Topics

The people, process, and technology required to deliver data & analytics solutions faster, better, cheaper. 

Guidelines and designs that specify how data is ingested, integrated, stored, accessed and consumed by business users, models, applications, and systems.

A business program that oversees the people, processes, policies, and standards required to manage data assets. A good data governance strategy balances openness and control, enabling companies to minimize risk and leverage data to its fullest.

Self-service is easy to promise, but hard to deliver without creating chaos. How do you balance user empowerment with sufficient governance to ensure data consistency and trustworthiness? Self-service is not a tool; it's the culmination of doing everything else right in your data environment, from data architecture and governance to an operating model. 

A data strategy is a communications vehicle: it shows executives what they'll get for their investments in data; it tells department heads and staff the data services they'll receive and when; and it aligns the data team, giving them direction and purpose.

To help your organization get more value from data, we've created this Guide to Data Strategy, which consists of 18 resources from our research library. The guide is designed to help data leaders and business executives understand, plan, and implement a data strategy. Contact us if you want to discuss any or all of this! 

Eckerson Group has been researching and writing about data mesh and data fabric since they each took hold of the data world. While they're no longer new concepts, the way organizations implement them is always changing. And that's what fascinates us. Learn how they can work together to solve your modern data challenges.

There is an almost unfathomable number of data & analytics products on the market today. So how do you choose the right one for your organization? This topic page contains resources that both provide tips, tricks, and templates for navigating the evaluation and selection process as well as evaluation guides for specific technologies, such as data catalogs, autoML tools, data marketplaces, and performance dashboards.  

If the data is the lifeblood of analytics and AI, then it's imperative that organizations manage the quality of their data, in particular the accuracy, completeness, and consistency of data and that it is fit for purpose and meets user expectations. 

For many data leaders, data monetization involves selling a data product to a third party or creating a customer dashboard sold as part of a service. In these scenarios, data generates top-line revenue and moves data operations from a back office to a front office activity. Few companies today make money from data but most use data to generate insights, streamline processes, and troubleshoot issues. There is business value in this type of back-office operation, but it's hard to quantify and even harder to materialize in a profit-loss report. Nonetheless, some data experts consider this data monetization.     

We at Eckerson Group are big believers in data products. They promise to address the core problems we see at EVERY client, which can be expressed in this mantra:

“Business users can’t find the right data; when they find it, they can’t access it; when they access it, they don’t trust it.”  

Baked into this mantra are the data problems afflicting most organizations: a backlog of data requests, data silos, poor governance, inconsistent results, minimal data sharing, and frustrated business users. In our opinion, data products can address most of these ailments.