Post Adapted from Top 10 Big Data and Analytics References
Price is what you pay, value is what you get
- Warren Buffett
Between rising Data Scientist salaries, rapidly evolving software and hardware needs, and mounting technical hurdles, enterprises are keenly aware of the price of data analytics.
While there is generally little doubt the potential value of data science, realizing that value can be more cumbersome than anticipated (as with most IT projects). That said, analytics professionals who firmly demonstrate the net gain of their projects, will find themselves with greater organizational support, higher degrees of trust, and more freedom to pursue the innate intellectual curiosity that drives so many data scientists.
As someone who sees beauty in simplicity, I often jot a note or two from reports, articles, and personal experiences. Most recently, I took moment to distill some of the common themes and advice about how data science and analytics initiatives can have the greatest IMPACT.
Even with an amazing internal team, occasionally an external recommendation or insight can be just what is needed to spur ideas, enhance cost-savings/ gain assumptions, and improve business buy-in. Consider starting with Data Analytics Explained for Business Leaders to demystify analytics and data science among managers and executives.
Let's take a moment to explore some of the references and sources that can help data science teams conceptualize, estimate and prioritize project opportunities. The Top 10 Big Data and Analytics References list was compiled based on specificity and robustness of recommendations (not simply buzzword reports), quality and breadth of analysis, frequency of updates, and recognition/ respect among business leaders.
1) McKinsey- McKinsey’s 2011 report arguably spurred the race for Big Data Analytics. I appreciate McKinsey’s specificity in projected cost savings, details and breakdowns of assumptions, and a willingness to make bold recommendations.
2) Boston Consulting Group- Industry specific recommendations provide high level perspective and assumptions are well explained. Case-studies embedded into BCG's reports help to understand the problem at an operational level.
3) Booz & Co/ Strategy&/ PWC- PWC’s acquisition and rebranding of Booz and Co to Strategy& may be somewhat confusing, but the insights and specific recommendations help to conceptualize opportunities. I appreciate Booz & Co/ Strategy&'s level of detail in industry-specific reports
4) Deloitte- Deloitte’s industry specific reports and recommendations provide context around a number of analytics initiatives at numerous managerial/ operational levels.
5) Accenture- Accenture’s recommendations span operational levels from executives to implementation leaders.
6) IBM- IBM’s expertise in analytics provides both thought leadership and interesting case studies.
7) Bain- Bain’s comparisons of major firms and their performance speaks volumes to the value of analytics and a motivating factor for executives.
8) Booz Allen Hamilton- Booz Allen Hamilton’s Field Guide to Data Science stands out in my mind as one of the best and concise resources to use to introduce business leaders to data science. Leaders in government recommendations and reports.
9) Investment Banks- Investment Banks are excellent resources for deep analysis into industry specific trends and performance. Although reports are often rather dense, the high level perspective and detailed review can be instrumental in designing high value analytics projects.
10) Notable Sources- I purposefully steered away from software, hardware and data providers. However, I realize that they can provide exceptional insights. Therefore, below are additional resources to keep an eye on (in no particular order).
Hopefully these resources will complement your understanding and intuition to identify new opportunities. Further, consider your industry and operational area to hone in on those thought leaders and resources that are most relevant.
Finally, I'd love to find additional quality references, where do you go for case studies, ideas, and recommendations for big data analytics projects?