The Clearly Podcast

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The podcast discusses the importance of understanding client and user motivations in IT projects. The consultants talk about how motivations can significantly impact project approaches and outcomes. They highlight common motivations such as reducing costs, unlocking new opportunities from data, and less valid motivations like individuals furthering their own careers. The latter can lead to political challenges and affect project success.

Effective communication and understanding internal politics are crucial. It's essential to ask clients what success looks like and how it will be measured. Clear internal communication helps avoid defensive assumptions and keeps projects on track. The consultants also emphasize the importance of choosing the right technology for the project, rather than being swayed by trends or client demands for specific tools like Power BI.

They share experiences of dealing with internal politics, including situations where project sponsors may not fully support the chosen solutions, leading to blockers and delays. The discussion underscores the need for consultants to navigate these dynamics carefully, ensuring they guide clients towards the best solutions while maintaining project momentum and addressing any internal resistance.

In conclusion, the consultants stress the importance of having open conversations, positioning themselves as helpers, and being prepared to pivot if necessary. Understanding and addressing client and individual motivations, while maintaining clear communication and a compelling vision, are key to successful project delivery. Next week's podcast will focus on "A Day in the Life."

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In this podcast, the hosts discuss the importance of matching implementation methodology to client culture. They reflect on the prevalence of agile methodology and its suitability for different types of organizations, such as government bodies, high-growth companies, and traditional commercial organizations.

They note that while many organizations claim to practice agile, they often revert to traditional methods like waterfall, especially in larger programs where full agile adoption is challenging. They emphasize the need to course-correct when agile isn't working and suggest that sometimes, a waterfall approach might be more suitable.

The discussion highlights the flexibility within agile, such as shifting from a sprint-based approach to a Kanban style when needed. They stress the importance of having early and honest conversations with clients about the best methodology for their needs.

Additionally, the hosts touch on the concept of "wagile," a hybrid of agile and waterfall, noting its potential benefits and risks. They conclude by recommending regular reviews of the implementation methodology and ensuring alignment between the project's objectives and the chosen methodology.

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Welcome to the podcast! Today’s episode focuses on the hosts' origin stories in the BI and analytics field. They reminisce about their early careers, starting with one host's experience at Navision, which was acquired by Microsoft in 2002. Another host joined Microsoft in 2004, where they bonded over a trip to Fargo, ND, and other shared experiences. A third host began with Access databases and later moved to SQL Server and Crystal Reports with Birmingham Council.

The hosts discussed their roles at Microsoft, emphasizing the big company discipline and how it shaped their careers. They explained how their Microsoft experience helped them in their current businesses, particularly in understanding competencies and building business cases.

Reconnecting randomly on a train, two hosts started working together again and formed Clearly Solutions in the US, focusing on Power BI and the Power Platform. They used the pandemic to improve their skills in these areas, which proved beneficial as cloud adoption accelerated.

They also touched on the future of jobs in their field with AI and automation, agreeing that while AI might automate some tasks, skilled professionals will still be needed to manage and implement these technologies. The episode concluded with a preview of the next topic: client motivations and their impact on projects.

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This week's podcast focused on the long-term costs and maintenance of analytics platforms, comparing cloud and on-premises solutions. The discussion highlighted key differences in cost management, emphasizing that cloud solutions offer greater flexibility in scaling up or down based on current needs, unlike on-premises infrastructure which requires upfront investment for future capacity.

The conversation also touched on the total cost of ownership (TCO), pointing out that while cloud services might seem to have recurring costs, they often result in overall savings due to reduced staffing and maintenance expenses. Cloud environments allow for remote management, reducing the need for physical access and related consultancy costs. Additionally, cloud providers handle frequent updates and security measures, which can be more cost-effective and secure compared to on-premises solutions that require dedicated personnel and manual upgrades.

Key takeaways include the importance of considering the ability to scale with cloud solutions, the benefits of a hybrid approach, and the need for proper resource planning. The discussion also noted that despite the recurring costs, cloud solutions often lead to savings through reduced staffing needs and better security management. Finally, the importance of understanding the skill mix required for supporting different infrastructures was emphasized, as personnel costs can be a significant factor in TCO.

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The discussion highlights the importance of data quality and the frequent issues surrounding it. Often, data quality is poor due to user laziness or lack of consequences for incomplete data entry. The conversation emphasizes that data quality should be prioritized because it saves time and effort in the long run. Regular monitoring and addressing issues at the source are crucial.

Incomplete data can lead to significant problems, such as invoice reconciliation challenges and over-engineered applications that frustrate users. A balance is needed between necessary data collection and ease of use. Training on system use and the impact of poor data, as well as allowing time for data correction, can drive better behavior.

Assigning data ownership can help improve quality, but it depends on the individual's commitment. Organizations need a culture that values data quality, with visibility on how data is used and its impact on decision-making and reporting.

Poor data quality can cost money and lead to inefficiencies. Data migration often reveals underlying issues, and treating data quality as an ongoing practice is essential. Some companies take data quality seriously by not hiding issues in reporting, which drives better behavior. However, achieving perfect data quality is rare.

The key takeaway is to identify and enforce critical data points at the front end without over-engineering. Understanding the importance of clean data and its impact can drive organizational change.

Next week's discussion will focus on the long-term maintenance of data in the platform and the long-term cost of ownership.

