This blog marks a new beginning. Given Eckerson Group’s work helping organizations develop data strategies and implement modern data architectures, we’ve decided to change the name of this blog to “Data Strategy Insider”. Our goal is to give readers a closeup look based on client engagements at what it takes to harness the power of data and analytics. We’ll expose challenges that companies face establishing a strong data foundation and surface best practices for turning that data into insights and business value. For many organizations, this information will come just in the nick of time.

The Data Divide

The Data Divide has replaced the Digital Divide in determining winners and losers in the new economy. Organizations that fail to master data and analytics are now falling behind competitors who do. The winners use data to proactively capitalize on opportunities and address problems while losers react after the fact, doing too little too late until they can no longer compete cost-effectively with fleet-footed rivals.

This not just about building better dashboards and democratizing data. It’s more revolutionary than that. The new Data Elite recognize that machines often make better decisions than humans. They use machine learning and artificial intelligence models to drive most decisions and actions. Their goal is to automate decisions when possible—and when not—use machines to present humans with options and tradeoffs.

Disruption by data is the weapon of choice used by ambitious start-ups and visionary incumbents. Digital natives, such as Amazon, Google, and Apple, lead the revolution, along with many new cloud providers. But top hedge funds, a few insurance companies, and some investment banks are following suit. Soon enough, the Data Divide will come to an industry near you.

What’s Needed

Good Data. Model-driven enterprises need lots of high-quality, timely data to fuel their algorithms.  But there is no “easy button” to fix data, which is in a sorry state at most companies due to acquisitions, organizational silos, fragmented operational systems, inconsistent master data, and lack of disciplined, validated data entry. It takes vision, commitment, and money to create a strong data foundation to power a modern, model-driven enterprise.

Good People. It also takes an army of data analysts and scientists to create, tune, and manage models that run the enterprise. Coordinating the work of these analysts is a major challenge. Most work in isolation in the bowels of various departments and continually reinvent what others have already done. They also spend more time finding and preparing data than analyzing it. Bringing these analysts into an extended data analytics family is key to landing on the right side of the Data Divide.

Good Strategy. So there is a lot to talk about. Modernizing an organization to harness data and analytics is challenging. It takes a lot of careful planning and strong execution. To guide this effort, organizations need to craft a comprehensive data strategy that balances people, process, organization, technology, and culture. This blog will tackle these dimensions of a data strategy through questions and issues we address for our clients. Some potential topics:

• How do you deal with lack of consistent master data for customer or product? Do you harmonize (i.e., map product IDs) while working to standardize master data in the source? Or just focus entirely on standardization?
• How do you handle changes in source systems, especially those that come unexpectedly? Is there a way to detect and model those in flight?
• How do you standardize key metrics and where do you store the logic?
• How do you ensure that your self-service initiative doesn’t create multiple versions of the truth?
• Do you need a centralized team of report writers or should you require business unit write all their own reports and dashboards? How do you ensure they use the proper business logic?
• How do you organize data scientists to get the optimal value from their experience and work?
• How do you handle operational reports? Who is responsible for creating them and where should users go to access them and request changes?
• How do you create more curious business users who think outside their departmental box and do more value-added analysis?

These are just some of the issues we grapple with inside client accounts. If you have a question or problem that you need to address to harness the power of data and analytics, please let me know! We want to make sure you make it safely to the right side of the Data Divide!