When most people envision the future, they imagine things like flying cars, sleek, techno-enabled cities, and clothing that doesn’t use zippers or buttons (for some reason). However, we’re currently living in the sci-fi future that previous generations could only dream of – all thanks to the growing prevalence of AI.

Fortunately, artificial intelligence isn’t the demonic bogeyman that pop culture has made it out to be. No sinister robots, no global network trying to kill all humans. Instead, machine learning has enabled businesses and other organizations to improve the customer experience. In some cases, these advancements have happened so seamlessly that it’s hard to imagine how we functioned before AI.

So, with that in mind, we want to take a look at how artificial intelligence is disrupting the user experience – both today and into the future.

#1 Improved Personalization

Modern consumers are much savvier than those from previous decades and generations. Because customers have so much information at their fingertips (i.e., product reviews and competitor pricing), they want authenticity more than anything. Not only does this mean transparency from the business, but it also includes customization. Niche marketing is much more productive these days because your message can resonate better with smaller groups.

AI is leading the charge in customer personalization, thanks to a variety of advancements and techniques. One of the most valuable has been predictive marketing. As companies gather data about shoppers and their habits, they can craft unique ads and promotions based on behavior.

Best of all, because customers see that their interests and values are addressed, they are much more likely to develop a stronger bond with a brand. Consumer loyalty is at an all-time high, thanks largely to AI.

#2 Better Customer Response

Gone are the days of calling a business and waiting on hold for hours on end. While this shift has made it harder to write sitcom episodes, consumers everywhere are breathing a sigh of relief. Artificial intelligence enables brands to respond to customer queries and complaints, thanks to tools and systems like:

  • Self-Service – Shoppers who don’t want to deal with a pushy salesperson can navigate menus and options much faster with a virtual assistant. Advanced AI can also learn to respond to various questions and provide answers.
  • Multi-Channel Access – when a business can use machines to respond to customers, it’s easier to “staff” things like instant chat, text, and email. Consumers can reach out through various channels and get an answer, which creates a better experience overall. Better yet, virtual assistants never have to go to sleep, so a business can always have “someone” online and ready to chat.
  • Programmed Positivity – even the best call center employee can get annoyed and frustrated at times. AI, however, cannot. Brands never have to worry about an irate customer creating an issue or demanding a refund.

#3 Seamless Integration

Although smart homes are not the norm now, they will be in the future. The Internet of Things (IoT) enables consumers to connect a wide array of products and systems within their homes for maximum convenience. Assistants like Alexa or Google Home can control everything from lights to door locks to the ambient temperature.

Beyond smart homes, AI assistants are also being utilized by other businesses. For example, customers can ask Alexa to schedule a bank transfer or order products online. In the future, almost everything can be run through these systems, allowing users to have a fully immersive AI experience. Rather than doing things themselves, they let machines take care of the details.

A side note on IoT integration, though – businesses will need to figure out how to merge convenience with privacy concerns. Currently, we can’t have it both ways, as AI can’t learn everything about a person while keeping that information private. As data leaks and ID theft continues to grow, these issues will become more and more prescient.

#4 Improved Decision-Making

Machine learning is only becoming a thing because of big data. Living in a high-tech world means a wealth of information – too much to be processed manually. Fortunately, AI can do it in a fraction of the time, providing digestible and easy-to-understand analytics.

For businesses, this data mining and analysis helps them make better decisions. Rather than going off of gut instinct or intuition, companies can utilize AI to make informed judgments. While this process isn’t perfect yet, it will only continue to improve as the technology does.

In the future, brands can avoid massive failures and setbacks, all thanks to AI helping them understand everything from consumer trends to industry changes.

Bottom Line: AI is Shaping How We Live

No matter what, everyone living in modern society is benefitting from AI in some fashion. As brands continue to embrace the benefits of this technology, our lives will only rely on machines more and more. Thankfully, based on current trends, we shouldn’t have to worry about a robot uprising (for a few more decades, at least).

