Daily Tech Digest - May 22, 2022

6 business risks of shortchanging AI ethics and governance

When enterprises build AI systems that violate users’ privacy, that are biased, or that do harm to society, it changes how their own employees see them. Employees want to work at companies that share their values, says Steve Mills, chief AI ethics officer at Boston Consulting Group. “A high number of employees leave their jobs over ethical concerns,” he says. “If you want to attract technical talent, you have to worry about how you’re going to address these issues.” According to a survey released by Gartner earlier this year, employee attitudes toward work have changed since the start of the pandemic. Nearly two-thirds have rethought the place that work should have in their life, and more than half said that the pandemic has made them question the purpose of their day job and made them want to contribute more to society. And, last fall, a study by Blue Beyond Consulting and Future Workplace demonstrated the importance of values. According to the survey, 52% of workers would quit their job — and only 1 in 4 would accept one — if company values were not consistent with their values. 

The Never-Ending To-Do List of the DBA

Dealing with performance problems is usually the biggest post-implementation nightmare faced by DBAs. As such, the DBA must be able to proactively monitor the database environment and to make changes to data structures, SQL, application logic, and the DBMS subsystem itself in order to optimize performance. ... Applications and data are more and more required to be up and available 24 hours a day, seven days a week. Globalization and e-business are driving many organizations to implement no-downtime, around-the-clock systems. To manage in such an environment, the DBA must ensure data availability using non-disruptive administration tactics. ... Data, once stored in a database, is not static. The data may need to move from one database to another, from the DBMS into an external data set, or from the transaction processing system into the data warehouse. The DBA is responsible for efficiently and accurately moving data from place to place as dictated by organizational needs. ... The DBA must implement an appropriate database backup and recovery strategy for each database file based on data volatility and application availability requirements. 

The brave, new world of work

The recent disruptions to the physical workplace have highlighted the importance of the human connections that people make on the job. In an excerpt from her new book, Redesigning Work, Lynda Gratton of the London Business School plays off an insight made nearly 50 years ago by sociologist Mark Granovetter. Granovetter famously discussed the difference between “weak” and “strong” social ties and showed that, when it came to finding jobs, weak ties (the loose acquaintances with whom you might occasionally exchange an email but don’t know well) could actually be quite powerful. Gratton applies this thinking to the way that networks are formed on the job, and to how people organize to get their work done, get new information, and innovate. She concludes that, especially in an age of remote and hybrid work, companies have to redouble their efforts to ensure that employees are able to establish and mine the power of weak ties. For Gratton, the ability to create such connections is a must-have. ... Now more than ever, people have to engage in the often challenging task of drawing boundaries. 

Most-wanted soft skills for IT pros: CIOs share their recruiting tips

Today’s IT organizations are called upon to drive and deliver significant transformation as technology seeps into all corners of a company and its products and services. With that, new and refined skills are necessary for successful technology leaders to influence business outcomes, innovation, and product development. Empathy, managing ambiguity, and collaborative influence drive innovation and are attributes we look for at MetaBank as we hire and develop top talent. Empathy lies at the core of successful problem-solving – viewing a problem from various angles leads to better solutions. ... Leaders often face challenging circumstances where they must quickly make a tough call with insufficient information. Making good choices in these situations can be critical for an organization’s success. It isn’t always easy to assess this in an interview, but behavioral interview questions and careful follow-up can help elicit specific examples from a candidate’s past work experience that may shed light on their judgment.

6 key steps to develop a data governance strategy

Much of the daily work of data governance occurs close to the data itself. The tasks that emerge from the governance strategy will often be in the hands of engineers, developers and administrators. But in too many organizations, these roles operate in silos separated by departmental or technical boundaries. To develop and apply a governance strategy that can consistently work across boundaries, some top-down influence is required. ... Horror stories of fines for breaching the EU's GDPR law on data privacy and protection might keep business leaders awake at night. This drastic approach may generate some interoffice memos or even unlock some budgetary constraints, but that would be a defensive reaction and possibly create resentment among stakeholders, which is no way to secure long-term good data governance. Instead, try this incremental approach, which should be much more attractive to executives: "Data governance is something we already do, but it's largely informal and we need to put some process around it. In doing so, we will meet regulatory demands, but we will also be a more functional, resilient organization."

