Cloud-embedded data analytics mission critical for businesses
The cloud serves as a tool for easier data analytics, but only two in five organizations have employees that can successfully analyze data without support from their IT department, according to a Ventana Research report prepared for SAP. To help build more confidence in analytics, almost 75% of companies are turning to embedded analytics tools.
A shift to predictive analytics is providing companies with a more competitive edge, but 62% of organizations don't have the talent required to use such tools, according to the report. In order for analytics to showcase up-to-date data, system updates are required. Only one-fifth of organizations update their analytic models on a daily basis.
- Data analytic models have taken a turn from focussing solely on model creation to incorporating model creation as part of the data science "lifecycle," according to an Eckerson Group report co-sponsored by SAP. Data scientists work on the front-end to "select, profile, clean, format, merge, manipulate, segment and validate" the data that is inputted into the model. Then models are put into production by integrating "model management and deployment features" for output accuracy in the back-end.
Having analytics tools built into the cloud gives companies a wider scope of their data, applies meaning to data and allows more employees to develop their analytic skills. However, employees outside IT departments with few developed analytic skills can only derive as much insight as tools offer.
This is why integrated analytic tools prove to be most beneficial. The tools offer employees reprieve from dedicating time to preparing data before it can be analyzed. The analytics, in return, become stronger because it eliminates the time it takes for a "line-of-business" employee to search for the various data sources they require.
Having these tools help shape the context in which data can improve overall company functions, but machine learning is guiding the way to the "new" frontier of predictive tools, said Nic Smith, VP of product marketing for SAP Analytics Cloud, in an email to CIO Dive. ML works to scratch away any "hidden meaning" manual data scraping can leave unintentionally ignored.
Still, data analytics is not something companies can afford to put on the back burner. Companies need to have data scientists in house, from a third party or at least train line-of-business employees to become "citizen data scientists" in their own right, according to Smith.
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