Does your organization attract talented people ?
Does your organization put its people’s talents to work ?
Do talented people get more talented when they work in your organization ?
Do all the people in your organization get to exercise their talents when they work ?
Is your organization glad to see its talented people move on to other organizations that will value and reward them ?
Does working with your organization bring out the talents of the people in other organizations ?
Does your organization support its talented people when they chose to go back to the “beginner” stage and acquire new talents?
The need to improve your organization’s data culture is driven by the powerful new tools for data that are becoming available and the new strategies that they make possible.
In the frenzy over powerful new tools it’s good not to forget the most powerful “intelligence” tool that your organization possesses, its “Human Intelligence”. An organization’s Human Intelligence is determined by its skills, training and ability to adapt to changing circumstances. Such an ability is grounded on its member’s enthusiasm, trust and willingness to trust.
Tools cannot be used effectively unless they are grounded in the organization’s human intelligence. Sometimes, without the proper training, experience and enthusiasm to come to grips with a tool the tool can actually do harm to the organization.
Each time you introduce a new tool you should take the opportunity to review, rebuild and strengthen your organization’s human resources so that you can take full advantage of the tool’s capacities.
The most interesting result of Sodexo’s widespread adoption of QuickBase is not just the productivity boost but the increase in innovation, Bryant says.
“QuickBase is a low barrier to entry to try new things,” she said. “Experiments are not always successful so people can be reluctant to try if they know a failure will be costly.” —QuickBase Pushes Low-Code into the Enterprise
With a strong data culture the organization can trust employees to try new things, and when they work, to take full advantage of them. When they don’t work the employee is not blamed or punished, but encouraged to try again.
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.
The roots of the equation can be traced back to David Gleicher, who Richard Beckhard and Reuben Harris reference in their breakthrough research on organizational change.57 Their research was later simplified by Kathleen Dannemiller and Robert Jacobs to this version:58 C = (D x V x F) > R Change (C) will occur when sufficient Dissatisfaction (D) with the current system exists, when there is clear a Vision (V) of what is possible, when there are First (F) steps towards that vision, and the product of these three factors is greater than the Resistance (R) to change. If any of the three factors are missing, then the resistance to change will never be overcome.–“Data Driven Nonprofits” by Steve MacLaughlin
This formula points out why people who are interested in change are often so frustrated. They are often driven by high Dissatisfaction but blocked because either there is no plausible Vision of what is possible or no First Steps for people to latch on to.
The most difficult block is often Resistance. When the organization has struggled with a problem repeatedly people may be convinced that there is nothing effective can be done, or that any new attempt to deal with the problem will fail like all the others.