But Salesforce CEO Marc Benioff says that AI is … really the next big thing after mobile and social changed the tech world over the past five years.
Smart computers that can think, talk, reason, and predict will be able to do more than just search Google for us or order a pizza. They will eventually do stuff we haven’t even imagined yet.
Benioff, talking to analysts during Salesforce’s quarterly conference call, called this the “AI-first world.” –“Salesforce CEO Marc Benioff just made a bold prediction about the future of tech” in Business Insider
Salesforce is planning to add an AI component (called “Einstein”) to all of its platforms. Find out more here. Making use of such advanced data handling will require a data culture that is strong and robust and than can support its members in gaining new skills and insights.
We are going to see cars that “drive themselves” much sooner than anyone would have imagined. We will also see organizations that can “drive themselves” and make use of their ability to understand their environments and use data to drive their processes.
You may not be planning to use AI in your organization but you should be planning to strengthen your organization’s data culture to the point where you will be able to use it when you need it.
Start by establishing a basic training program and working to get everyone on board.
The tClara team created the Trip FrictionTM benchmarking application. This app shows HR managers which of their employees are at risk of burnout based on their individual travel data. The firm provided employee travel data such as how many weekends they traveled per year, how many overseas trips they took, and how many weeks they spent away from home.–from “Putting People First in Your Big Data Initiative” by Juice Analytics.
This is a great example of using data to do something about events that have not happened yet. In this case events, employee burn-outs, that would be very costly for the company.
In this case the HR department’s ability to predict which employees might be candidates for help depends on using an outside datasource and combining it with their own data.
Think like Google: Produce a simple concept or idea as a beta, release it, and let users test it and give feedback. Then study the user responses and adapt the capabilities as you go. This kind of logic allows for a quick product release, less development dollars up front, and the opportunity to design the product based on consumer feedback. These initiatives yield successful results. It’s better to prototype a data product that is ready to put in front of a user in six weeks instead of six months. This allows you to keep it simple and make adjustments quickly based on what’s working and what’s not —“Putting People First in your Big Data Initiative” by Juice Analytics
This is the data-centered approach to change. Make your changes in small steps. Project what results you expect from each step. Test before and after the steps to check on your progress. If a step does not produce measurable results, modify your plan.
“Consumer feedback” is just one way of measuring the results of your changes. Most steps you take should also make some other impacts that you can measure. Making a data entry form simpler and easier to use should result in good consumer feedback, but it should also have other effects: fewer incomplete forms, fewer errors in filling out the form…etc. Often trying to figure out how you will measure the effects of the changes you are planning will give you new insights into just what you want to do, and how you should try to do it.
“Culture eats strategy for breakfast.” –Peter Drucker
Plans have to be executed. The minute an organization starts to execute a plan things will start to go wrong (or go right when the plan things they should go wrong).
The organization’s ability to adapt to the difficulties that happen when the plan is executed is central to its ability to execute any plan. An organization’s culture is what gives it the ability to overcome such unexpected events.
Think tortoise and the hare. Given the choice between having an organization with truly brilliant strategies, but with such a poor culture that it is unable to execute them and an organization with average (or even poor) strategies, but with the ability to execute them, most people would go for the later.
To make matters worse (or better, depending on how you look at it) part of a strong culture is being able to learn from its mistakes. One characteristic of a weak culture is that its ability to learn is also weak, so it keeps making the same mistakes over and over again.
To start thinking about your organization’s culture (in particular its data culture) you can take the Basic Diagnostic. Seven simple questions will give you some perspective on your organization’s attitude towards its data.
some data initiatives will
succeed, while others will fail.
One of the most important reasons for having a data culture that gives you access to Real Time Data
is that you can detect failing initiatives sooner and recover from them faster.
It would be entirely unrealistic to think that you can plan well enough to avoid committing yourself to any initiative that might fail. A good “early warning system” keeps your planning from being paralyzed by fear of failure.
Lead by Example
After you’ve prompted interest in analytics, continue growing the conversation by sharing anecdotes and case studies with the company. If people see the result of a data-driven decision, they are more likely to trust in the potential of evidence-based decision making themselves.
“We started our conversations in a quarterly meeting where people shared success stories about where they had utilized data,” Schmidt says. “Getting people talking can help get the ball rolling and take you to a point where you can build and improve.”
–How to create a data driven workplace, by Cornerstone
You can find examples in your rear view mirror, or you can see them coming down the road in front of you. Whenever your organization is discussing or planning a change take the time to explore how data culture impacts the problem you are trying to deal with. Consider whether the change can be successful without also changing the data culture to support it. Consider the changes in your data culture that will happen because of the change. Consider the range of opportunities that would be available to you if you had a stronger data culture. Consider what pathways towards solving the problem are blocked by your current data culture.