Although you probably aren’t thinking about organizational implications when you learn new data skills, the strategies of your company may ultimately determine how successful you and others are at getting the most from available data.

The organization itself, or rather the leaders of it, have a responsibility to build a cohesive data strategy that empowers its people to be the data-driven employees it claims to need.

Below are five questions that your company must work through on its path to become data-centric. Even if you aren’t directly involved in each stage, you should proactively press senior management if you sense a lack of clarity or progress for any area.

1. What are our organizational objectives?

The first question is about focus and priorities. What are we trying to accomplish? What are our unique capabilities? What does success look like?

Although data may not be the centerpiece of your core business, it will almost certainly support your operations and product development.

Data Strategy by Bernard Marr is a great resource for companies attempting to balance overall strategy with data considerations.

2. What data do we have or need to find in order to support these goals?

Once you answer question 1, you should:

Take inventory of existing data

This is a very important exercise, especially for medium and large organizations that likely sit on more pieces of information than they realize. You should get representatives from each team in a room and catalog what datasets are being generated or used by a given group. There are many benefits to this. The organization will get a better sense on how much data is currently available and individuals will learn how others are using data to support their work.

Brainstorm potential data

This exercise is a token gap analysis. Start by reiterating your organizational objectives and summarizing the sources of data identified during the inventory. Then ask, “what data do we not have that are needed to achieve our goals?”. The result is a list of action items. Some will be easy (e.g., turn on Google Analytics for all web properties) and some will take more effort (e.g., collect consistent user feedback metrics across all divisions).

3. How can we collect and store this data in the best way?

Now things turn more technical. How will specific data assets be collected? Once collected, where will they be stored? Coordination between data engineers, analysts, and business leaders is crucial. This stage requires significant investment in both talent and infrastructure and can take months or years to setup depending on the current systems and complexity of plans.

The end user (e.g., employees trying to understand the market better or customers purchasing insight products) doesn’t care about how hard it is to maintain the ETL, Data Lake, or Data Warehouse, they just expect a useful environment to interact with accurate insights.

4. How do we ensure the proper legal, ethical, and accurate use of these data assets?

Data collection and use is increasingly under the microscope from ethical and legal considerations. Policies such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States are forcing organizations to have a better idea of why and what they are collecting and how it is going to be stored and used.

Cross-functional Data Governance groups are often organized as a centralized resource in addressing such issues. They may contain representatives from across the organization who have an active or passive interest in collecting, maintaining, or utilizing data. Most commonly these include members from IT, legal, product, and analytics teams. In addition to keeping an eye on compliance, the committee may also help oversee the organization of a company’s data assets and attempt to assign monetary value to it.

5. How can we empower our employees to generate insights, make evidence-based decisions, and create derivative value from our data?

Finally, we need to ensure that everyone in the organization is empowered to use available data to better understand the business, so that discussions and decisions are based on information over instinct.

Business Intelligence (BI) tools are often at the forefront of this goal. Dashboarding products such as Tableau, Microsoft Power BI, or Looker connect to an organization’s data assets and have the potential to surface data and insights in engaging ways - even for non-technical people.

Success of a BI tool rollout depends on (1) the condition of a company’s data systems, (2) clear documentation for what is available, (3) encouragement from senior leadership for making of data-driven decisions, and (4) a baseline level of data literacy for all employees.

Moving forward together

Organizations spend millions in the pursuit of data transformations. These investments are justified based on the belief that becoming a data-centric organization will lead to better operational decisions, product enhancements, and customer engagement - all of which should improve the bottom line. Having clear strategies and assigned ownership relating to the five questions above will improve your company’s chances of success.

You can keep progress on track by asking questions when clarity lacks. For example:

• Do we have a data governance team?
• Where can I find more information about the data in this dashboard?
• How long do we store customer information for?
• Is there a data privacy policy that I can share with customers?
• What is the single source of truth for monthly financial numbers?

Want to identify more data-led strategies? Explore these courses.

• Big Data Strategies To Transform Your Business from edX
• Data Science For Business from DataCamp
• Data Science For Business Innovation from Coursera