The Importance of Failure

some data initiatives will
succeed, while others will fail.
–https://www.cognizant.com/InsightsWhitepapers/how-to-create-a-data-culture-codex1408.pdf
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.

Six Steps to Improve Your Data Culture (from Cognizant)

The following steps will help guide CDOs to create
successful, thriving data cultures:
1. Map your organization’s data supply chain.
2. Focus on the “art of the possible.”
3. Be transparent about data.
4. Develop reward-sharing mechanisms.
5. Identify areas of friction within the organization.
6. Elevate the conversation to focus on strategy
and innovation.
–https://www.cognizant.com/InsightsWhitepapers/how-to-create-a-data-culture-codex1408.pdf
The Data Flow Map site offers a simple tool to map how data flows through your Organization.
Data driven organization’s are capable of planning and monitoring incremental changes to get where they want to go. They do not have to attempt risky huge leaps into the dark.
Data transparency is both one of the results of improving your organization’s data culture and one of the strongest tools at your disposal for moving your organization in that direction.
Improving your organization’s data culture should make everyone feel that their work is more worth while and their job is more meaningful. For most people, that, in itself, is a large reward.
A good data map will probably not only show where there might be areas of friction, it will probably show huge holes in you data operations. A good data map will also allow you to plan to start to map improvements. You can change the conversation from some variation of “the sky is falling” to a discussion about what realistically can be done to improve everyone’s situation.

Real Time Data

Making sure the organization has access to full-system visibility and real-time data is one of the first steps to creating a proactive company culture. Embedded analytics enable personnel—from … executives to … managers, to continually monitor issues, predict customer needs and make well informed decisions, critical for strategic problem-solving and proactive response to pending market changes.–http://www.industryweek.com/systems-integration/improving-company-culture-power-analytics

Many companies use their data as a record of things they have done in the past.

The big shift everyone is trying to make is to use data to project forward into the future. Reports that arrive at the end of the quarter or the end of the year don’t provide a platform for such a shift.

To analyze your data in “real time” means that your data is collected in “real time”. Data that is entered into spreadsheets, or kept on paper notes, and entered some time later by a data entry person imposes a lag in your data analytics that really works against trying to convince people that your data is a future resource and not just the basis for reports about the past.

Data Culture Tricks: Offer Learning Opportunities

Offer Learning Opportunities

The real key to instilling a culture around data, however, is to create opportunities for people to engage with analytics themselves. While conversation and success stories generate intrigue, the next step is dedicating resources to transform intrigue into action.

–How to create a data driven workplace, by Cornerstone

Every time that your organization handles data, either its own data or the data of its clients, is a wonderful opportunity to also push the envelope on growing its skills and improving its tools.

By looking at each project from the four corners of the data square the organization can offer its own people the opportunity to grow into the “unknowns” and offer its clients new insights and opportunities.

Data Culture Tricks: Lead by Example

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.

Data Culture Tricks: Start Small

Start Small

You have to crawl before you walk (or run) with analytics. When you’re just beginning a data program, focus on a single metric or goal that can prompt a larger conversation about the impact of analytics, Schmidt advises.

“If you can come up with even one metric that people can understand, talk about and utilize, that will have a huge impact,” she says. “As people begin to see value in using data, you can continuously improve your metrics, gain more access to data, and get people interested in collecting new data.”