Originally posted on the Catalytics Blog: https://catalyticsconsulting.com/4-ways-that-ai-is-disrupting-the-customer-experience

Companies are quick to hire full-time data scientists to solve their most complex problems when it comes to data innovation. The reality is that the success of implementing emerging technologies can lie with your current internal employees themselves.  One way to improve your success rate is by imparting your employee the requisite skills required to understand data and how to use it in their daily work life. Not only is upskilling your existing employees a benefit to the organization, but it also offers employees the opportunity to future-proof their careers.

Data Scientists are not the Only Answer

Even though the term “Data Scientist” has entered the daily vernacular of business terms, there is still no agreed certification that demonstrates qualification for the role. Most businesses hire a single data scientist (or small team) with the assumption that they have the means to understand the intricacies and nuances of the company because of their advanced statistical expertise. But to support a successful data initiative, subject matter experts will also be required.

This subject matter expertise will have to come from your existing internal employees. No one knows better than these employees the patterns of your business, where to find relevant data sources, and why specific levers can influence an outcome.

The Current Data Gap

So why are organizations looking externally for these skillsets? In short, employers are not developing the tools or providing the opportunities to inspire, motivate, and incentivize their workforce to learn and utilize these skills.

To allow your employees to take advantage of AI and machine learning benefits, you must implement training programs that will educate them on AI and machine learning principles. One way to get employees to adopt AI is by letting them work on problem-solving and analysis challenges cross-functionally. However, leaders need to recognize that many of the first ventures into Data Science will not materialize into considerable benefits. But what will be gained is an opportunity for the employees to become comfortable with the concepts and methodology that will eventually lead to sustained growth and profitability. A key element of improving your Analytics maturity lies in building a culture that supports experimentation and failure.


According to QuantHub, some 35% of organizations surveyed said they anticipate having the most difficulty finding appropriate skillsets for data science roles. And the problem will not improve anytime soon. For this reason, businesses need to look towards the same people they are relying on to handle today’s challenges and prepare them with the capabilities to address tomorrow’s as well.

Originally posted on the Catalytics Blog: https://catalyticsconsulting.com/upskill-employees-on-ai-to-drive-innovation



Artificial Intelligence (AI) is everywhere around us. It has already been widely integrated into our daily lives by the smartphone in our pocket and the Apple Watch on our wrist. This technology has become an integral part of our everyday lives, and we are now interacting with it regularly. We are already living in the age of AI, which is projected to continue growing at an exponential pace. In turn, the number of job roles and the type of skills necessary to support AI initiatives at the enterprise is also increasing. The days of everyone calling themselves a “Data Scientist” are almost over, so let’s take a look at the various roles within AI.

Software Engineer

One pivotal role when discussing AI is the role of Software Engineer. At its core, AI requires data to perform – and without systems to capture this data (consistently and reliably), no algorithm or fancy model will provide valuable insights. Software Engineers are the first line of AI – developing tools and systems to make it easier to build machine learning and deep learning-based algorithms. Social Media apps, sensors, mobile apps, Internet of Things (IoT), analytics tools all have the potential to capture this valuable resource. These software applications (internal and external) have the power to make a new AI initiative within a corporation successful or painfully troublesome.

Data Engineer

If producing and capturing data is pivotal, then finding it and accessing it is indispensable. At the most basic level, the Data Engineer is responsible for analyzing and cleaning the data gathered from the various systems and tools used across an ecosystem. The Data Engineer is the all-around data specialist that prepares data and ensures that it can be consumed and utilized within the organization. By extracting information from various systems, transforming/cleaning data, and combining disparate sources to form a functioning database – the data engineer is the “hidden jewel” in AI. Often these individuals need to have an in-depth knowledge of the business processes that enable them to find hidden data treasures.

ML Engineer

Moving from simple data to predictive models is where the Machine Learning (ML) Engineer shines. The ML Engineer is responsible for developing and training models and algorithms using advanced statistical techniques and data science skills. They identify patterns in historical datasets, find the most influential factors and attributes to a particular outcome, and experiment with feature engineering to improve these models’ scalability and deployment. A discounted responsibility of the ML Engineer is related to business consumption. If the predictive model has exceptional predictive power, but business users are not utilizing its recommendation – “well if an algorithm makes a prediction in the woods and no one hears it…”. The ML Engineer must create the most accurate model possible using their advanced analytical skills and the best method for business users to trust and use the insight to run and optimize their business results.

