What are the Characteristics of a Data-Driven Culture?
Decisions Based on Facts, Not Gut Feeling
A data-driven organization is one where the people make decisions based on research, and discussions are backed up with data vs. using gut instinct. Executives, for example, prefer to access their performance information daily via dashboards. They want to see trends toward goals so they can correct trending problems or do more of—and enhance--what's working.
Metered Goals are Valued
The organization places more focus on achieving metered goals. Metered goals establish performance baselines that regularly monitor trends against the goal—not just a single point in time. Trends are the signals that send out either assurances or warnings. So, you should have both a trend AND a goal.
Trends tell you one of three things:
1. you are flat-lined and resting in place 2. you are successfully moving towards the goal 3. if you continue with the same downward/upward trend, you won't meet your goal
If it's number 3 above, you need a deeper dive into the data and start working on a corrective action plan.
The Data Talks; the End-User Listens
The data "speaks to" the end user in a data-driven culture. "Speaks to" means the BI system finds things on its own using defined business rules and algorithms. Those "things" are based on what the user does on a daily basis.
The BI product automatically brings essential information--like KPI measurement or market trends--to the business user's attention. Then, the user can choose whether or not to act on the information.
Analytics Delivery will be Enhanced Continually
You know you're in a data-driven culture when what's being delivered will continuously be enhanced because:
Delighted users will ask for more enhancements and constantly provide feedback.
Your IT shop is set up to handle those enhancements of the delivery models.
Your product is agile and designed to regularly be enhanced to meet the needs of users increasingly relying on the data.
Data Dashboards Have Become a Prime Feature of Business Presentations
A data-driven organization uses its analytics tools in presentations. So when you see users showing or embedding a dashboard in their presentations, that's a sure sign that data-driven adoption is occurring.
Finally, remember the old military adage that the troops perform best in what the leaders are personally interested in? In promoting a data-driven culture, analytics must be championed from the top. Leadership has to buy in and lead the way to the data-driven culture.
Here are 7 ways to Make your Business More Data-Driven:
1. Establish a centralized analytics delivery organization.
In most organizations, this will be the IT department. What is required is a group that develops and delivers analytics and data standards. Developing and delivering analytics requires coordination, collaboration, and consultation with all data originators, recipients, and beneficiaries.
Determining what each user and group NEEDS is essential to the successful adoption of a data-driven culture. Without that determination, adoption will not occur. Otherwise, users will pay lip service and return to their data silos.
2. Engage the business users on their needs for data.
This is a give-and-take process. At KASH Tech, we refer to this as the "Art of the Possible." It is a consultative approach to gathering and extracting the correct data and showing the business user what is possible. IT organizations must consider hiring a business analyst person with a consultative personality who will go to the end users to gather requirements and push for improved information delivery to do their jobs.
3. Enable managed access to data.
As a subset to number 2 above, the central analytics group must define the data sets the organization will use. Then users must have access to the data and understand what it tells them.
4. Empower the user to ask for understanding, education, and more data to do their job.
Make it a two-way street with feedback, and make users active participants. If you need data for your job, there should be an easy way to access it. If you don't understand the data, there should be a quick way to get an answer. For example, guided applications (dashboards) can have the definitions built directly into them.
5. Establish data governance to ensure accurate and secure data delivery.
Beware of inaccurate and ill-defined data. Both lead to bad decisions. Likewise, wrong conclusions result when decision-makers don't know what data is available or the data is ill-defined.
You need secure data delivery. This means giving access to the appropriate people, i.e., those who should have it. Access controls and permissions are the first lines of defense against hackers or accidental data corruption by well-meaning employees. Protecting the company's data assets (downloading data, etc.) is the key to data governance.
Also, calculations and metrics need to be clearly defined across the organization at the beginning. For example, if gross margin is calculated differently across the business, it shouldn't be called gross margin; it should have a qualifier or be consistent across the board. If it is calculated differently, how does it relate to another calculation of "gross margin"? Clear up those ambiguities and inconsistencies so that your metrics are consistent.
6. Provide data training.
If you provide data, all users should know what it is and how to use it. Documentation and training are the keys. Users want to do their best to apply the data that makes their jobs more productive and more effortless.
7. Deliver or adopt a suitable analytics tool model for each type of business audience.
1. Provide the user with a DIY analytics dashboard tool*
2. Provide a guided analytics application (specified data input followed by standard reports and dashboards).
3. Provide a guided analytics application with some free-form analytics capabilities (a variation of 2 above, with enhancements
4. Provide an interactive analytics application that allows data manipulation, correction, and entry along with workflow for approvals.
5. Provide a hybrid approach to the above
*Important note: You need to avoid #1 (the do-it-yourself approach) by itself. Many vendors call this tool "self-service analytics" meant to fit all audiences. Unfortunately, it doesn't. Your users' and clients' needs won't be met without some combination of analytics tools tailored to their business requirements.
The Bottom Line
Setting up a data-driven culture is not a one-and-done. It needs to be its own branch of the business. Businesses aren't static, so the data BI strategy should continually evolve as the business does. It should be a continuous information delivery model.
The success of a data-driven culture depends on making governed and accurate data available and translating that data into analytics delivery that is meaningful and useful now.