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The podcast discusses the importance of the four levels of data analytics: descriptive, diagnostic, predictive, and prescriptive. The hosts emphasize that descriptive analytics, often considered basic, is fundamental because it answers "What happened?" without which further analysis is impossible. Diagnostic analytics answers "Why did it happen?", predictive analytics answers "What will happen?", and prescriptive analytics addresses "How can we make a certain outcome occur?"

They stress that the foundational elements like data quality, data architecture, and data governance are crucial for successful analytics. Many organizations overlook these aspects, leading to masked issues and unreliable data. There's a need to correct data at the source and ensure ongoing governance to maintain data quality.

The discussion highlights that most companies think they are more advanced in their analytics capabilities than they are. It's important to recognize where they truly stand and understand that achieving higher levels of analytics requires a solid foundation in descriptive analytics. The hosts also discuss the cultural aspects of data governance and the need for a process-oriented approach to maintaining data quality. The episode concludes with a reminder that good governance and a solid data foundation are essential for reliable and advanced analytics.

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The podcast discusses the concept of being seduced by technology and its impact on projects, client interactions, and consultants. The conversation begins with light-hearted remarks about a recent haircut before diving into the main topic.

The first example given is an OLAP Cube implementation from 15 years ago that failed because it didn't address a real business need. This leads to a discussion on how businesses and consultants alike are often drawn to new technologies like machine learning and artificial intelligence, even when they don't solve actual business problems.

The speakers highlight that the desire for new technology is driven by various motivations: businesses wanting to boast about their cutting-edge tech, consultants seeking to enhance their CVs, and IT professionals looking to upskill. They also touch on the hope that new technology will magically solve business issues, often spurred by vendor promises.

The conversation then shifts to the importance of addressing foundational issues such as data quality and governance before implementing advanced technologies. They emphasize learning from failed projects to improve future implementations and stress the need for honest conversations with clients about the limitations and prerequisites of new technologies.

The speakers agree that foundational work can run parallel to more glamorous projects, and it's crucial to set realistic expectations and prioritize essential groundwork for long-term success. They conclude by agreeing to discuss BI foundational levels in the next episode.

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In this podcast, the hosts discuss the concept of best practices in IT consulting and why they often need to be debunked. They highlight that clients frequently seek a definitive list of best practices, expecting these to be universal guidelines for tasks such as building databases or setting up environments. However, the hosts argue that best practices are highly situational and can be constraining if followed blindly.

The term "best practice" is considered misleading, as what is deemed best practice can change over time and is context-dependent. Clients often find comfort in the idea of best practices, seeing them as a way to de-risk their projects. However, the hosts emphasize that blindly following supposed best practices can actually increase risks rather than mitigate them.

They discuss how to handle client inquiries about best practices by explaining that there isn't a one-size-fits-all solution. Instead, they focus on scenario planning and providing preferred options based on the specific needs of the client. Transparency about the options and trade-offs is crucial for building trust and achieving better outcomes.

The conversation also touches on the allure of new technologies and features still in preview. While these can be tempting, it’s important to balance the excitement with the practicalities of using untested features and understanding the associated risks.

In conclusion, the hosts agree on the importance of having mature, informed discussions with clients to tailor solutions to their specific needs, rather than following generic best practices. They also decide to plan their podcast topics in advance for better organization and end on a light note, humorously assuring listeners that they are fully clothed.

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This season, we're aligning our podcast with the structure of our upcoming book, "BI Confidential." Our goal is to create material that will directly contribute to the book, addressing real-world practices in BI and analytics.

We'll start by debunking the myth of "best practice." Best practice can often lead to paralysis, as people get stuck on whether something is standard rather than considering if it fits their specific needs. Instead, we should focus on good practices that work for our specific contexts, even if they aren't the so-called best.

Another important topic will be the journey from descriptive to prescriptive analytics. Basics are crucial, but they can take a lot of effort to get right. Once the basics are in place, advanced analytics often follow more easily.

Understanding client types and motivations is key. Different organizations—whether they're high-growth and chaotic or structured and regimented—require different approaches. We must adapt our methods to fit the client's specific needs.

We'll also discuss the importance of equipping your toolbox with the right tools. No single tool can do everything, so it's important to have a variety and know when to use each one. This includes tools like Excel, which remains a staple.

Selecting the right partner is another crucial element. Understanding client motivation and organizational context helps tailor our approach to better meet their needs.

Other topics will include long-term maintenance, data quality, governance, cloud infrastructure costs, licensing, and subscription models. Ongoing costs are often overlooked but can significantly impact the overall success and sustainability of BI projects.

This season aims to provide practical, real-world insights and strategies for effective BI and analytics, blending our podcast discussions with the structure needed for our book. Stay tuned for a series of engaging and informative episodes!

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In the last of our Machine Learning Trilogy and final episode of Season Two, we talk about applying ML in the context of Power BI. We once again try to trick the algorithm with mentions of Excel, and end with a summary of our thoughts on Machine Learning.

Artificial Intelligence in a more general sense is also the topic of this year's Reith Lectures.  Just to prove how important and topical The Clearly Podcast really is.

If you already use Power BI, or are considering it, we strongly recommend you join your local Power BI user group here.

To find out more about our services and the help we can offer, contact us at one of the websites below:
UK and Europe: https://www.clearlycloudy.co.uk/
North America: https://www.clearlysolutions.net/