AI Business Strategist

We can now capture data, access data, and even make unique predictive models, but just because it can be built – should it be built? The AI Business Strategist is an often-neglected role when enterprises are instituting AI for the first time. This role is less about the technical aspect of AI and more about the softer side of AI. The AI Business Strategist should be a senior individual who understands what AI is capable of (Art of the Possible) and recognizes the business impact it can have across an organization (Transformational). They know the business goals and can garner executive sponsorship to experiment with minimum viable products (MVP). They have the business acumen to identify and prioritize the first AI projects an organization should pursue based on their analytics maturity and data fluency. In the simplest terms, an AI Business Strategist can help an organization launch successful AI initiatives that can demonstrate positive ROI.


When considering the ongoing progress of AI within the enterprise, it’s essential to take a step back and look at the big picture. AI is the latest technological advance that’s changing the way business is being conducted today. Companies are leveraging AI across a broad spectrum of functions, enabling them to provide a superior customer experience and deliver a higher return on their investment. Organizations must understand how AI will impact many of the current jobs and ensure they consider all the roles that will enable a successful AI implementation.

Originally posted on the Catalytics Blog: https://catalyticsconsulting.com/what-are-the-different-job-roles-within-artificial-intelligence


Perhaps in the past few years, you have heard the adage “Data is the New Oil”! Given the exponential growth opportunities that are possible with Data, I can see why so many people have embraced this phrase. However, in a few respects, this could not be further from the truth.

Why the Phrase works

The phrase was first coined by Clive Humby in 2006. Michael Palmer expanded upon the quote to say that like Oil, Data is “valuable, but if unrefined, it cannot really be used”.

I grew up in the ’80s in Houston, TX, and from my earliest years, I was the biggest Houston Oiler fan (“Luv Ya Blue”), and at that time and in that city, you could see how Oil was King. Oil was lucrative, and fortunes could be made if one had the means to extract it, refine it, and find use cases for it (i.e., gas, plastics, chemicals).

Similarly, over the past decade, fortunes have been made by those savvy enough to do the same with Data. Today, the list of Fortune 500 companies continues to be disrupted by these businesses – Google, Amazon, Facebook, etc. So it is understandable why many continue to use the analogy.

Where the Phrase breaks down – Availability and Reusability


Oil is not available to just anyone. Companies with deep pockets have to scour the earth for it, and if you happen to live in a place that dinosaurs tended to frequent – well, then you are in luck. But if dinosaurs would not be caught dead in your neck of the woods (see what I did there), well, sorry no fortune for you.

Data does not have the same challenges. Data is everywhere and available to anyone that has the forethought and means to capture it. Individuals, Communities, and Organizations of all sizes have the potential to begin acquiring and leverage this valuable resource. Needless to say, collecting it is not always easy, and refining it does take a unique set of skills. However, Data is available across all geographies like Oil could never be.


The other challenge with Oil is that it is a non-renewable resource. You use it once, then “poof” it’s gone. Sure, you could look for ways to increase the efficiency of its use, but it cannot be reused.

Data is not only reusable; the value that you can extract grows the more you use it!

The same datasets can be used across various functions, analyses, and predictive models. Combine one dataset with another, and you now have new insights that could not be leveraged before –> 1 + 1 = 3. With the proliferation of Artificial Intelligence, your ability to reuse the Data is imperative to identify patterns and learn from historical events.


Overall, I understand why people continue to use the phrase “Data is the new Oil.” But because of its Availability and Reusability, Data can be much more lucrative to many more organizations, communities, and people than Oil alone could ever be.

Unfortunately, I do not see a future where Houston will rename their NFL football team the Houston “Data.” Perhaps the Houston “QuantJocks”?

Originally posted on the Catalytics Blog: https://catalyticsconsulting.com/data-is-not-the-new-oil/

When most people think about artificial intelligence (AI), they imagine something like a robot that can learn, possibly to humankind’s detriment. However, these days, AI is already becoming integrated into our daily lives, albeit much less drastically than an army of androids taking our jobs.

While artificial intelligence has already demonstrated a lot of potential across multiple functions, today, we’re going to focus our attention on one particular industry – human resources (HR).

The Current State of AI in Human Resources

On the surface, it seems a little ironic that computers would be taking over elements of a job that’s all about human interactions, but when you dig a little deeper, it actually makes a lot of sense.

In fact, relying on AI for data management and analysis enables HR personnel to spend more time interacting with people, meaning that technology is helping bring people together, not tear them apart. Let’s see how it works.

Addressing Employee Questions

There are certain times of year that everyone is clamoring for time with their favorite HR generalist (i.e., Performance Reviews, Annual enrollments).  And for many HR generalists, the same questions are asked over and over again, though perhaps phrased a bit differently. Well, enter the Virtual Assistant (or Chatbot or Virtual Agent)!

No matter what you call it, Virtual Assistants (VA) have been designed to understand human language using artificial intelligence.  These VA’s can be trained to understand multiple variations of particular questions and offer prescribed answers to help address employee’s needs promptly and accurately. In addition, VA’s do not require sleep or rest, so if an employee needs help at 1 am, their VA is ready and willing to assist.

Already organizations are deploying VA’s to help employees answer questions concerning health insurance needs, freeing up valuable resources to address higher priority tasks.

New Age of Recruitment

When trying to sift through dozens (or hundreds) of new hire applications, much of that time is wasted. Either you’re spending time looking at candidates who don’t fit your needs, or you are taking significant time categorizing those that are qualified.  This does not include the time necessary to correspond with potential candidates and secure interview times.

What if a computer system could handle all of that for you? While it is not recommended that you solely rely on an AI to choose your next hire, AI can help narrow the field by looking at keywords in a cover letter or resume (using techniques such as Natural Language Processing – NLP) and comparing them to the job description. AI has also been used to help augment your job description so that it’s more accurate to what the position entails. Over time as the system is able to collect more data, the predictive models will improve in accuracy.

With AI, recruiting new people is no longer a time-consuming hassle. It can be streamlined, and allow you to find qualified applicant in a fraction of the time.

Operational Efficiency

As you can see, there are numerous ways that organizations are already applying artificial intelligence to the workplace. Not only does it have the potential to effectively address employee questions and improve the efficiency of the recruitment process, but it can optimize many HR operations as well.

The data-entry or analysis task being performed manually (i.e., managing an employee’s profile, filling out paperwork, quarterly employee surveys) can all be done with AI. What can you do with this kind of power?

Predictive Models

The other side of artificial intelligence (and potentially its most powerful side) is that it can help you make predictions and proactively address issues. In human resources, this can be done by analyzing data about particular employees and optimizing their workflow. You can predict how well an employee will do on a particular task, as well as pair groups based on how well they work together to optimize their performance even more.

Predictive models using advanced analytics techniques like Machine Learning or Deep Learning do require large amounts of data. This data could come in the form of surveys and questionnaires, but also could be gathered based on internal tools (i.e., compensation tools, CRM databases, learning management systems).  Companies are already beginning to leverage these internal sources of data to help improve employee engagement and overall employee satisfaction.

Overall Benefits of Artificial Intelligence in HRTech

Remove Bias: if done properly, AI has the potential to remove bias that may have been historically impacting disadvantaged populations. People can advance or prosper based on merit, not personal prejudice. 

Optimize Work Environment: when you optimize how people interact with each other based on AI models, you have the potential to increase productivity and creativity.

Enable More Human Interactions: rather than spending hours transcribing data and entering forms, HR generalists can spend more time engaging staff and supervisors to ensure tools and resources are being leveraged to create a supportive workplace.

Downsides of AI

While artificial intelligence has a lot to offer, it’s far from perfect. Let’s look at a few of the drawbacks of implementing this technology.

Still Learning: even the most advanced programs are only as capable as the training data that was used to train them (garbage in, garbage out). Generalized AI is not possible yet, so there are many limitations, and you do have to supervise the process a lot of the time.

Over-reliance on AI: at this stage, information that is provided by a cognitive system, like a prediction or recommendation, is just information. People with their vast amounts of experience and context are still necessary to fulfill the gap in judgment that AI systems lack.

Bottom Line

Overall, AI in “human” resources may sound like an oxymoron, but it’s the wave of the future. As HR technology continues to improve, you can expect to see more of these programs installed and implemented in workplaces around the world.

Originally posted on the Catalytics Blog:



Data scientists are the next generation of analytical experts, having evolved from statisticians and data analysts in response to the growth of big data storage, IoT devices, cloud computing, and improved algorithms. It’s a marriage, consisting of the IT and business realms.

Early in my career, I became a Six Sigma Black Belt and learned early the importance that data could have on helping solve some BHAGs (Big Hairy Audacious Goals).  By becoming a Black Belt, you are required to understand a multitude of statistical tools that enable you to decipher anecdotal from impartial. However, at the time, terms like “Big Data” and “Internet of Things” had not yet entered the lexicon, and as a result, predictive algorithms had not yet reached the level of accuracy we are witnessing today.

What is a Data Scientist?

A data scientist must possess in-depth knowledge in the fields of science, mathematics, and coding. A data scientist is known to possess analytical skills, an insatiable curiosity, and a toolkit that allows them to interpret vast amounts of data to help test hypotheses that can improve a business’ bottom line. Think of them as a scientist without the lab coat, but with a t-shirt and sneakers.

Scientist – Lab Coat + T-Shirt + Sneakers = Data Scientist

Of course, that is a broad generalization of the individual and overview of the hard skills required for the profession. But not entirely wrong.

A data scientist assists a company’s operations to help them gain a formidable edge over the competition. Analyzing digital data streams in a company’s website, improving upon existing data collection procedures and creating systems to track anomalies are all examples of the duties of a data scientist.

Why Being a Data Scientist is So Cool

There is a huge demand for Data Scientists these days. In fact, demand has surpassed supply putting the median base pay for a mid-level data scientist at $128,000. Not only are Data Scientist well compensated, Glassdoor recently named it the Top Job for 2019 in America, with job satisfaction scores of 4.3 out of 5.

A Challenging Profession

Although the skills necessary to succeed in the role are lengthy, attaining a thorough understanding of the job is not an insurmountable feat. In my experience, a good data scientist not only needs to comprehend the methods to mine data, wrangle data, visual data, and model data but also possesses the communication skills necessary to work with their business partners to deploy models that will have the largest impact on the organization.

Is Data Science for You?

The skills mentioned above may seem daunting; however, it’s more of a matter of learning the skills as opposed to being born with them. If you are always analyzing situations and calculating the odds in a game of chance, this might be a great career for you.

However, I will caution anyone that is considering this career path; it requires continual self-learning. Data Science is a new discipline, and there are often multiple ways to solve the same problem. The tools, resources, algorithms, and programming languages are in constant flux. And although there are countless educational paths available for people interested in learning these skills – there is no governing body or one formal path to certify your education.

And as I mentioned above, a fully underestimated skill for any successful Data Scientist is communication. The ability to work with multiple functional areas of a business and tell a story with data can be a differentiator for you.

If you’re committed to lifelong learning, and you find joy in the process of developing your skills, the challenges and opportunities faced in this field could be right up your alley.

Have you considered a career as a Data Scientist?

If so, what is preventing you from taking the plunge?




Originally posted on the Catalytics Blog:


In the world of business and startups, most of us already know the term “Entrepreneur.” An entrepreneur is a person who is motivated to start a new business and propel it to success through innovation, hard work, and long hours (a lot of long hours).

However, one idea that is quickly spreading through the ranks of many high-profile companies is the term “Intrapreneur.” Rather than being a solo enterprise, the intrapreneur is part of an already-established business and leverages some of the same tools and techniques to introduce new products, services and/or processes that disrupt the status quo.

Because this idea is becoming so pervasive and disrupting the current corporate climate, we wanted to understand how it works, why it’s becoming so popular, and how it can benefit your company.

What is an Intrapreneur?

Having been both an intrapreneur for most of my career and now an entrepreneur, I know firsthand that you cannot equate the two. The most obvious difference centers on support structure and capital. Without going too much into the details, an entrepreneur often does not have the support structure or capital to pursue their idea; hence, the long hours mentioned above.  An intrapreneur, on the other hand, has the backing of the company for which they currently work. This process is different and unique compared to something like research and development on a new product line.

Intrapreneurs are encouraged to develop new ideas that could potentially become alternate businesses for the company. Whether it’s a brand-new product that can be sold outside of the current business model or a new app that can be branded and identified separately from the parent corporation, ideas that are fostered through intrapreneurship are often independent of anything else going on or disrupt how companies operate daily.

Why is Intrapreneurship Popular?

Technically speaking, this idea has always been around (Gifford Pinchot III coined the term back in 1978), but lately, it’s been gaining popularity due to the sexiness of entrepreneurship in our society. The concept of a go-getter who challenges the norms and standards of a company is nothing new; it’s just becoming more widespread, and companies have recognized that these individuals differ from their average employee.

But what is it about intrapreneurship that’s making it more prevalent?

Part of it is that many “new” corporations don’t have decades of history behind them. Without those strong roots, they are more willing to explore new ideas and experiment with them. Think businesses like Amazon, Google, and Salesforce.

Secondly, there are a lot of benefits to following this line of thinking. By incorporating intrapreneurs into your business strategy, you are more likely to adapt to any changing landscape and increase your chance of success.

The Benefits of Intrapreneurship

If you’re new to this idea of fostering innovation by disrupting your current business model, then it can seem a bit daunting. We’ll get into the nuts and bolts of how to do this process correctly, but we should first understand why it’ll help your company succeed in the long run.

Spurs Growth

Too often, large corporations start to rest on their laurels. The brand is successful, so why should we innovate and try something new? As the old saying goes, “if it ain’t broke, don’t fix it.” However, this complacency can become a problem as sales stagnate and the brand starts to underperform compared to projections.

With intrapreneurship, you are always going to foster creative solutions and expand your corporate model into new branches. As such, you are cultivating a network of growth that will keep your brand active and engaged with consumers, regardless of any changes that may be happening in society.

Creates Leaders

Intrapreneurs are natural leaders, inspiring those around them to succeed. By investing in these kinds of people in your current organization, you can benefit by allowing them to become the leaders that they were meant to be. Not only will this promote better cohesion within the company, but it will enable you to grow and expand because you now have leaders who are motivated and willing to help guide your brand to the next level of success.

Keeps Workers Engaged

Although some people are okay with doing the same thing every day for years (or decades), many of us get bored after a while and crave stimulation. By cultivating intrapreneurship, you can help stimulate changes that will keep your employees engaged. Rather than following the status quo and doing things the way they’ve always been done, you can disrupt the norms of your business and create excitement in the process.

How to Become Intrapreneurial

If you’re interested in following this change and seeing how it can benefit your brand, then it’s imperative that you do so in the right way. Rather than throwing money at the problem or letting your employees run wild, it takes some level of discipline to get it right. Before we look at the right way of doing things, let’s see some real-world examples for inspiration.

  • PlayStation: back when Nintendo dominated the gaming industry, it was a worker at Sony who realized the potential of a system that could produce more powerful and engaging games. Ken Kutaragi developed the first prototype while still doing his day job at Sony, which led to the first console.
  • Gmail: Google has always been keen on fostering intrapreneurship, and its flagship email service is a direct result of that kind of environment.
  • Facebook Likes: Another major player that thrives off of innovation and growth is Facebook, which hosts “hack-a-thons” for coders and programmers to develop new ideas. The “like” button was a direct result of one of these experiences, and now it’s become ingrained in our current culture.

So, how do you become intrapreneurial in your business? Follow these steps.

Step One: Have Open Communication with Employees

Workers who feel stifled creatively are going to develop other passion projects outside of work. However, talking to them can help you find out what they are interested in and will enable you to work with them on their next idea.

Step Two: Empower Them

In your communications, make sure that you remind your staff that their ideas are valid and worth pursuing. If they know that they can be innovative and creative within the company, they are more likely to do so.

Step Three: Engage Them Outside of Work

If employees are stuck doing the same things, they won’t get creative. However, you can stimulate their skills by providing engagement outside of their normal work parameters. If you want them to think outside the box, you have to take them outside the box.

Step Four: Invest in Their Ideas

Once you have something brewing, allow it to incubate for a little while to see where it goes. Even if it fizzles out or doesn’t work, it’s imperative that you enable your employees to pursue their creative interests along with their regular work. At some point, you will strike gold.

Bottom Line

As businesses have to adapt to a changing world, intrapreneurship is going to play a significant role in shaping the future. Fair warning, if your company culture does not embrace failure or punishes employees for taking risks, this attempt at fostering intrapreneurship will be met with disappointment.

Are you already nurturing your intrapreneurs?

Do you know the successful qualities of an “Intrapreneur”? Next week I will release a new blog on the 5 Most Essential Qualities of a Successful Intrapreneur. Can’t wait, hit me up and I will send you an advanced copy!



Originally posted on the Catalytics Blog:


We have all heard the research that states that 52% of companies in the Fortune 500 have either gone bankrupt, been acquired, or ceased to exist as a result of digital disruption since 2000. So how does a business constantly update themselves and their products to stay competitive and continue to offer their customers value? Innovation.

Modern Innovation

Innovation in the business world isn’t just about creating something that’s never been seen or done before. Innovation is the process that puts an invention or idea on the market where people are willing and able to pay for it. For a business to have truly done something innovative, they must have identified a unique need, they must be able to create a service/product to satisfy that need, and they must make potential clients aware of it.

Unlike previous generations, the barrier to creating new products and services is not being left to the titans of industry. In the not too distant past, product innovation required vast amounts of capital and brand equity. However, the accessibility of technology is making smaller organizations able to compete and often perform more agile than their more risk-averse, larger brethren. Consumers have become less brand loyal, and the ability for smaller organizations to offer personalization targeted to specific niches has proven a formidable challenge for larger organizations.

3 Ways to Innovate

While innovation isn’t easy, there are some basic techniques to keep in mind that will help you step outside the box and start being that leader rather than another imitator.

  • Watch how customers use your product. Often customers will find new ways to use a product or service that the business may not originally have envisioned. Speaking to customers and observing how your products are used, allows organizations to evolve and market their products to a new audience to do exactly what it was “accidentally” discovered it could do — exposing your organization to new market growth opportunity.
  • Combine products to add functionality. Sometimes all it takes is putting the benefits of two products together into one that can put it ahead of everyone else in the market. It wasn’t long ago that cellphone cameras were considered a rarity, but now it’s nearly impossible to find a cellphone without one. As Aristotle stated, “the whole is greater than the sum of its parts.”
  • Collaborate with non-competitive businesses. Most businesses rely on other industries to supply materials to create new products or services. Those businesses that add support, or that are in entirely different industries can turn into a great partnership. Entirely new lines of businesses can be forged, and economies of scale are possible when a collaborative mindset is applied to the relationship. Rather than develop an adversarial relationship with vendors and suppliers, perhaps open a line of dialogue on how you can develop new emerging services or products together.

There is no reason to believe that the impending disruptive technologies that are coming will slow soon. And just imitating your competition will be insufficient for long-term success.

How are you introducing innovation into your organization?




Originally posted on the Catalytics Blog:




Now that the world is digitized, data is everywhere. Whether it’s the food we ate this morning or our shopping habits online, businesses have more information on consumers than ever before. But how can they manage all of this data effectively?

There are two fields that companies rely on to maintain and analyze all of these numbers and statistics. They are Business Intelligence (BI) and Data Science. While they are related to the same thing (interpreting numbers about consumers and industry), they operate in fundamentally different ways.

Today we’re going to break down the elements of both of these systems and compare how they can be utilized together to create a better business model for anyone.

Business Intelligence Overview

At its core, BI is all about understanding information as it is and as it was. Analysts will look at current and past data to draw conclusions about various elements of the business. For example, you may look at how consumers shopped for particular products around the same time of year for the past five years.

