12 Qualities Your Next Chief Data Officer Should Have (continued)

12 Qualities Your Next Chief Data Officer Should Have Infographic

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7. Be Technology Savvy and Understand the Latest Trends

A CDO deals with both the IT as the business aspects of Big Data and should, therefore, understand the concepts of data mining, artificial intelligence, machine learning, data modeling and data governance. Not only from the business perspective, but also from the IT perspective. In addition, the world of big data changes rapidly and new big data trends constantly emerge; he or she should, therefore, remain up-to-date with the latest trends.

8. Be Open to Input from Others

The CDO should not be the only one calling the shots. Of course, when a decision needs to be made on important, company-wide projects, the CDO should, together with the rest of the board, make the decision. However, a CDO should also empower his/her employees to make their own decisions within their projects. The Chief Data Officer should give control to his or her staff and should avoid micro-managing them.

9. Be Customer-Driven

When dealing with Big Data, organizations should take the customer into account in everything they do. This human-centered approach is vital. When projects are developed based on the actual needs of the customer, the chances of success are a lot higher. In addition, when developing big data projects, the CDO should ensure that the customer does not become the victim in terms of lost privacy and should ensure that the customer’s privacy is protected.

10. Be Security Driven

My vision is that all organizations will be hacked and if you are not being hacked, you are simply not important enough as an organization. Therefore, a CDO should have a strong security focus. As a CDO, the starting point should be that you will be hacked and if that’s the case, how do you prevent that hackers get access to any personal data or important company data or systems. A CDO should enforce the right security processes, implement the right encryption measures and use right IT Operations Analytics tools to detect attacks in real-time.

11. Be a Visionary Leader

A CDO needs to have a vision where he/she wants to bring the company to in the next 5-10 years. As mentioned Big Data projects take a lot of time to be completed and a strong and clear vision will help to complete the projects in the right way.

12. Be a Change Manager

Big data requires a culture change within the organization. Moving to a data-driven and information-centric culture is difficult as people have a natural inertia to change. Therefore, the Chief Data Officer should be a strong change manager, who is capable of changing people’s behavior within the company.

*This article was featured in our Leadership Newsletter series. To sign up for this free publication, click here.

Review: Small Data By Martin Lindstrom

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The focus on big data — the aggregation and analysis of a seemingly bottomless pool of data on what we buy, what we watch, what websites we navigate and even whom we talk to on social media — is nearing “craze” proportions. Into the fray steps marketing iconoclast Martin Lindstrom, who argues that businesses need to put the databases and algorithms aside for a bit and focus instead on a different kind of data — data about the kind of magnets people have on their refrigerators, for example, or why single young men really buy Roombas (vacuum cleaning robots), or why store clerks began wearing T-shirts with Apple logos even though the store was not an Apple store.

These are all examples of what Lindstrom calls “small data,” and are taken from some of Lindstrom’s actual client projects as described in his fascinating new book, Small Data. As a branding consultant, Lindstrom spends 300 days a year traveling to people’s homes and workplaces to better understand who they are, why they do what they do and how this information — this “small data” — can help his clients serve them better. Lindstrom doesn’t just talk to his customers. He goes into their kitchens and their bedrooms, he looks through their drawers and purses (with permission), he examines the art they have on their walls — all in the hunt for the breakthrough clues that will lead to better products and services or more successful stores.

Click here to read this review in full.

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Modern Day Gold Rush

The discovery of gold nuggets in the Sacramento Valley in early 1848 sparked the Gold Rush, arguably one of the most significant events to shape American history during the first half of the 19th century. As news spread of the discovery, thousands of prospective gold miners traveled by sea or over land to San Francisco and the surrounding area; by the end of 1849, the non-native population of the California territory was some 100,000 (compared with the pre-1848 figure of less than 1,000). A total of $2 billion worth of precious metal was extracted from the area during the Gold Rush, which peaked in 1852.

To accommodate the needs of the ’49ers, gold mining towns had sprung up all over the region, complete with shops, saloons, brothels and other businesses seeking to make their own Gold Rush fortune. The overcrowded chaos of the mining camps and towns grew ever more lawless, including rampant banditry, gambling, prostitution and violence. San Francisco, for its part, developed a bustling economy and became the central metropolis of the new frontier.

Fast forward to the 21st Century and a new gold rush is taking place. As Charles Morgan, author of Matters of Life and Data, puts it: “Data mining is the new gold rush, and we were there at first strike, dragging with us all our human frailties and foibles. In this book’s cast of characters you’ll find ambition, arrogance, jealousy, pride, fear, recklessness, anger, lust, viciousness, greed, revenge, betrayal, and then some.”

Morgan, the Founder, Chairman & CEO of Acxiom Corporation (NASDAQ: ACXM), world leader in data gathering and its accompanying technology, grew Acxiom from an early-stage company to an international corporation growing to $1.4 billion in annual revenue during his tenure as CEO from 1972 to 2008.

If you’d like to hear the inside story of this 21st century gold rush from one of the key players, join us on June 30th for our Soundview Live webinar with Charles Morgan titled The Remarkable Story of a Big Data Visionary. You’ll get the inside scoop of the good and bad players in the data industry, as well as learning about how this data gold rush is affecting our business and personal life.

