CIO Magazine

You want to be Agile.  I get it.  But whenever a business leader tells me that they want to be “Agile”, my first question is: “Why? What does that mean to you? And how will you know if you’re making progress?”  

Real Agile transformations are hard…. really hard. They require organizational fortitude, tough prioritization choices, institutionalized trust, and a team of leaders who is willing to constantly encourage small failures. 

There are 3 phases of agility:

1. Knowing Agile 
2. Doing Agile 
3. Being Agile 

KPMG

 Knowing Agile is providing academic information about business agility and Agile practices.  It starts with training and education, and often includes some level of certification or recognition that training has been completed.  It typically does not include practical knowledge or experience.

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In today’s hybrid blend of on-premises and virtual servers, public and private cloud platforms, and mobile devices of all types, it is more critical than ever to identify, resolve, and mitigate potential outages or performance issues. And these issues are now more difficult to detect and identify, much less remediate. Both the volume and variety of network and application traffic that data enterprises are generating have skyrocketed.

To effectively monitor and manage IT operating at this level of complexity, organizations are turning to artificial intelligence for IT operations, or what’s called AIOps. The term AIOps describes using artificial intelligence (AI), machine learning (ML), and other advanced data analytics technologies to automate the processes of identifying and resolving IT performance issues. AIOps uses the massive amounts of data generated by IT systems and services—both physical and virtual—to monitor all those enterprise assets, proactively examine network and application traffic data, and achieve full visibility into all applications and system dependencies.

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Oh, the wonderous things artificial intelligence, machine learning, deep learning, big data analytics, and the correlations and causations from data science are uncovering these days! With so many new findings, it is critical for CIOs to stay on top of the ethical challenges these new insights are bringing their way.

Data monetization is a growing big data analytics business, anticipated to reach over $200 billion by 2020. The result of such analytics is expected to expand the growth of data from 33 trillion gigabytes (33 zettabytes) in 2018 to 175 zettabytes in 2025. All this data being analyzed and sold means more and more businesses are going to know more about each of us. Let’s look at some examples…

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Silicon Labs is a global, “fab-less” semiconductor company. Historically, it focused on niche markets such as chips for TV tuners. Its customer base was small and tightly focused, so the company had little need for digital marketing. But when it undertook a strategic product shift to chips for the Internet of Things (IoT), industrial automation, consumer automation, and broader markets, the number of customers and prospects grew dramatically.

As Deirdre Walsh, director of marketing and communications for Silicon Labs, observed, “We went from a few hundred customers to more than 35,000 in a very short period. Many business processes changed. Perhaps the most important was implementing a digital-first marketing approach. We had to rethink marketing to support the entire customer journey and move our metrics and management from a high-level view to a more detailed and comprehensive view of our entire process.”

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There is a lot of discussion about CIO leadership in professional and academic circles. One topic that rarely makes headlines is developing the next generation of CIOs. Technical training and the accumulation of acronym-heavy credentials is the norm in IT organizations. This is important given the changing nature of information technology.

However, there is no more valuable set of skills than the “leadership” type – especially for professionals with a technical background. Such skills as executive communication, relationships with business partners and articulating the value of IT are required for all IT leaders and must be intentionally developed over time. Leadership development should be led by CIOs, not HR professionals.

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Many brands have recognized that LinkedIn is invaluable to their marketing efforts. Now Adobe and LinkedIn are working together to significantly increase the platform’s marketing utility, especially for account-based marketing (ABM) programs. Any brand that is serious about ABM can now take it to the next level.

Marketo Engage, part of Adobe Experience Cloud, is making it easier to draw information from LinkedIn’s base of users that can contribute to highly targeted, effective campaigns.

Brian Glover, head of product marketing for Marketo Engage, said that the new ABM enhancements “start with the process of identifying the right accounts that best fit a specific ABM initiative. Using Adobe’s Marketo Engage application, with its AI capabilities and analytic tools, brands can build a specific set of accounts that best fit very specific criteria.”

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Self-service analytics tools have long empowered users to produce data visualizations without the need for IT intervention. Recent advances, such as data prep automation, have further lowered the barrier of entry, but this push to democratize analytics surely has its limits. After all, users still have to interpret the data visualizations they produce.

Tableau’s 2019.3 software update, released Wednesday, aims to address this issue with a new feature called Explain Data that seeks to tell the story behind the chart, delivering analysis in clear language to those without the statistical expertise to do it for themselves.

In a way, Explain Data is the counterpart to Ask Data, which Tableau included in its 2019.1 release in February. Ask Data enables Tableau users to describe in a chat window the visualizations they want to see. It then takes this natural language input, uses it to construct a visualization, and allows users to refine the chart or to layer on more data interactively.

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The Estée Lauder Companies (ELC) Information Technology (IT) operations are in New York City, where the unemployment rate is low and the competition is high. When Michael W. Smith became CIO of ELC in August 2017, he knew that recruiting, developing, and retaining IT talent would be as important to the company as process improvement and consumer engagement. That’s when Smith, his IT leadership team, and his human resources (HR) partners initiated a dazzling number of talent programs designed to attract and retain the best and brightest from the technology talent pool.  

“When I joined The Estée Lauder Companies, the company was pivoting to become much more consumer-driven,” says Smith. “In IT, that meant shifting from our traditional back-office and infrastructure work to focusing on breakthrough innovation, unique in-store experiences, and personalized consumer engagement.”

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