8 Master Data Management Best Practices

When software development began embracing agile methodologies, its value to the business skyrocketed. That’s why we believe a MDM best practice is to embrace DataOps. hen software development began embracing agile methodologies, its value to the business skyrocketed. That’s why we believe a MDM best practice is to embrace DataOps. DataOps acknowledges the interconnected nature of data engineering, data integration, data quality, and data security/privacy. It aims to help organizations rapidly deliver data that not only accelerates analytics but also enables analytics that were previously deemed impossible. DataOps provides a myriad of benefits ranging from “faster cycle times” to “fewer defects and errors” to “happier customers.” (source) By adopting DataOps, your organization will have in place the practices, processes, and technologies needed to accelerate the delivery of analytics. You’ll bring rigor to the development and management of data pipelines. And you’ll enable CI/CD across your data ecosystem.

5 tips for building your innovation ecosystem

A common mistake when looking for innovative technology vendors is to look at companies touted as the most innovative or to go with best-of-breed, on the assumption that innovation is baked into their roadmap. It’s likely that neither approach will net you the innovation you’re looking for. Best-of-breed works well for internal IT such as your ERP or CRM, or anything under the covers in terms of client-facing solutions, but when it comes to your value proposition and differentiation you need to look elsewhere. In this case, the best-of-breed tools become the table stakes that you utilize as the foundation for your ecosystem or industry-cloud and your core IP comprises your own IP plus that of those innovative players that you’ve developed unique relationships with. The “most innovative” lists you find on the internet are often based on public or editor opinion and end up surfacing the usual suspects with strong brand awareness. While they may be leading players in the market, this does not guarantee continued innovation. If you do look at the “most innovative” lists, be sure to check the methodology involved and see how it fits your own definition and expectations for what constitutes innovation.

Zen and the Art of Data Maintenance: All Data is Suffering (Part 1)

Data can be used for many types of nefarious activities. For instance, an article in Wired described how a website stored video data regarding child sex abuse acts and how they used this data in threatening, destructive ways leading to all sorts of suffering including suicide attempts.[i] We are often bombarded with social media data (both factual and misinformation) that are designed to hold our attention through emotional disturbances such as fear. These are generally intended to elicit reactions or control behavior regarding many matters including purchasing, voting, mindshare, or almost any other matter. Have you suffered with data? How? Data is the plural form of the Latin word, ‘datum’, which Merriam Webster defines as ‘something given or admitted as a basis for reasoning or inference’. Thus, everything we receive through our senses could be considered data. It could be numbers, text, things we see, hear, or feel. But how could all data be suffering? What about positive data that communicates increased sales, better health, positive comments, data showing helpful contributions, and so on? 

The Metamorphosis of Data Governance: What it Means Today

There’s nothing more galvanizing to an organization’s board of directors—or the C-Level executives who directly answer to it—than stiff monetary penalties for noncompliance to regulations. Zoom reached a settlement for almost $100 million dollars for such issues. Even before this particular example, data governance was inexorably advancing to its current conception as a means of facilitating access control, data privacy, and security. “These are big ticket fines that are coming up,” Ganesan remarked. “Boards are saying we need to have guardrails around our data. Now, what has changed in the last few years is that part of governance, which is security and privacy, is going from being passive to more active.” Such activation not only entails what data governance focuses on, but also what the specific policies it’s comprised of focus on, too. The regulatory, risk mitigation side of data governance is currently being emphasized. It’s no longer adequate to have guidelines or even rules about how data are accessed on paper—top solutions in this space can propel those policies into source systems to ensure adherence when properly implemented. 

Five Steps Every Enterprise Architect Should Take for Better Presentation

Architects invariably care about the material they’re discussing. The mistake is believing or assuming that the audience cares as intently. They may. They may already be familiar with the content. This may simply be a status update on the latest digital transformation project and everyone is knowledgeable about the subject matter. ... Generally speaking, the audience isn’t going to automatically care as much about the material as does the Architect presenting. The key to this step is usually the hardest of all the points made in this article. The key is empathy. Thinking what you would do or what you would be interested in if you were the listener is not empathy. That’s simply you projecting your own headspace onto the audience. Trying to understand how that person is receiving your information is the key. Why do they care, what aspects will they be interested in. To do this requires knowing in advance who you will be speaking to and knowing their background, their education, their professional position, their issues or problems with the subject at hand… knowing, in effect, through what lens they will be viewing your content.

Quote for the day:

"Leadership should be born out of the understanding of the needs of those who would be affected by it." -- Marian Anderson

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