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.

Data is critical for learning about the needs of the market, product bugs and issues, competitive solutions, and many other things. As such, analytics plays an important role in the innovation process.

Originally published at https://www.eckerson.com/articles/how-can-analytics-support-business-innovation

In this episode, Wayne Eckerson and Matthew Schwartz discuss non-traditional uses of business intelligence tools. Although BI tools have been around for almost three decades, most companies just scratch the surface of what’s possible to do with those tools. Using web layers and APIs, a company can use their imagination to customize and leverage their exiting BI tool-set to monetize data, integrate tribal knowledge and build industry-specific proprietary products.

Matthew Schwartz is the chief technology officer of Sage Hospitality, one of the world's largest hotel operators. Although Matt is responsible for all aspects of Sage’s IT operations, he has a deep fondness for data and analytics, having served as a BI director for several companies, including PetSmart and Staples. Matt firmly believes in the power of BI tools to transform organizations.

This audio blog probes the business value in analytics by examining the concept of Action Distance.

Originally published at https://www.eckerson.com/articles/finding-value-in-analytics-action-distance

One of the hardest parts of running a data analytics program inside a large organization is governing data and reports. It’s simply too easy for the definition of core data elements and metrics to get out of sync and reports to contain conflicting information.

Angie Davis has straddled both the business and IT worlds for more than 20 years. She served as a business analyst in several organizations before switching to the information technology side of the business where she ran analytics teams, first at JD Irving for six years and more recently at Brookfield Renewable where she is an IT director. Angie has a degree in mathematics and electrical engineering from Dalhousie University in Halifax, Nova Scotia.

This audio blog focuses on the importance of establishing a strong relationship between business and technical teams and describes various business engagement models that sit at the heart of all successful data analytics programs.

Companies that excel at advanced analytics and data science maximize the value of their data. They unearth hidden opportunities and become innovators in the industry. Although each organization has different goals, the underlying processes and tools to become successful at analytics remain somewhat the same. In this episode, Alan Jacobson explains them one by one and finishes off with his top three recommendations.

Alan Jacobson is the chief data and analytics officer (CDAO) of Alteryx, driving key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson leads the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains, responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.

Alan was recognized as a top leader in the global automotive industry as an Automotive Hall of Fame Leadership & Excellence award winner and an Outstanding Engineer of the Year by the Engineering Society of Detroit, and works with the National Academy of Engineering and other organizations as an advisor on data science topics.

With the growing popularity of machine learning and artificial intelligence, creating a data science program is a key initiative at most companies today. However, it’s not always clear to executives how they can deliver a return on investments in data science. To explain this, we invited an expert who has spent most of his career in the data science trenches and has a clear-minded perspective on how to deliver ROI with data science.

Alan Jacobson is the chief data and analytics officer (CDAO) of Alteryx, driving key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson leads the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains, responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.

Prior to joining Alteryx, Alan held a variety of leadership roles at Ford Motor Company across engineering, marketing, sales and new business development; most recently leading a team of data scientists to drive digital transformation across the enterprise. As an Alteryx evangelist at Ford, Alan spent many years leveraging the Alteryx Platform across the company and witnessed first-hand the impact a culture of analytics can have on the bottom line and what it takes to succeed as a data-driven enterprise.

How do you organize a data analytics program to maximize value for the organization? Although there is no right or wrong way to do this, several patterns emerge when you examine successful organizations.

Originally published at https://www.eckerson.com/articles/organizing-for-success-part-ii-how-to-organize-a-data-analytics-program

The goal of self-service analytics is to empower business people to build their own reports, dashboards, and predictive models. If that happens, does your company still need a corporate business intelligence team?

Originally published at https://www.eckerson.com/articles/organizing-success-part-1-organize-bi-team

Before a company hires data science talent, they should understand the role and types of data scientists. Failing to differentiate between research, applied, and citizen data scientist can result in appointing the wrong people on crucial projects. To continue our previous episode's discussion, we invited Alex Vayner for a second time to get an answer to the question: What is a data scientist?

Alex Vayner is a Partner and Americas Data & AI Practice Leader for PA Consulting Group, an innovation and transformation consultancy. Alex has spent his entire career in data & analytics, with his last five roles focused on building and running high-performance data science teams and capabilities in consulting and corporate environments. Before joining PA Consulting, Alex ran the NA Data Science & AI practice at Capgemini. He joined Capgemini from Equifax, where he served as VP, Global Data Innovation Leader, building a team responsible for pioneering disruptive data & analytics solutions for clients across all industries.