The data mesh makes business domain experts the owners of their data, which they deliver as a “data product” to analytics teams using a self-service data platform and a federated governance framework. Published at: https://www.eckerson.com/articles/why-enterprises-should-implement-the-data-mesh-with-dataops

An operating model for data & analytics is critical for aligning resources across the enterprise and balancing the needs for agility and governance. An effective operating model is critical to data & analytics success and its creation and upkeeping should be the primary focus of a chief data officer. Published at: https://www.eckerson.com/articles/an-operating-model-for-data-analytics

An analytics center of excellence is the cornerstone of every data strategy, yet few data leaders know how to design one that works effectively. The key is to embrace federated techniques that balance standards and speed, agility and governance. This article explains the core components of an analytics center of excellence. Published at: https://www.eckerson.com/articles/how-to-design-an-analytics-center-of-excellence

Consider key trends and challenges as you design an effective organizational architecture for data governance while generating value with pervasive analytics. Published at: https://www.eckerson.com/articles/organizational-architecture-can-make-or-break-your-data-governance-program

An analytics center of excellence is the cornerstone of every data strategy, yet few data leaders know how to design one that works effectively. The key is to embrace federated techniques that balance standards and speed, agility, and governance. This article explains the core components of an analytics center of excellence. Published at: https://www.eckerson.com/articles/how-to-design-an-analytics-center-of-excellence

Today’s data architecture discussions are heavily biased toward managing data for analytics, with attention to big data, scalability, cloud, and cross-platform data management. We need to acknowledge analytics bias and address management of operational data. Ignoring operational data architecture is a sure path to technical debt and future data management pain. Published at: https://www.eckerson.com/articles/the-yin-and-yang-of-data-architecture

We in the West have watched Russia's invasion of Ukraine with disbelief and horror. How could this happen to a European country in the 21st century? Is there any justifiable rationale for the wanton destruction of people and property there? As we ponder these questions, our data colleagues in Ukraine have experienced the war firsthand.

To help us get a handle on Ukraine's role in the data economy and how teams based there are coping with Russia's military onslaught, Wayne interviews two software executives today who share how the war has affected their companies and how they are adapting to the evolving situation.

Dragos Georgescu is vice president and chief technology officer of DataClarity, an innovative data analytics vendor with a development shop in Lviv, Ukraine.

Bogdan Steblyanko is CEO of CHI Software, a software development company based in Ukraine with more than 500 employees spread across four development centers, including hard-hit Kharkiv in the east, which is the company's headquarters.

The number of women entering data professions is growing, and men need to adapt. This podcast is designed to enlighten men about the role of women in the data field. Our guests are all executives at data and analytics software companies who have held positions in other sectors of our field: Prukalpa Sankar, Cindi Howson, Debika Sharma.

Nothing has galvanized the data community more in recent months than two new architectural paradigms for managing enterprise data. On one side there is the data fabric: a centralized architecture that runs a variety of analytic services and applications on top of a layer of universal connectivity. On the other side, is a data mesh: a decentralized architecture that empowers domain owners to manage their own data according to enterprise standards and make it available to peers as they desire.

Most data leaders are still trying to ferret out the implications of both approaches for their own data environments. One of those is Srinivasan Sankar, the enterprise data & analytics leader at Hanover Insurance Group. In this wide-ranging, back-and-forth discussion, Sankar and Eckerson explore the suitability of the data mesh for Hanover, how the Data Fabric might support a Data Mesh, whether a Data Mesh obviates the need for a data warehouse, and practical steps Hanover might to take implement a Data Mesh built on top of a Data Fabric.

Key Takeaways: - What is the essence of a data mesh?
- How does it relate to the data fabric? - Does the data mesh require a cultural transformation? - Does the data mesh obviate the need for a data warehouse? - How does data architecture as a service fit with the data mesh? - What is the best way to roll out a data mesh? - What's the role of a data catalog? - What is a suitable roadmap for full implementation?

Nothing has galvanized the data community more in recent months than two new architectural paradigms for managing enterprise data. On one side there is the data fabric: a centralized architecture that runs a variety of analytic services and applications on top of a layer of universal connectivity. On the other side, is a data mesh: a decentralized architecture that empowers domain owners to manage their own data according to enterprise standards and make it available to peers as they desire.

