There’s a lot of talk today about Business Intelligence and Analytics, and about Big Data. But there’s far less acceptance of the critical importance of data quality.
Here’s why it matters: Gartner reckons that bad data is costing the average organization $8.2m a year – and the bigger you are, the more it hurts.
If you simply stand still, this data quality gap gets worse, fast-fuelled by exponential growth in the volume, value and velocity of data, and amplified by:
- Poor application design
- Inadequate business processes
- The inability to integrate multiple data types and sources
There are, however, a growing number of organizations that are successfully mastering these data quality challenges. Their leadership teams have a laser-like focus on the quality, completeness and relevance of their data.
In addition to the volume, value and velocity of their data, they pay critical attention to the veracity of their data: Is it complete? Is it accurate? Does it tell us what we need to know? And, can it be relied upon as the foundation for key business decisions?
They are following 3 key steps to ensure that they are basing decisions on the best possible data.
Get Smart – Three Key Steps to Better Data
STEP 1 - Integrate and consolidate data from multiple sources around the organization
- Enterprise applications, spreadsheets, cloud-based applications and more
- Structured, unstructured and multi-structured data
STEP 2 - Implement multi-dimensional data cleansing
- Normalizing different spellings of the same name
- Address cleansing & verification
STEP 3 - Enriching corporate data from external sources
- Risk & financial data - credit ratings, fraud data, natural disaster
- Marketing data - demographic
- Free data - news and social media
Integration, cleansing and enrichment are the key foundations for a sustainable data quality initiative. Organizations that get their data right get a far higher percentage of their business decisions right – because smarter decisions are founded upon better data.
You could try and accomplish this with traditional enterprise BI tools – but only if you have very deep pockets and a close to unlimited amount of patience, because it will surely take an unaffordably long amount of time.
You could imagine that you might accomplish this with self-service BI - but data quality is the unspoken missing element in these solutions – a matter of “garbage in - garbage out”. Or you could try a different approach: data management and analytics delivered via a single, cloud-based platform.
It’s the approach we recommend – and one that a growing list of FTSE 100 and Fortune 500 companies are adopting. Perhaps there are lessons we can draw from such companies?
What are your thoughts and experiences? We'd love to hear your comments.
You may find our Ebook - The High Cost of Bad Data - worth a read.