Major league baseball takes data-based decision making seriously, and so should your organization.
As an IT industry analyst, many research reports cross my desk. However, few are more striking than the research published in NewVantage Partners Big Data Executive Survey 2017.
The NewVantage survey revealed that:
"Ninety-five percent of the Fortune 1000 business leaders surveyed said that their firms had undertaken a big data project in the last five years. However, less than half (48.4%) said that their big data initiatives had achieved measurable results."
This followed an October 2016 report from Gartner which concluded that: "Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 percent)."
SEE: Quick glossary: Business intelligence and analytics (Tech Pro Research)
Based on these findings, a CIO in an organization would recognize that there is as much organizational education and awareness to be built around the value and the potential of an analytics payoff as there are technical challenges in implementing new big data projects. Further, analytics would be a failing effort if projects were unable to advance beyond pilot test phases.
Play ball with analytics
So, how do you get senior management, the board, middle managers, and employees in your organizations firmly engaged with analytics?
With the major league baseball playoffs in full swing, it's timely to use MLB as an example.
Professional baseball is a closely knit network of managers, scouts, player, and former players who move fluidly from team-to-team and frequently pass opportunities to family members and relatives. It's not always easy for outsiders to break in to this community.
SEE: Quick glossary: Big data (Tech Pro Research)
However, analytics advancements changed this culture several years ago. Suddenly, teams saw that analytics could assess players from numerous different perspectives, and it enabled general managers to make better decisions about the types of players they either wanted to retain or bring into their organizations.
Teams with the vision to adopt analytics early such as the Los Angeles Dodgers and the New York Yankees invested heavily in their front office research budgets, and this enabled them to assemble winning teams.
At the same time, there was widespread recognition in baseball that analytics wasn't everything. Teams still needed skilled and experienced managers who learned the game and its nuances on the field and from a player and game perspective. These are intangibles that analytics can't provide.
4 lessons learned
What were the lessons learned from this?
1. You can't advance analytics in an organization unless you have the talent to produce the analytics.
This requires an upfront investment in either bringing in new talent, or re-training existing employees who have the potential to become data scientists and analysts.
2. You have to demonstrate a rapid analytics payoff and render your analytics usable.
Whether you make analytics consumable by creating easy-to-navigate reports or tell-tale dashboards, the key influencers in your organization (e.g., middle managers, C-level management) must use the products from analytics in the business--and see the results.
3. Never substitute analytics for experience.
One of the major resistance points in organizations failing to adopt analytics comes from a fear of abandoning experiential knowledge for what a computer tells you. Every analytics effort should include a plan on how to blend the best of both worlds for the maximum business benefit. In other words, where do the analytics help decision making and at what point do humans with deep experiential know-how make the calls?
4. Upper management must walk the talk.
Too many corporate executives boast that their companies have big data and analytics, but they themselves don't use the tools--and they don't really see the business benefit. This is dangerous ground for CIOs to tread. Every big data and analytics project should have a usable product for the C-level. The ultimate metric for considering a big data project "well done" is when you see the individual in the corner office using the product.
- How big data won the 2017 World Series (TechRepublic)
- Field of digital dreams: Why MLB is betting its future on big data, Wi-Fi, apps, and AR (TechRepublic)
- Analytics in 2018: AI, IoT and multi-cloud, or bust (ZDNet)
- Data analytics to save our most valuable resource: Water (ZDNet)
- How powerful data analytics can be with the right tools (TechRepublic)
- Decision factors: Do you need real-time analytics? (TechRepublic)