This week’s roundup looks at five advanced analytics algorithms that can transform your business, why a ‘data sciences team’ may be a better approach, how to harmonize your marketing technology stack, and the real cost of big data for your business.
by Troy Hiltbrand, Chief Digital Officer at Kyäni, featured on TDWI
Advanced analytics often starts with a single use case. This includes the application of new methods of data transformation and analysis to uncover previously unknown trends and patterns within their data. When this new information is then applied to business processes and operating norms, it has the potential to transform your business. To extract greater value from your data, put these five categories of algorithms to work.
by Tapan Rayaguru, COO of Tredence, Inc., featured on YourStory
Finding all the essential skills for an effective data scientist in one person is like looking for a unicorn! We experimented on an alternate approach that focussed on the creation of a ‘data sciences team’, instead of trying to create a ‘team of data scientists’. This team had the right mix of people, as well as the right culture in the team to deliver what is required by the organization.
by KN Kasibhatla, SVP, Marketing Technology Services at Harte Hanks, featured on Forbes
How do you begin to solve the problem of disparate marketing technologies? One solution is to make all marketing stack components serve one, central brain. Can we bypass the brain in each marketing system and use the central brain to trigger tactical actions by each legacy system? In effect, each legacy system is converted into a dumb terminal. Tactical functionalities remain intact, but strategic functionalities are performed by the central brain.
by Andrea Steffes-Tuttle, Director of Marketing at Lityx
They don’t call it “big data” for nothing. Companies today are struggling to store, manage, and most importantly, use the massive amounts of customer data they collect. Even worse, bad data is holding them back from tremendous business opportunities. Poor data quality costs the U.S. economy around $3.1 trillion per year, according to IBM. This unreliable data forces everyone in your organization—from decision makers to managers, knowledge workers to data scientists—to double-check its accuracy. Or worse, not trust the data at all. And that’s bad for business.
Did you see an interesting article in the last week? Share it with us! Send it to astuttle [at] lityx.com.