A single, cohesive data management strategy to build a foundation for better decision making has never been more important
By Mike Luke, Data Management Practice Lead, SAS Canada
Data management has never been very sexy, but that may soon change as 2015 promises to be the year it becomes a hot topic. Take a look inside any successful organization and you’ll likely discover data management technology powering every process. It’s that critical. Data management can often mean the difference between a business that will succeed and one that will drown in too much disorganized information.
With high-quality, well-managed data, organizations can gain a clearer picture of their business, access the right data when they need it, and make better, more strategic decisions. However, far too many companies are failing to deliver and the data scientist skills shortage just may shine the necessary light on common data management shortfalls.
The data scientist skills shortage has been a popular topic of discussion, and many among us know there’s a need for more qualified data experts in most organizations. As enterprises increasingly recognize the value of the vast volumes of data they collect, the demand for people who can unlock that value is rising sharply. McKinsey predicts that by 2018, there will be a shortage of as many as 190,000 data scientists in the U.S. alone. In fact, there’s even a Big Data Consortium in Canada tapping at the issue.
Data Scientists are a hot commodity as organizations realize that prospering from big data is moving beyond simply employing new technologies. Organizations are clamoring to build teams of experienced analytics users in order to stay competitive in a global data economy. No doubt that these highly sought after data wizards have clout, and they certainly add value; but many organizations are missing huge opportunities by not placing enough emphasis on the importance of data management optimization – the fuel that powers any good Data Scientist.
Data practitioners around the globe can attest to spending more time preparing the data to solve problems than actually solving the problem. Most organizations are failing to deliver on the unique data management needs of the statistician. A strategic data management infrastructure will streamline how the Data Scientist gets the data, getting it to the right place, at the right time, in the right for form and to the right people. This will ultimately allow data scientists to spend more time and energy solving business problems and less time preparing the data.
There is little doubt that effective data management remains a critical and under-optimized component to gaining the necessary insights to positively impact the bottom line. According to Gartner, up to 40% of all failed business initiatives are a result of poor data quality. TDWI estimates that poor quality customer data costs U.S. businesses alone over $600 billion a year. There is no time like the present to ask yourself if your organization’s data management practices are up to par. In the meantime, here are three tips that might help you put the sexy back into your data management practices:
- Reduce the data scientists’ time investment in non-producing activities involving data preparation
- Provide automated governance that enables the data scientists to show their work and create a data-proven — as well as data driven — culture
- Establish an infrastructure that supports data science and the unique processes that are required to support advanced analytics
To learn more about visit: http://www.sas.com/en_ca/software/data-management.html
As the National Practice Leader for Data Management at SAS Canada, Mike is assisting clients across Canada leverage one of their most important assets, their data. Mike has gained an extensive background in Information Technology working with Financial Institutions, Retailers and Telecommunication Providers across Canada.
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