Obstacles to Applying Insight from Predictive Analytics
The ever growing volume of data that organizations have at their disposal presents a series of daunting challenges.
- Determining which data to collect, and having a sound rationale for doing so
- Establishing methodologies for efficiently accessing, classifying, assessing and prioritizing data
- Rapidly deploying analytical models for business users to incorporate into routine business operations
- De-mystifying and simplifying analytics for business users
- Deriving business insight from the data
- Effectively monetizing business insight
- Institutionalizing an analytics culture and associated behaviors among business users
- Managing the ongoing storage and computing requirements associated with the ever growing volume of data
In most organizations, a relatively small cadre of data science professionals is responsible for big data analytics. This team typically manages a gargantuan set of related tasks:
- Developing analytical models
- Testing model accuracy
- Deploying models effectively into the business
- Collaborating with the IT organization to facilitate model deployment
- Coaching business users on using analytical tools
- Maintaining analytical models so that they retain their accuracy
These activities comprise a never-ending cycle that can exhaust even the most coffee-dependent, fanatical data scientist. Hiring more data scientists can help alleviate some of the pressure, but these skills are scarce in the marketplace. Beyond that, there are still only 24 hours in a day.
How can an organization overcome these obstacles?
Our data science expertise applied to your business
Zementis not only builds powerful predictive analytics software solutions, we also help organizations develop and implement data strategies, put the necessary technical tools in place and optimize the data analytics environment to help you drive tangible results.
For more details or if you have any questions, please feel free to contact us!