–How to create a data driven workplace, by Cornerstone

6 Steps to improve your data culture from Canopy Labs

  1. Ensure dashboard metrics are tied to what is valuable to your business. It is very easy to be mired in “vanity metrics”, tracking numbers around information that ultimately has little impact on the success or failure of a business. Ensure that the metrics you are tracking are ones that are directly tied to the success of your team, division, or business. Otherwise, your dashboards might be improving even as your business starts to fail.
  2. Organize daily “standup” meetings to review dashboards. Early on during the data-driven change process, it is important to encourage your teams to check dashboards and reflect on what they mean. It is best to schedule daily (or weekly, if dashboards are not updated so often) standup meetings so the entire team can review progress around specific metrics and plan around them. Encourage team members to provide feedback on dashboards and suggest ways to improve key performance indicators.
  3. Be a role model on the use of data. The only way to encourage your team to use data and dashboards to make decisions is to start doing so yourself. Ensure that your own decisions are grounded in data, and that you refer to the numbers when promoting those decisions.
  4. Define goals in terms of what is being measured. Your goals should reflect what is being measured in the dashboards and reports, and vice versa. By holding everyone accountable for specific progress in the metrics being measured, your team members will actively track those metrics and respond to their changes. Defining goals that are not related to the dashboards themselves will mean those dashboards get ignored, as they will not have a significant impact on employees’ personal success.
  5. Celebrate when thresholds are passed. Encourage behavior change is through positive reinforcement. If you’re defining your goals in terms of metrics milestones or passing certain thresholds, make sure you celebrate those successes. Doing so publicly will encourage other team members to track their own metrics more closely, too.
  6. Promote a “continuous improvement” mindset. Once you have set goals around dashboards and are celebrating the success of those goals, begin to adopt a continuous improvement mindset: encourage your standup meetings to be ones where ideas around improving metrics are discussed, and incentivize all team members to come up with new ideas to improve metrics. Strategies that actually work can be rewarded with recognition among the team or company, or even through a monetary bonus. –https://canopylabs.com/blog/importance-of-data-culture-corporate-strategy

These are all good steps, and something like them can probably be found in many recommendations.

One thing to notice is that they do not talk about how to use data to monitor whether or not your data culture is improving. This is the role of the Data Fluency Inventory: if data and data culture is important to your organization, then don’t just encourage it, measure it.

How do you stay on track ?

Establishing a data cult

ure and improving data quality is not a one-time project. It is an ongoing discipline that, when executed correctly, delivers breakthrough results and competitive advantage. –http://betanews.com/2014/12/04/enabling-a-data-culture-through-continuous-improvement/

One of the problems with all ongoing efforts is how do you know how things are going.

The Data Fluency Inventory offers a simple way for a group to measure their progress and plan their efforts.

The four sections of the survey allow you to focus your efforts on one quadrant at a time rather than trying to improve everything at once.

Having an up to date Data Flow Map gives you  great tool to use along with the survey when you’re trying to discuss your data issues or plan for changes.

If you have your organization take the survey at intervals the survey scores give you an objective, external benchmark that you can use to discuss what is working and what you want to try next.

 

Data Maps

The primary challenge is to first understand and map the current data landscape, but retain the flexibility to easily adapt and update it as the business and the underlying landscape continue to change over time. The most effective means of doing so is through data models to describe the data (and metadata) as well as process models to describe business processes that create, consume and change the data. This allows data to be understood in context, and is the basis to identify redundancy and inconsistency. All manifestations of each critical business data object must be identified and cataloged. Typically the most critical business data objects are also master data, as they are utilized in most transactions (for example: customer, product, location, employee, etc.). Without context, it is extremely difficult to ensure that the proper data is being utilized for reporting and analytical purposes, and hence, decision making. In order to complete the understanding, the models must be supported by integrated business glossaries and terms that are owned by the business stakeholders responsible for each area. It is imperative that the business team is able to utilize tools that allow them to collaborate amongst themselves, as well as with technical staff that are assisting them. –http://betanews.com/2014/12/04/enabling-a-data-culture-through-continuous-improvement/

Making a Data Flow Map

The data flow map will depict sensitive information in all of its forms, origins, paths, exit points and storage locations. The map should show where sensitive information is processed, where it transits the organization’s network and where it is stored….While making a data flow diagram can be daunting, breaking the job up into smaller tasks makes it simpler to implement and maintain. Use simple tables to show where sensitive information flows. Then use these tables to make your data flow diagram overlays. The end result will be something your organization can be proud of and will help it better protect sensitive information.

–http://searchsecurity.techtarget.com/feature/How-to-keep-track-of-sensitive-data-with-a-data-flow-map