In the world of data-driven decision making, we often fall into a trap that I call the “measurement paradox.” It’s a sneaky cognitive bias that leads us to focus on what’s easily measurable, rather than what’s truly important. Let me illustrate with a simple example.
Read MoreThe Fragmentation of Psychological Science
The phenomenon of repackaging old ideas, as seen across different eras in psychology (but exists in every discipline), raises questions about the practices of pop psychologists like (dare I suggest) Brené Brown, who often employs grounded theory—a methodology typically reserved for less understood phenomena—to restate established concepts under the guise of novelty. For example, Brown's claim of inventing the idea that individuals adapt their identities within group settings restates theories already well-explored by Smith and Berg in the late '90s, illustrating a tendency among some in the field to "invent" rather than build on existing knowledge. This practice not only misrepresents the originality of an idea but also contributes to the unnecessary fragmentation of psychological science. We must become more discerning. https://lnkd.in/g3BwXRAF
The excessive creation of new constructs and measures leads to fragmentation, complicating the generation of insights and creating barriers to knowledge transfer. This fragmentation makes it difficult to compare results across experiments, limiting the development of a cohesive understanding of phenomena and impeding progress.
To address the challenges of fragmentation, there needs to be a shift toward greater hashtag#methodologicalrigor. We must improve, reuse, and validate existing constructs and hashtag#measures rather than continuously inventing new ones. This approach would make applying insights effectively in hashtag#decisionmaking processes easier and foster a more standardized and accessible body of knowledge.
🎯THERE IS A BETTER WAY
Improve methodological rigor and data quality processes. Apply operational excellence. Leverage data quality and senior stakeholder management to streamline data transformation processes. For example, by identifying and eliminating redundancies in data collection and processing, data-driven decisions' timeliness significantly improves and becomes more accurate.
Key Performance Indicators (KPIs) include efficiency, productivity, and quality outcomes. Focusing on DQ and meta data management and developing or refining KPIs that accurately track the performance of data transformation processes can ensure they align with organizational goals for operational excellence.
🎯WHY PRIOTIZE DQ KPIs?
They ensure accurate, reliable metrics for decision-making. High-quality data underpins effective KPIs, enabling organizations to track performance accurately and make informed strategic decisions, which is essential for operational excellence.
🎯WHY DOES STAKEHOLDER MANAGEMENT MATTER?
It nurtures beneficial relationships between a business and its stakeholders, creating shared value. It helps avoid or resolve conflicts, secure support, communicate effectively, and manage expectations, which is essential for operational excellence.
4 Perspectives to drive effective data translation
When driving data projects, you will encounter business stakeholder challenges that often go unspoken. This is not always because people hold back but because they don't fully know how to vocalize their constraints.
If they can't directly address their requirement, chances are we can't either. To hear others' speech, we start by asking questions from different perspectives.
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What does it take to become data-driven?
Since most people don’t know a lot about IT organizations or data teams it’s important to understand why moving from ad hoc efforts to a mature approach to driving data projects makes sense. The timing might not be right (now). Becoming data-driven through data as a service requires a serious investment of resources, finances, staff, equipment, services, etc.; scaling efforts will only increase those topline demands. It’s a serious ongoing commitment many organizations find themselves surprised by—even today.
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Back to Basics: The Benefits of Data Projects
Data teams should be regarded as intentional business partners because they provide the underlying technology that enables business strategy and maintains data as a corporate asset. They can help educate business partners on the upstream and downstream impacts of poor data quality, and they can help cultivate more effective ambassadors for data governance across the organization.
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Finding Meaning in Data Projects by Asking: Why
Most data teams cover WHAT and HOW with standard reports and KPIs. They will optimize processes and analyze business domains impacting the company's bottom line from a data perspective.
But how many data teams truly understand the WHY behind the reports they generate? How many actively consult with the business as a true partner to understand the underlying business concerns behind the numbers? Without the WHY, delivering true value in the WHAT and HOW is ten times harder.
Data consumers must be mechanics and pilots: 5 takeaways from the guide
With tell-me features and AI Tools, we forget the importance of and lose ambidexterity skills like managing details and thinking strategically. Additionally, the emphasis used to be on being able to talk to the box and not the people; now, we must reason with the box (and the people). Skills cultivating engaged stakeholders and executive sponsors weren’t emphasized as much but are now increasingly important. Those are radically different skills!
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Dos and Dont's for Data Analysts Relying on ChatGBT
Data analytics is filled with complexity. Anyone saying otherwise is selling products. Knowing the data sources, data sets, general lineage, and behavior of the numbers are table stakes for the average data consumer. We must know where our data comes from. Much like we need to know where our food comes from and how it's processed. Is it safe to consume?
Lately, I’ve heard many stories about early career folks with data analyst titles turning to ChatGBT for help because they don't know where to go with questions. ChatGBT should only be used when the output can be rigorously challenged, which can only happen if you have the foundational knowledge of how the output was generated. Here are some handy Do’s and Don’ts to remember before turning to ChatGBT.
BOOK ANNOUNCEMENT, OPPORTUNITY TO PRE-ORDER!
After teaching informatics for seven years, I’ve got a new book coming out! Driving Data Projects.
It’s a love letter to my students and a guide to my fellow colleagues. Many employees seek out or are thrust into a series of responsibilities in data management for which there is little formal training. How they engage with data in those roles impacts the privacy and security of consumer data and overall risk to the company's bottom line. The problem?
They aren’t quite sure how data works or how to drive data projects, not really. Today, almost all projects involve data to some degree, yet the data aspect is not adequately addressed.