for decision making. Bad data decreases your productivity, causes major project delays, wastes valuable resources and can negatively impact your customer satisfaction.

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So how can you achieve good data quality? Here are some ideas:

  1. Identify the source – Bad data quality can derive from multiple sources. It might be human error, or come from legacy systems, and/or it could come from the outside world (customers and vendors). Identify the source and find ways to stop bad data at the point of entry.
  2. Define a Data Governance Model – Define business rules, correct processes, approval workflows and user controls around how data will be entered into the system.
  3. Build a Data Management Organization – Form a team of experts known as the Data Management Organization (DMO) that owns the data. Build a process around data creation and change. Only allow your DMO to create data in the system once this process is completely validated and approved. Data driven organizations are also looking to create a new role to lead the DMO – Chief Data Officer (CDO).
  4. Begin Data cleansing – Data cleansing can be done for existing data in your system of records. Extract, transform or clean data and load the clean data back into your system of record. Make sure you define controls for users to perform data cleansing activity.
  5. Build a culture – Promote a culture for users to understand the importance of data. Raise awareness on how data can impact organizational growth.

Big data analytics can have a huge impact on the business, but full success won’t be achieved until your system’s data is reliable, consistent and error free. That’s why high quality data should be a priority in your big data strategy. Find out how Winshuttle can enable you to achieve high data quality by visiting – http://www.winshuttle.com/ today.


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