Many businesses are facing challenges with business intelligence and artificial intelligence projects. These challenges can be daunting, but they are not insurmountable. With the right approach, businesses can overcome these challenges and reap the benefits of these powerful technologies.

Start with a curious attitude and an open mindset.

The first step to overcoming intelligence challenges is to be open to new ideas and possibilities. Don’t be afraid to experiment and try new things. The field of intelligence is constantly evolving, so it’s important to be willing to adapt.

Be aware of the latest technologies and trends.

There are a number of new technologies and trends that can be used to improve intelligence projects. For example, cloud computing can make it easier to store and analyze large amounts of data. Machine learning can be used to automate tasks and make predictions.

Don’t be afraid to ask for help.

There are a number of resources available to help businesses overcome intelligence challenges. There are consulting firms that specialize in intelligence projects. There are also online communities where businesses can share ideas and best practices.

Follow a structured development and risk management approach.

It’s important to have a structured development and risk management approach when working on intelligence projects. This will help to ensure that the project is completed on time and within budget. It will also help to mitigate the risks associated with the project.

Be patient and persistent.

Intelligence projects can take time to complete. It’s important to be patient and persistent. Don’t give up if you don’t see results immediately. With time and effort, you will be able to overcome the challenges and achieve your goals.

Here are some specific challenges that businesses face with intelligence projects:

• Dealing with new technologies. It can be difficult to keep up with the latest technologies and trends. Businesses need to make sure that they are using the right technologies for their needs.
• Dealing with flawed data. Even the best data can have flaws. Businesses need to be aware of the limitations of their data and take steps to mitigate the risks associated with flawed data.
• Dealing with too much data. It can be overwhelming to deal with large amounts of data. Businesses need to find ways to make sense of their data and extract the insights that they need.
• Dealing with operational or other types of risk. Intelligence projects can introduce new risks into the business. Businesses need to identify and mitigate these risks.

Here are some tips for overcoming these challenges:

• Start with a clear understanding of your business needs. What do you hope to achieve with your intelligence project? Once you know your goals, you can start to identify the right technologies and solutions.
• Work with a qualified consultant. A consultant can help you to assess your needs and develop a plan to overcome the challenges.
• Use a structured development and risk management approach. This will help you to stay on track and mitigate the risks.
• Be patient and persistent. Intelligence projects can take time to complete. Don’t give up if you don’t see results immediately.

Overcoming intelligence challenges is not easy, but it is possible. By following these tips, businesses can increase their chances of success.

We will be a proactive helper to your and your firm. We will act as a key ingredient in driving solutions and producing results for your organization. We’ll bring years of experience in rigorous data science and it’s practical application to a wide variety of business issues or opportunities. And we’ll show up with an attitude that is both confident and humble as we have seen this process work many times and we have learned that success always requires effective teamwork and collaboration. That’s why we define our firm as an ingredient.

Key principles that guide us:

• Integrity
• Teamwork and collaboration
• Thinking ahead and being proactive
• Confidence and humility
• Customer-driven quality
• Being direct
• Remaining curious

How we seek to operate:

• Setting the team up for success
• Interacting always professionally and respectfully
• Thinking critically and scientifically about the issues
• Facing the facts good or bad and becoming part of the solution
• Being courageous to do the right thing
• Combining data, intuition, and good judgement when making decisions
• Listening to multiple perspectives, being open, and making decisions based on the merit of the idea
• Keeping it as simple as possible (and also not simpler than that)
• Engineering appropriately to each phase (light POCs, robust production solutions)
• Implementing quality assurance appropriate to each deliverable
• Adopting the right process for each project (and preferably an agile one such as scrum or kanban)
• Ensure intelligence is reproducible
• Ensure solution architecture remains solid (design carefully, follow the guidance, minimize tech debt, pay it consistently)

Speed to market is crucial for staying ahead of the competition when developing products or services. As an executive decision maker, having timely and directionally correct intelligence often outweighs perfect data that arrives too late. This need for speed is paramount in both BI and AI domains.

So, how does one achieve speed?Our recommended combination is AGILE DEVELOPMENT + ARCHITECTURE. Agile development enables short-term speed, while architecture determines speed in the medium and long term. These two disciplines are not contradictory; solutions can be designed to leverage both. However, sometimes tensions or tradeoffs arise between short-term and long-term goals, making it vital to implement a development process that balances both concerns with wisdom and experience.

Two common flavors of agile are available: SCRUM and KANBAN. The choice depends on the specific project and context. Nevertheless, the key lies in understanding the underlying principles and adhering to them. Merely hiring a scrum master, setting meeting cadences, implementing CI/CD, and following practices are not foundational principles; they are ways to apply the principles.

I particularly appreciate Marty Cagan’s description of Agile:

1. Tackling risks upfront
2. Collaboratively defining and designing the “product”
3. Focusing on solving problems rather than just implementing features

In the coming weeks and months, we will delve deeper into how to effectively use agile and master other disciplines like architecture. Stay tuned for more insights.

There are multiple angles of quality that we pay attention to:

• Quality of the inputs (source data quality)
• Quality of the deliverables (KPIs, benchmarks, forecasts, models, recommendations, etc.)
• Quality of the development pipeline (change control and testing before release)
• Quality of the value or operations pipeline (detecting changes in data, monitoring models and watching for drift, etc.)

Multiple roles may be involved in ensuring quality:

• The developers (e.g. data scientist, data engineer, ML engineer, data analyst, or other)
• The development team (e.g. peers reviews, manager reviews, etc.)
• The product owner (deserves special mention)
• The user or a representative of the user (e.g. UAT)

I’d like to borrow from an excellent QA methodology created by FedEx named: Quality Driven Management

The key principles in QDM are:

• Quality is defined by the customer
• Be scientific
• Measure measure measure
• Optimize business performance
• Quality involves teamwork
• View failures as opportunities

In the weeks and months to come I will be writing more about how to apply these principles to ensure adequate quality in BI and AI projects while also remaining fast. More to come!

Welcome to Proactive Ingredient!

Let me introduce myself. I am Carlos Miranda Durand, the Founder and CEO of the firm. I have 23+ years of professional and leadership experience in technology including 13+ years of experience in data science. I believe data science is absolutely a team sport and have the privilege to have worked with many talented and brilliant people as my clients or leaders, as my collaborators or partners, and as my team members or gems as I’ve learned to see them. I hope the next team that I collaborate with is yours.

Having a consulting business has been a lifelong dream and I am seeking to build one practice that goes above and beyond. Let me share what’s in the company name. Personally I am high-energy, people have described me as conscientious, and my engineering formation has taught me the importance of diligence, anticipation, curiosity, and continuous search for excellence. My vision is to build a team with diverse set of talents but that no matter what we are doing, we will do it with a proactive attitude.

Also fundamental to any big accomplishment that I have seen in this field is a well-functioning and diverse team: people with their own individual strengths and interests collaborating effectively. The effort of the most brilliant data scientist can be wasted when it’s not informed by the voice of the customer and the business context, or understood and adopted by the stakeholders or professionals that would act on the intelligence, or supported by a safety net that would catch an error (yes, even brilliant data scientists can make mistakes, and that’s why we have multiple validation methods in our tool belt.) Data Scientists and their algorithms can have a huge impact for your business, but they are just one ingredient. Our firm will partner with you and your organization to add this awesome ingredient in a way that produces results.

Call me if you want to explore how business intelligence, artificial intelligence, or data science in general may help your company. Looking forward to working with you!

Carlos

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