What are the barriers to data science teams success in larger organizations? Why have so many data science projects missed their mark? What should you consider as you make a data science platform decision? This is where I see IBM’s Data Science Experience as the right choice for many of my clients. I will share more of what I learned at Think 2018 in Las Vegas today.
Scaling Data Science capabilities is something that all data science leaders must consider. Data Science adoption is a hard trend, it is going to happen. Now how do you pick the best solution for your team today that continues to grow tomorrow? You can read the first part of this blog at Developing High Performing Teams at What Should You Want from Your Data Science Experience?
With so many great legacy databases available that are just beginning to be explored, throw in some unexplored dark data, and for, many organizations the possibilities are endless. Now, I wonder, is that a good thing or bad thing? All I know for sure, is if you’re not exploring your data, you are missing incredible opportunities for growth.
I’ve found Data Science fanatics using different tools tend to bring different capabilities to the discussion. Mentoring and cross training provide for better teamwork and, typically, more engaged team members. My younger top gun data scientists tend to lean towards open system tools and architecture. They can’t understand why I believe in buying tools from a big dumb company. Machine Learning open platforms are transforming how we work with data. They see the possibilities.
I then ask to see their phone and they get my point. How many of those have they bought in a row? Most technologists love to learn more but hate changing the platforms and tools they use. How many days did you wait to get your first one versus today? Almost all of them are household names. The power of disruptive incumbents is just starting to be felt in our data science technology markets.
The third capability I want is a partner who is looking at how these many moving parts come together to create an extraordinary data science experience. This means I want it all, I want it now, and I want it so my CEO has an easy time when making a faster, better decision. To me, this means that the solution I choose must have a strong community supporting it. I never want to be more than several calls or texts away from an expert. As CIO, does that sound familiar to you?
Not just any community, but one that is committed to keeping state of the art practices improving as needs in the community evolve and change. This a bit tricky. How do you get the best people to work on your products and services for free to make it a better experience for everyone involved? Open Source Data Science tools to have done just that.
Data Science can offer us many new and different solutions and opportunities. I believe data science has the capability to transform how we do business. I also believe that machine learning will change how we look at predictive and cognitive capabilities. It’s a whole new ball game with rules yet to be written. I want my technology partners to be leading the way!
What’s the foundation of data science today? How will it change as it evolves? We need to consider the ethics and values we choose to use when dealing with data science. Data Science is finally reaching critical mass where capabilities and imagination are unlimited by any standard measure you might choose.
Data Science Experience is not as time bound as many other more traditional analytics technologies. The rise of almost unlimited on demand computing power is going to change the game in many industries and markets. Global data science teams are already being leveraged by many larger organizations. Incorporate Quantum Computing into the equation and we go from 3D to 5D data almost overnight. That’s a whole other discussion that I will be sharing in a future blog! Data Science will usher in an era of hyper competition or new global collaboration opportunities, limited only by the imagination of the many different stakeholders and their ecosystems.
What about data security and data democratization.? What role does it have as we allow people within our organizations’ access to large amounts of personal data and corporate intelligence? What is an advertiser buying when they buy data and for how long? These are some of the questions you will face as you take your data science team to the next level. You will need to be prepared to have these discussions with many of your organization’s key stakeholders.
We’ve seen recently in the events with Facebook, we are just beginning to see many new discussions on the horizon. I believe that strong governance is critical to the future of data science. Your ability to enable a ready, fire, aim strategy is critical to your organization’s long-term success. I’ll return to this topic next week when we talk about how strategy is changing in a data drive world.
GDPR is the beginning of a much larger regulatory discussion! Global government regulation is another hard trend. Will your data science platform be able to support increasing new government regulations? You’re going to want a partner who is keeping you up to speed! I believe IBM is one of the few partners who can do this effectively.
We’ve covered several keys to the choosing your Data Science Experience. I hope I’ve given you several reasons why I recommend IBM Data Science Experience to my clients and partners. Please let me know if you have any questions or would like to learn more about this great technology. See you next week when I return to safer waters.
See you next week.