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:
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.
Define a Data Governance Model – Define business rules,
correct processes, approval workflows and user controls around how data will be
entered into the system.
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).
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.
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.
Article From: winshuttle.com