 

The Revolution Transforming Decision Making, Consumer Behavior and Almost Everything Else

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In a January 2015 New York Times Review of Books essay, critic and magazine editor Leon Wieseltier warned against a post-humanist — after the human — culture in which technological devices and data replace human beings and thought. “Quantification is the most overwhelming influence upon the contemporary American understanding of, well, everything,” he writes. “It is enabled by the idolatry of data, which has itself been enabled by the almost unimaginable data- generating capabilities of the new technology.”

In short, “Where wisdom once was, quantification will now be.” One might assume that Wieseltier does not have a copy of Data-ism, a new book from New York Times technology journalist Steve Lohr, on his bedside table. At first glance, Data-ism seems to be the embodiment of Wieseltier’s fear that quantification has replaced wisdom. The “ism” title seems to promise an introduction (manifesto?) to the philosophy of quantification. The subtitle is not timid: “The revolution transforming decision making, consumer behavior and almost everything else.” And within its pages, Lohr does a masterful job of describing all of the possibilities of “big data.”

Data-ism is perhaps one of the most balanced, levelheaded examinations of the potential of big data. Lohr never hesitates to give voice to the critics or skeptics of a data-driven world, nor fails to point out the limitations of artificial intelligence. It is this balance and restraint, however, that makes Lohr and his book the most persuasive champions of the massive and generally positive changes that “the virtuous cycle of more and more varied data and smarter and smarter algorithms, written by human programmers” will make in our lives. In short, quantification will not replace wisdom, as Wieseltier fears; but, Lohr shows, it will augment our wisdom — working with our amazing human brains — to help us make better decisions, free our time and energy to focus on the tasks where we can make the most difference, and, ultimately, make the world a much better place.

 

Know Your Talent Better Than You Know Your Customers

THE DECODED COMPANY

Using Big Data in Human Resources

What if companies knew as much about their employees as they knew about their customers? That is the provocative question at the heart of The Decoded Company — a book written by a group of entrepreneurs connected to a technology-driven health care marketing agency called Klick Health. Klick Health CEO Leerom Segal and his co-authors are great believers in the potential of big data — the myriad of information that is quietly and continuously collected from you as you go about your business as a consumer. Surprisingly, while companies have near-unanimously embraced the use of big data technology for their customers, very few attempt to find out more about their employees.

Three Principles

Using their own experiences as leaders of a fast-growing technology company, the authors describe in their book three fundamental principles for decoding your organization — that is, truly understanding in real time the individual skills, motivations and successes of employees, recognizing the challenges they face, and supporting each individual or groups of individuals as needed.

  • Principle 1: Technology as a Coach and a Trainer. According to the authors, most organizations use technology as a referee rather than as a coach. Technology allows companies to monitor what employees are doing and to whistle the fouls when they fall behind or fail. In decoded companies, technology is a      trainer and coach — preparing employees for the game (to continue the metaphor), then watching from the sidelines and jumping in to coach as      needed. One coaching idea proposed by the authors is the hiring of a “concierge” — someone who might use some of the traditional HR tools, such as career counseling or performance reviews, but whose one and only goal is to design a customized solution for each employee that helps them perform and grow. Technology as a trainer, the authors explain, means using “data and systems to watch blind spots, identify teachable moments, and proactively intervene with just-in-time training.”

 

  • Principle 2: Informed Intuition. The second principle is that technology does not replace but rather augments the intuition of leaders born from their      experience and knowledge, thus allowing them to make better decisions. The      capture of ambient data — ongoing information about what employees are doing or saying — is vital. (One example of the creation of ambient data is your Facebook activity. Facebook tracks with whom you communicate on their site, how often, from where and through which method, such as posting or chat message. This ambient data determines which Facebook friends end up on your newsfeed.) After analyzing a combination of ambient data and selected self-reported data, such as performance self-evaluations or monthly results, managers in decoded companies use their intuition to seek solutions to employee challenges. Bank of America discovered that the performance of call-center employees improved based on whom they talked to  during overlapping lunches. The bank thus decided to create more opportunities for employee conversations by changing a policy that had restricted overlapping breaks.

 

  • Principle 3: Engineered Ecosystems. The third principle is to use data to set up the culture and the environment that enables employees to work at their highest levels. Engineered ecosystems are both data-driven and talent-centric. For example, the authors describe how Google — which, as the company that tested 41 shades of blue for one of its toolbars, is notoriously data-driven — launched a major initiative to identify the most important traits for its managers. The results seemed at first less than earth-shattering: The eight identified traits included not micromanaging, expressing interest in employees’ success, having a vision and a strategy, and having the technical skills to advise the team. The data, however, not only identified the traits but also ranked their importance, and this is where Google’s leaders uncovered a truth about its culture that was contrary to everything they believed: technical expertise, once thought to be the keystone of a great Google manager, is the least important trait that a manager can have. Everything else comes first.

While Segal and his co-authors use Google and numerous other companies in a variety of industries as examples, it is their own success at Klick Healthcare that make The Decoded Company an authoritative, balanced and real-world exploration of the human resources potential of big data.