Most data leaders are still trying to ferret out the implications of both approaches for their own data environments. One of those is Srinivasan Sankar, the enterprise data & analytics leader at Hanover Insurance Group. In this wide-ranging, back-and-forth discussion, Sankar and Eckerson explore the suitability of the data mesh for Hanover, how the Data Fabric might support a Data Mesh, whether a Data Mesh obviates the need for a data warehouse, and practical steps Hanover might to take implement a Data Mesh built on top of a Data Fabric.

Gordon Wong is on a mission. A long-time business intelligence leader who has led data & analytics teams at HubSpot and FitBit, Wong believes BI teams aren’t data-driven enough. He says BI leaders need to think of themselves as small businesses owners and aggressively court and manage customers. He says too many don’t have metrics to track customer engagement and usage. In short, BI teams need to eat their own dog food and build success metrics to guide their activities.

If you are a data or analytics leader, do you know the value your team contributes to the business? Do you have KPIs for business intelligence? Can you measure the impact of data and analytics endeavors in terms the business understands and respects? Too often BI and data leaders get caught up in technical details and fail to evaluate how their technical initiatives add value to the business. This wide-ranging interview with a BI veteran will shed light on how to run a successful BI shop.

The advent of big data, self-service analytics, and cloud applications has created a need for new ways to manage data access. New data access governance tools promise to simplify and standardize data access and authorization across an enterprise. Data management expert, Sanjeev Mohan, provides an industry perspective on this emerging technology and what it means for data analytics teams.

In the physical world, you can see a bridge rusting or a building facade crumbling and know you have to intervene to prevent the infrastructure from collapsing. But when all you have is bits and bytes - digital stuff, like software and data ---how can you tell if your customer-facing digital interactions or data-driven analytics and models are about to go up in smoke?

Observability is a new term that describes what we used to call IT monitoring. The new moniker is fitting given all the technology changes that have happened in the past decade. The cloud, big data, microservices, containers, cloud applications, machine learning, and artificial intelligence have created a dramatically complex IT and data environment that is harder than ever to manage. And the stakes are higher as organizations move their operations online to compete with digital natives. Today, you can't run digital or data operations without observability tools.

Kevin Petrie is one of the industry's foremost experts on observability. He is vice president of research at Eckerson Group where he leads a team of distinguished analysts. He recently wrote an article titled "The Five Shades of Observability" that describes five types of observability tools. In this podcast, we discuss what observability is, why you need it, and the types of available tools. We also speculate on the future of this technology and recommend how to select an appropriate observability product.

In this episode, we explore an area of data analytics that everyone knows they need to improve but no one knows how to do it. That is data literacy. Data literacy ensures that business people have the skills to accurately interpret data represented in charts, tables, and dashboards, as well as the knowledge to use those tools to gather and analyze data on their own.

To guide us through the nuances of data literacy and explain how to implement it in an organization, we invited a data literacy expert to share the secrets of his trade. Kirill Makharinsky is the founder of Enki, a San Francisco-based company that provides data-as-a-second language training services. Kirill is a serial entrepreneur, having previously co-founded ETG, one the largest online B2B travel companies in Europe, and Quid, a leading research and analysis tool.

Every December, Eckerson Group fulfills its industry obligation to summon its collective knowledge and insights about data and analytics and speculate about what might happen in the coming year. The diversity of predictions from our research analysts and consultants exemplifies the breadth of their research and consulting experiences and the depth of their thinking. Predictions from Kevin Petrie, Joe Hilleary, Dave Wells, Andrew Sohn, and Sean Hewitt range from data and privacy governance to artificial intelligence with stops along the way for DataOps, data observability, data ethics, cloud platforms, and intelligent robotic automation.

The COVID shock forces enterprises in every market to accelerate and reshape their data analytics strategies. This trend is likely to continue. “Data Elite” enterprises survived this year through a mix of agility, efficiency, and intelligence. They met these requirements of survival as they accelerated their digital transformations, adopted cloud data platforms and embraced advanced analytics. As these data leaders continue their momentum in 2021, the data laggards will strive to catch up.

In this episode, Kevin Petrie, VP of Research at Eckerson Group, interviews Sumeet Agrawal, VP of Product Management at Informatica, to discuss the impact of COVID on enterprises. Sumeet talks about the trends of adoption during the onslaught of COVID and how enterprises are navigating in the post-pandemic era.

