Earlier in the week we covered a story by CMSWire where they outlined the topics that would be discussed at Gartner’s Business Intelligence & Analytics Summit conference, which begins today. The main theme of the column was that there may be problems ahead for the analytics market considering ethical concerns that are now being raised as a result of companies stretching the boundaries in data collection.
In conjunction with that coverage, I also wanted to take some time to outline common sense guidelines for businesses in order to help them avoid the data ethics cliff, as Gartner so eloquently refers to it as. The research and technology giant has developed this set of best practices in order to assist organizations in staying on the straight and narrow when it comes to the data they collect:
1. Companies should look to anticipate problems before they occur by setting all their business goals out before launching data analytics initiatives. This can help users gain an understanding of what could go wrong, allowing them to be prepared no matter what.
2. Big data sets do not necessarily require big analytics solutions. Complicated questions, not big data sets, require complex algorithms.
3. Contrary to what big data vendors will have you believe, not all problems require analytics tools. Other solutions exist, including statistical modeling, data mining, and machine learning.
4. Make sure that your company has the resources to properly use the analytics solution that it purchases. The more complex the solution, the more resources and expertise it will require on the back end.
5. Simply having an analytics solution alone does not guarantee anything. Transformational results are delivered via the tool to be sure, but so much more goes into that than meets the eye. Having analytics success involves looking at the right data, having skilled users to read the insights, and more. Analytics may be better served to produce small but frequent updates to existing business processes.
Check out these additional resources:
- Red Hat Drops New OpenShift Data Science Cloud Offering - December 3, 2021
- Starburst Unveils New Version of Galaxy for Cross-Cloud Data Access - December 3, 2021
- Yellowfin 9.7 Touts New Guided NLQ Natural Language Query Tools - December 3, 2021