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@chrispehura
November 9th, the results from the 2016 US Presidential Election were in. Trump won. Communism won. Racism won. Sexism won. Hate won. That was the narrative we were force fed by the mainstream media, Facebook, and Twitter. In an age of getting pretty much any information we want with a quick google, how can such unsubstantiated claims even be made? Then clicking through my phone I saw a tweet about Big Data failing America. Say what?
There is a new member of your C-SUITE, the chief data officer, also known as the CDO. Your CDO is a corporate data officer that focuses on making data your new capital, data your new assets, using both to build your data-driven corporation. With your CDO at the helm your data-driven corporation creates the business value needed to multiply your corporation’s revenue growth. To do this your CDO needs to do things in a certain way.
Big Data is the new buzz word, the new paradigm of data-driven approaches for sharpening our business's competitive edge. Because of Big Data's newness, there are misconceptions, misunderstandings and misinformation about Big Data. Here are the top 5 misconceptions that push our Big Data initiatives off track.
Sociology is the study of the development, structure, and functioning of our human society. Today our society contains more than us humans. Our society also contains machines; machines made up of data, algorithms, and artificial intelligence (A.I.); machines that develop us, structure us, and support us. They fuel our human intelligence. They fuel our culture. They fuel our prosperity. With things such as self-driving cars, mobile notifications, geo-location, and tweets; the machine is in charge. But the machine is not the overlord depicted in science fiction. The machine is our companion, our partner that supports our success and supports the functioning of our society.
But the support the machine gives us is rudimentary at best. In some aspects, our society has advanced so far with the machine, but in other areas we are years if not decades behind. To take full advantage of the machine we need to catch up in other aspects of our society.
Not too long ago from the ivory tower to the front lines it was straight forward to do analysis. We all came from the same place, the same schools, and we used the same language. We were the same k…
How Do You Accelerate Your Analysis?
Not too long ago from the ivory tower to the front lines it was straight forward to do analysis. We all came from the same place, the same schools, and we used the same language. We were the same kind of people meeting at the same table. It’s very different today. Today, we are global teams, diverse communities doing group analysis for the global market. We are now so very different, coming from different schools of thought, using different bodies of knowledge, and different business language. Today we are facing new challenges with our analysis; challenges most of us are unprepared for. We need to rise above what we do now, and improve how we analyze as a group.
All effective analysis methodologies follow the same pattern, may they be in engineering, law, or the humanities. By recognizing and following this pattern we can sharpen our competitive edge, fast-track our analysis and mold our culture to transition to data-driven organizations; organizations where Big Data, data science, and artificial intelligence are the norm.
Are you ready to consider SHELL?
There are many different definitions for Big Data. Definitions range from business outcomes, to the 3 V’s, to the Hadoop Ecosystem. Though there are many different definitions, the optimal way on how we scope and build our Big Data solutions really doesn’t change. To build the right Big Data we must build our right org structure and build our right foundation for strategic change, and build them in the right order.
Data strategy is often too narrow not taking into consideration our data capital, culture, or politics. This makes our data strategy rigid, difficult to align, and hard to manage; all leading to li…
Data strategy is often too narrow not taking into consideration our data capital, culture, or politics. This makes our data strategy rigid, difficult to align, and hard to manage; all leading to limited, if any successful outcomes. The key to our data strategy is our people and how our people invest in people, infrastructure, and the right things. So it’s no surprise that our data strategy is effective, not from the methodologies or designers in our conversation, but from our people involved in our data strategy conversation.
To increase our data strategy’s effectiveness we must bring in the right people and widen our conversation the right way so our data strategy is more diverse, more politically sound, and more culturally accepted. We must cautiously structure our conversation and keep our conversation on track; scaffolding and aligning our data strategy conversation with our other strategic narratives. We must stop asking about our data’s business value and start asking what data we need for our strategies, our operations, our growth, and our business development.
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The best stories out there are the stories about the underdogs. It makes for a good movie even a great video game. But in our non-fiction world, a world where dog eats dog, underdogs get eaten. Bob…
Leadership: Do Underdogs Really Win? | C-SUITE DATA
The best stories out there are the stories about the underdogs. It makes for a good movie even a great video game. But in our non-fiction world, a world where dog eats dog, underdogs get eaten.
“Look at our executive team. Do you see our A-team? Do you see their exhaustion from balancing the day-to-day with the ten year vision? Do you see them as political warriors with battle scars, scar…
“Look at our executive team. Do you see our A-team? Do you see their exhaustion from balancing the day-to-day with the ten year vision? Do you see them as political warriors with battle scars, scars that could tear the whole team apart?”
Executives set the goals and expectations for our organization for the next decade. They get us to the finish line by managing our corporate capital and assets. Executives are the living and breathing personifications of our organization’s groupthink, politics, cultures, and organizational structures. The CDO, the Chief Data Officer, though an executive is a little different. Besides being an executive, the CDO is also the political personification of our data, our Big Data, and how to best leverage our data both operationally and strategically. The CDO supports our executive team by strategically managing our data, our data assets, and our data capital to drive, multiply, and maximize our organization’s revenue growth.
Single best infographic describing the role of a Data Scientist. #DataScience #BigData https://t.co/93J8JJmAdN Check out @cdoyle43's Tweet:
I have not failed. I've just found 10,000 ways that won't work. ~Thomas A. Edison #leadership Check out @LeadershipCures's Tweet:
6 (Mega)Trends for Deriving Big Value from #BigData: https://t.co/zr91RtmNZg #MachineLearning #DataScience #DataOps https://t.co/pN4WIMkpDh Check out @KirkDBorne's Tweet:
Big Data And Business Intelligence: https://t.co/QwC38gkzqG #BigData #BusinessIntelligence https://t.co/jkDSvmOqEd Check out @kesbutters's Tweet:
Check out @v_vashishta's Tweet:
DATA SCIENCE: THE NEW MONETIZATION MODEL FOR ANALYTICS INDUSTRY | #DataScience #Analytics #… https://t.co/AmdHnJwYbn https://t.co/ZSul7lHdfJ Check out @Ronald_vanLoon's Tweet:
Cogs for Big Data #BigData has lots of moving parts, and this #infographic is not even current! https://t.co/6qYAyq1I2R #DataScience https://t.co/t5pyNmCQb0 Check out @KirkDBorne's Tweet:
Lakes vs Streams: Which is a better survivor? Don't we need both? Data lakes vs data streams: Which is better? via @ITProPortal https://t.co/CN4qHqIG59 HT: @tamaradull #DataLake #DataScience Check out @IBMIIG's Tweet: