Data Quality: The Real Priority

Addressing data quality is more important than creating sophisticated reports because if the data quality is poor, then no matter how sophisticated the reports are, the insights will be unreliable and inaccurate. This can lead to poor decision-making, incorrect predictions, and ultimately, business failure.

Here are a few reasons why addressing data quality should be prioritized over creating sophisticated reports:

1. Reliable Insights: Reliable insights require high-quality data. Sophisticated reports might look impressive, but they are useless if the underlying data is inaccurate or incomplete. Addressing data quality issues ensures that the data used in the reports is accurate and relevant, increasing the trustworthiness of the insights and allowing businesses to make better decisions.

2. Saves Time & Money: Fixing data quality issues can seem like a time-consuming and expensive task. However, ignoring data quality issues can result in costly errors that require more time and money to fix later on. By addressing data quality issues early on, businesses can save valuable time and resources in the long run, avoiding the need to rework reports and redo analyses repeatedly.

3. Improved Data Governance: Addressing data quality issues is an essential part of managing data effectively. Poor data quality can lead to inconsistent data across systems, making business decision-making even more complex. Addressing data quality issues helps businesses create governance processes that regulate data management, resulting in better data quality, consistency, and reliability.

4. Customer Trust: Addressing data quality issues is essential for building customer trust. If data quality is poor, customers will notice and lose trust in the company's ability to provide accurate information. This can damage the company's reputation and result in lost business. Addressing data quality issues ensures that the data provided to customers is trustworthy, increasing their trust in the company.

In conclusion, addressing data quality issues is more important than creating sophisticated reports. Poor data quality can lead to unreliable insights, costs, and time spent correcting errors, poor data governance, and a loss of customer trust. By focusing on addressing data quality issues, businesses can create a solid foundation for reliable data and insights, leading to better decision-making, reduced costs, and improved customer relations.

The Best Way to Drive Strategy: Data-Driven Decision Making

Strategy is the lifeblood of any organization, and data-driven decision-making is still the best way to ensure that you are making informed choices that will produce the best results. Too often, leadership makes decisions based on gut feelings or incomplete information. This can be disastrous for a business. By leveraging information, seeking insights, and determining the best sources of information, you can make sound decisions that will benefit your organization for years to come.

Any efforts to drive a firm towards data-driven decision-making should include creating a culture that rewards evidence-based arguments, and providing a framework in which each knowledge worker is provided the support they need to obtain the information they need to back their arguments. Leaders should also be encouraged to ask for data when making decisions, and to seek out multiple perspectives before coming to a conclusion.

A culture that rewards an evidence-based approach to decision-making involves leadership and management setting expectations with their teams. Teams should be encouraged to follow a consistent template when putting forth arguments in favor or against a particular initiative. A template might include a list of pros and cons, citations from reliable sources, independently produced objective metrics, or even subjective and anecdotal survey results. Whatever configuration of the template is used matters less than the need to encourage team members to use some form of structured data when making their case.

An operating framework that enables a created culture of data-driven decision-making involves breaking down data silos. All data within an organization, produced, purchased, or in any way acquired is property of the organization, and no function or department should wall such data unless there are legal or regulatory reasons to do so. A firm that allows the free flow of information must not necessarily worry about the quality of data, especially if the data can be classified and tagged so any consumer could make an informed decision about using it. The mere democratization of data, and its availability, become a critical first step. Other steps include organizational support to purchase data subscriptions, reaching out to and leveraging relationships with industry peers, and supporting efforts to survey colleagues.

Data-driven decision-making is not a new concept, but it is often overlooked in favor of other methods. This is a mistake. Data should be at the center of every decision you make, and by using all credible and relevant information available, you can make choices that will produce the best results for your organization. Use data to drive strategy, and you will be on your way to success.

Value-Oriented Development: Ensuring Your Teams Work on What Matters Most

In the world of software development, there are many ways to ensure that your teams are working on what matters most. One popular technique is known as "value-oriented development." This approach focuses on ensuring that each and every investment made by your firm provides the greatest possible reward. In this article, we will discuss how value-oriented development can be used to make sure your teams are working on the right projects, and provide some tips for getting started.

Whenever starting a new project it can be beneficial to identify the outcomes you're hoping to achieve. At first, these outcomes may not be very specific or measurable. However, given some refinement you should be able to produce variations of your desired outcomes that are not only specific, but realistic and measurable. These outcomes may generally include targets that aim to improve performance, increase reliability, reduce cost, mitigate risk, and other ambitious goals. This is where most teams will stop, and will use these objectives and outcomes to determine the opportunity cost for engaging in one activity versus another. This is where a "value-oriented development" approach can help improve that decision making activity for more effective results.

All activity within an organization is in service of the goals and objectives of the firm as a whole. As a result it is imperative that work be done which advances the organization towards those goals. A "value-oriented development" approach helps to ensure that this occurs by identifying and then prioritizing work which will have the greatest impact on meeting those desired outcomes.

In order to practice a value-oriented development approach, there are several steps your team can take:

• Make sure your team is aligned and supportive of the organization's goals. This should go without saying, but too often than not it's a fairly abstract connection. This is a frequently overlooked role of a leader which is to do more than communicate objectives, but build support for them.

• Ensure a clear, and simple link to how the work your team is doing advances the organization's goals. This means a value proposition which is clear and easy to understand. Make it easy for everyone on your team to see how their work connects to these overarching goals. This can sometimes best be accomplished by having a narrative to which people can relate, sometimes from the point of view of a customer or client of the organization.

• Make sure the work your team is doing is directed at improving key performance indicators. This will help to ensure that the value of the work being done is clear, and that it's easy to track progress. Engage in regular review and reflection. This will help to ensure that your team remains focused on the goals which matter most, and allows for course correction when necessary.

Following these steps will help to ensure that your team is focused on the work which provides the greatest value to your organization. By doing so, you can be confident that you are making the most of your team's time and talent, and ensuring that your organization remains successful for years to come.

The Golden Mean: Finding Balance in Extremes

In any decision we make, there are usually two extremes to consider: the ideal option and the pragmatic one. On one side you may find the perfect solution, while on the other is the most practical choice. But it is only in the middle that we find what creates the most value. It can be a challenge to find this balance, but it's an essential part of making sound decisions. Whatever approach you take, it's important to be aware of both ends of the spectrum so you can make the most informed decision possible.

In software engineering or similar disciplines there is a constant balancing act between pragmatism and idealism. This is often manifested in observations one might make around designs being overengineered, too sophisticated for the intended use cases. On the other extreme the designs could be too tactical, too dependent on assumptions and chance, or too focused on delivery timelines than sound engineering. There is a tension here that can never be fully resolved, and it's one that teams constantly have to negotiate, but I've found the best solutions exist in the middle of those extremes. Sometimes the compromise is a matter of finding the midpoint along that distance between the two opposing extremes.

Some of the most effective engineering teams are those composed in such a way that they allow for that natural tension, and when high functioning, can produce the outcome at the midpoint, which quite often is the most balanced overall. This happens practically through design, code, and architectural reviews. It happens during pair programming sessions, or feedback during a demo. It should happen all the time. This is not always easy, mostly because building that kind of team composition can be challenging, and once built it is imperative that the tension be a healthy one, devoid of personal egos and motivated by a common, shared objective.

Finding a balance between any two extremes is never easy, but it's always worth it. It's an essential part of making informed decisions and creating value. The next time you're faced with a choice, take a step back and consider both ends of the spectrum. By doing so, you just might find the perfect solution in the middle.

The Modern Data Warehouse: Simplicity and Speed Over Complexity

It wasn't too long ago that a data warehouse was an expensive, complex affair - housed on-premise and requiring armies of consultants to keep it running. But in the modern world, where companies are moving towards a limited on-premise footprint and faster movements of larger volumes of data, this strategy is no longer viable. To be successful in today's market, you need to adjust your strategy and focus on simplicity and speed. In this blog post, we'll discuss why a modern data warehouse is so important, and how you can make sure yours is up to par.

A data warehouse is a centralized store of information, potentially federated at a data mart level, but with the essential view of democratizing access to all. This means that anyone in the organization can access and use the data, without having to go through IT or other gatekeepers. This is a vast departure from the old model, where only a select few had the knowledge and understanding to navigate an unclear labyrinth of tables, hierarchies, and database objects. These few were the data heroes of old - the wise oracles (pun intended) who could get decision makers what they needed. But in today's world, where users are more sophisticated and demanding, this model is no longer feasible.

To be successful in the modern world, you need to focus on simplicity and speed. This means removing unnecessary hops, keeping transform logic (if it's even needed!) centralized and concise, and making sure that everyone in the organization has access to the data they need when they need it. With the advent of cloud data warehouses, cloud native databases, API focused services, and a variety of other public and private cloud features, we can do better.

ETL and traditional transform oriented tooling and processes need to be reconsidered for the modern data warehouse. Gone are the days when it was always necessary to Extract, Transform, and Load your data. In many cases newer sources have the ability to stream data to targets, and not require transformations due to the performance benefits of auto-scaling warehouses. Maintaining complex, custom stored procedures, or heavily decorated SSIS packages, may serve legacy use cases, but aren't needed in a future forward data warehouse. As technologies and platforms shift in terms of their paradigm, it requires a rethinking of how we approach traditional problems in light of newly viable solutions.

So what does this mean for you and your data warehouse? It means that you need to focus on simplicity and speed. Remove unnecessary hops and keep transform logic to a minimum. Make sure that everyone in the organization has access to the data they need, when they need it. And most importantly, don't be afraid to rethink traditional problems in a new way. With the right approach, you can have a data warehouse that is up to par with the best in the business.

The Importance of a Data Dictionary and Business Glossary: Ensuring Communication Across Segments

A data dictionary and business glossary are two very important tools that should be in place when trying to ensure communication across business segments. A data dictionary is a collection of terms and their definitions, while a business glossary is a collection of terms and their meanings specific to a given industry or company. By having these two tools in place, you can avoid any confusion when trying to communicate with other departments or teams within your company. Let's take a closer look at each of these tools and why they are so important!

A Data Dictionary is a collection of definitions for fields in a data model that can be used to help people navigate the often overwhelming number of data items that might be present in a large database or other data repository. Data dictionaries are an important tool because they provide a common language that everyone can use when talking about data. This is especially important when you have employees who come from different departments or who have different levels of experience with data. By having a data dictionary, you can be sure that everyone is on the same page and using the same terminology.

A Business Glossary is a collection of terms and their definitions that are specific to a particular industry or company. This is important because it allows everyone to be on the same page when talking about industry-specific terms or company-specific acronyms. By having a business glossary, you can avoid any confusion or miscommunication among employees.

The combination of both a Data Dictionary and Business Glossary is extremely powerful. Linking terms from a business glossary to a data dictionary provides a bi-directional mapping of information that can close the gap between technology and the business. In bridging the two there is a reduction in misunderstanding, increase in effective communication, and positive outcomes through more fruitful collaborations.

So, why are data dictionaries and business glossaries so important? Simply put, they provide a common language that everyone can use when talking about data. By having these tools in place, you can avoid any confusion or miscommunication among employees. Do you have a data dictionary or business glossary in place at your company? If not, now might be a good time to start one!