Culture reflects the unwritten rules about how things get done in an organization. The data culture in many companies resembles the Wild West--everyone relies on their own data and resources and distrusts everyone else’s. Data silos abound and data workers spend long hours harmonizing fragmented and inconsistent data. These organizations spend more time arguing about data than acting on it.

In contrast, data-driven organizations live and breathe data. Users trust data and take an active role in governing it. Executives use standard metrics, reports, and master data when making decisions. Analysts spend more time analyzing data than finding and cleaning it. These organizations empower people with self-service tools and custom training programs. They use data to work proactively and increasingly apply algorithms to optimize or automate business processes.

This series describes twelve characteristics of an analytics culture. For each characteristic, it describes signs that it exists and symptoms when it doesn’t. Most importantly, it presents techniques that can help organizations cross the data divide and become data-driven. It groups these characteristics into four categories: top-down, bottom-up, middle-out strategies, and technology-in.

Different Starting Points 

Data Pedigrees. Some companies have an advantage in creating an analytics culture because their product is fundamentally data. This certainly applies to financial firms, insurance companies, retailers, and telecommunications firms. Their core products and services (money, risk, inventory, and dial tone) are represented as data. If they can’t properly manage data, they aren’t in business for long.

Data Accelerators. If your company is a manufacturer of automobiles, chemicals, or steel or produces consumer packaged goods, such as beer, toys, or computers, it’s at a slight disadvantage. It often takes enlightened executives (or a lot of hard knocks) before the organization recognizes the importance of data. Some executives now see that the value of data may be greater than the company’s core product. Without data, they can’t anticipate or respond quickly to competitive threats and opportunities. It’s like running a marathon blindfolded.

Data Products. In addition, executives are starting to see that data and analytics can be a new revenue stream in itself. Data about customers and the way they interact with products and each other is a veritable gold mine. If companies can harvest that data internally, they can create better products and services and anticipate customer buying trends. If they can anonymize and aggregate that data, they can sell it to interested third parties and industry players and create a new revenue channel.  

Twelve Factors

There are many ingredients that go into creating a culture of analytics. The overarching culture of the organization is huge. Does the company encourage new ideas? Does it reward innovation? Does it unify business units and departments while giving them sufficient autonomy? Does it have an open culture where groups share information and ideas?

These factors influence how quickly an organization can create a culture of analytics. Without these factors, it is harder to get traction for data and analytics; with them, it accelerates its formation. Whether these factors are in place or not, there are many things an organization can do to foster an analytics culture. Here are twelve things organizations can do to foster an analytics culture, divided into four categories.  

Top-Down Approach (Executives)

• Commitment. The CEO has issued a mandate to treat data as a corporate asset and invests time and money to build the infrastructure and processes to do it.
• Example. Top executives use standard KPIs and certified reports when making decisions and avoid using spreadmarts.
• Decisions. Executives and managers use data to validate their intuition and intuition to validate the data. They know the best decisions happen when experience and data align.

Bottom-Up Approach (Employees)

• Trust. Business users trust the data and take an active role in governing it. Data owners and stewards work closely with data teams to ensure high-quality and consistent data.
• Empowerment. Business users generate insights on their own, using self-service tools and well-defined governed processes to ensure data alignment.
• Literacy. The organization fosters data and analytics knowledge through a curriculum of courses, certification programs, coaching, and mentoring.

Middle-Out Approach (Departments)

• Council. The organization maintains an active Analytics Council that serves as a board of directors for all data analytics resources and activities in the enterprise.
• Governance. The Council in conjunction with the data team takes an active role in governing reports and data (e.g. metrics, dimensions, master data).
• Collaboration. Departments readily share data while business users share tips, tricks, and reusable code through meetups, online forums, and a shared repository of work.

Technology-In (Corporate Data Team)

• Center of Excellence. The data team creates a federated center of excellence to align departmental and corporate resources and foster strategic technology partnerships.
• Data Platform. The data team runs a data management platform that delivers clean, consistent enterprise data and tailored views for each department and use case.
• Data Agility. The data team uses agile, techniques, DataOps processes, and cross-functional teams to build solutions quickly that deliver real business value. 

There are other characteristics of an analytics culture, in fact, many more. But these are some of the most important, and ones that are largely actionable. If you want an in-depth look at all the factors required to generate a culture of analytics, you will be interested in having your data team take Eckerson Group’s Success Signals assessment, which presents 150 best practices statements for data teams to evaluate. We have an abridged version of 30 questions that takes 12 minutes to complete.

The next article in this series will drill down into the top-down approaches and discuss how to make them happen.

Read - Creating an Analytics Culture, Part II: Top-Down and Bottom-Up Traits