Continuous Intelligence (CI) integrates historical and real-time analytics to automatically monitor and update various types of systems, including supply chains, telecommunications networks and e-commerce sites. CI encompasses data ingestion, transformation and analytics, as well as operational “triggers” that recommend or initiate specific real-time actions.

CI casts a wider net than traditional analytics because it includes contextual data, for example related to market behavior, weather patterns or social media trends, that help enterprises operate the core systems more intelligently.

In this episode, our VP of Research Kevin Petrie interviews Simon Crosby, CTO at Swim.ai, a continuous intelligence software vendor that focuses on edge-based learning for fast-data. He co-founded security vendor Bromium in 2010, later sold to HP Inc in 2019.

This blog compares Predictive vs Prognostic analytics and gives a quick view into systems dynamics and causal modeling. If it sparks your interest, watch for an upcoming series of articles connecting the practices of systems thinking, causal analysis, and analytics.

Originally published at: https://www.eckerson.com/articles/looking-at-the-future-through-analytics-predictive-vs-prognostic

This blog is about Continuous Intelligence (CI) and how it integrates historical and real-time analytics to operate, monitor and tune systems of all types. Our next blogs will explore architectural approaches to CI, and how to navigate the trade offs it introduces to your organization.

Originally published at: https://www.eckerson.com/articles/continuous-intelligence-the-nexus-of-data-integration-analytics-and-operations

This audio blog discusses cloud adoption and how data teams will migrate an increasing portion of their on-premises operational and analytics workloads to the cloud. They can best meet budget and project requirements by using data streaming technologies such as change data capture (CDC), which replicates real-time updates between data source and target.

Originally published at: https://www.eckerson.com/articles/the-next-wave-of-cloud-migrations-needs-data-streaming

This audio blog is about how the CHOP’s data and analytics (DnA) team uses near real-time data and information to decide how to marshal its resources to contain the pandemic. The culmination of all of this work has been an enterprise COVID-19 dashboard that is distributed to enterprise leadership daily. Originally published at: https://www.eckerson.com/articles/chop-harnesses-the-power-of-data-analytics-to-address-the-covid-19-pandemic

As of this writing, billions of consumers live in quarantine. They buy what they need online, comforting themselves with food, TV, and toilet paper. Nobody is splurging at the mall.

To say the least, it is an interesting time to analyze discretionary consumer behavior. As Director of the Voice of Consumer Analytics at Adidas, Tiankai helps measure and manage the perception of a consumer brand that is mentioned on social media an average of 260,000 times per day. An amateur musician, Tiankai went viral himself lately with his series of “Quarantunes,” songs such as “Self Quarantine” and “Parent in Quarantine,” that poke fun at our homebound predicament.

Tiankai recently spoke with Eckerson Group about the art and science of consumer analytics, the COVID-19 conundrum, and (of course) the role of creativity in modern data analysis.

Chief data officers (CDOs) first appeared in enterprise organizations after the Sarbanes Oxley Act became law in the United States in 2002 to improve corporate governance controls. CDOs started with a trickle, but have since become a flood, now populating more than two-thirds of large enterprises, according to a recent survey by NewVantage Partners.

To explore this dynamic role in detail, we invited Joe Dossantos, newly minted CDO for the data and analytics software vendor Qlik. Joe is responsible for data governance, internal data delivery, and self-service enablement. He also evangelizes data and analytics best practices to Qlik customers.

Prior to joining Qlik, Joe led TD Bank’s data strategy, and built and ran the Big Data Consulting Practice for EMC Corporation's Professional Services Organization.

Data leaders who launch self-service analytics programs without knowing their business users risk unleashing chaos. Data leaders need to canvas the organization and understand who produces what information for whom and where.

Originally published at https://www.eckerson.com/articles/succeeding-with-self-service-analytics-know-thy-customer

The rise of machine learning has placed a premium on finding new sources of data to fuel predictive models. But acquiring external data is often expensive and many data sets are rife with errors and difficult to combine with internal data. But that’s going to change in 2020.

To help us understand the scale, scope, and dimensions of emerging data marketplaces is Justin Langseth, one of the visionaries in our space. Justin is a VP at Snowflake responsible for the Snowflake Data Exchange.  Prior to Snowflake, Justin was the technical founder and CEO/CTO of 5 data technology startups: Claraview (sold to Teradata), Zoomdata (sold to Logi Analytics), Clarabridge, Strategy.com, and Augaroo. He has 25 years of experience in business intelligence, natural language processing, big data, and AI.