"Big Data does not convert data into actionable information. Big Data does not create value. But Data Science does, and it does not have to be complex or expensive, or even big."
Michael O'Connell Chief Analytics Officer at TIBCO
Practical, to the point with actionable list of the key tasks to extract value from relevant data whether IoT or not.
Also emphasises the need to focus on the right data rather than "big data". This often means accessing data not usually analysed such as emails, free-form text fields. My article "Increase ROI- access more of your data" describes how.
Michael quotes Qiao Li, Senior Market Analyst, Big Data and Analytics (BDA) at IDC Asia Pacific
“One in three organisations in the region find it difficult to build business case or measure ROI while leveraging BDA solutions, and we see organisations are becoming more pragmatic in justifying business cases and starting small in their BDA journey.
This reflects another of my articles "Damning analysis of Analytics" reflecting the lack of financial return from the majority of Big Data Analytics projects. But, like Michael offers advice to reverse this disappointing trend.
Data does not replace insight as I discuss in the linked article. Data is a means to and end, and combining the skill, intuition and market knowledge of data savvy business & operational users with the right data in an iterative common sense approach is more likely to lead to success than jumping in with two feet to IoT/BDA " Smart" projects.
Another red flag! Despite individual success with AI and Machine lEarning Projects it is more probable that you will gain enterprise-wide competitive advantage, customer satisfaction and superior financial return with a combination of analytics as described in this series of linked articles than by adopting AI.
Read how at "Augmented Intelligence" rather than AI, i.e. combining human intuition with machine learning. But not before you are proficient following the processes Michael O'Connell describes so well.
Data Science is a three-legged stool that combines business acumen, data wrangling and analytics to create extreme value. Focusing on the hard science skills such as statistical methods is a common mistake when actually, developing the knowledge about a particular business and wrangling the relevant data are often the most important skills to bring to the table. It’s one thing to know how to play with numbers, but it’s more important to understand what insights these numbers reveal on the business, and what actions to take based on these insights. Experience and business knowledge plays a role, as well as curiosity and passion. Sometimes the best results come from unlikely people just because of their desire and persistence.
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