The goal of business intelligence is to provide a comprehensive picture of the marketplace for executives and business owners to better understand their customer base. This information is invaluable because it offers strategic insight based on hard facts, not bias or belief. With the numbers coming in from BI, any company can adjust its marketing strategy to match, allowing them to adapt.

Data Science Overview

While BI is all about analyzing what happened before, Data Science is the next logical step in this process. Once you have a complete picture of your consumers, where do you go from there? That’s the core question driving data scientists and how they work.

Overall, this practice is all about taking the numbers and analyses and formulating projections and models for what’s to come. Using our example from above, if you notice that shoppers are buying certain items around a particular season, you can anticipate their needs and deliver better products or services to meet them.

Comparing the Two

As you can imagine, BI and data science are technically two sides of the same coin. Both are necessary for a business to adapt to a changing marketplace, and both systems are highly valuable in their own way. You can’t do data science without hard numbers, which is why they are mutually beneficial.

To further help understand how both of these operations can work together (as well as how they differ), let’s break down some of the fundamental methods used for data gathering and intelligence.

Managing the Hard Numbers

For the most part, BI is an excellent way to take all of the data you have and present it in a consumable format. Things like graphs, dashboards, spreadsheets, and KPIs are all par for the course when using business intelligence.

However, there are some significant limitations. Usually, this data is transcribed and formatted in a particular way, which doesn’t offer a lot of room for changes and adaptations. Thus, if the method of data collection or presentation is flawed, you may not realize the issue until much later.

With data science, you have the flexibility to make adjustments as necessary so that you can adapt in real-time. Feature engineering may be necessary when analyzing metrics in Data Science. Feature engineering is the process of using domain knowledge to create new attributes that can be used to insert additional context to algorithms. Technology has evolved, and with it comes our ability to monitor, extract, and manage any information related to the marketplace.

Analysis vs. Prediction

When looking at BI graphs, you can get a clear picture of what’s going on right now in your business. Having quantifiable items can offer better insight into the potential of your company based on where you’ve been and what’s happened before.

Data science lives exclusively in the future. When you look at models and graphs with this system, they are all guesses and predictions, which can make them a little hard to master. Because you can’t see the future, you aren’t basing your knowledge off of real data, but simply what could be.

Data Discovery

One critical difference between these two strategies is that data science allows you to discover new elements of the marketplace and your business that may otherwise go unnoticed. BI is based on questions you already know, while data science can help you figure out different questions that you want to have answered. The inherent flexibility of the system allows for greater discovery and adaptation in the company.

Storage and Deployment

Traditionally, businesses that used BI had a very limited number of methods of distributing the data across the company. It was managed and analyzed by the IT sector, which stored it on proprietary servers and hard drives. To get access, you usually had to use legacy systems or wait for a presentation to get the information you needed.

Data science, however, is much more reliant on new technology like the cloud, which means that updates and changes can not only be interpreted in real time but distributed as necessary to keep everyone up-to-date. Since the data is not stored on legacy hardware, it’s much more accessible. And although many organizations still have Data Scientists reporting through the IT function, more and more organizations are embedding them throughout the organizations (i.e., Marketing, Finance, Manufacturing organizations).

To further blur the lines, BI software vendors are introducing Data Science elements into their BI solutions. Tableau has built integrations to allow users to execute R and Python code to make models more accessible to organizations. Microsoft PowerBI has also included visualizations in their marketplace that allow users to quickly incorporate K-Means clustering, an unsupervised machine learning algorithm, into their dashboards.


While business intelligence is a core component of any analysis procedure you may have, data science is the key to unlocking your company’s future. Rather than waiting for numbers to come in and be analyzed, you can get a real-time understanding of what’s happening and how it can change in the near future.

Data science will become more and more critical for businesses to stay ahead of their competitors, which is why so many top-tier companies are investing heavily in the technology and the people with this skillset.


Every company does some type of forecasting, and although methodologies differ, the goal is the same: obtain the most accurate forecast possible to leverage and utilize resources most efficiently. Forecasting is important, and a lot is riding on it. A quality and insightful forecast can help a company make informed decisions that positively impact the business. A less than stellar forecast can lead to a series of bad decisions and cause irreparable damage. Artificial intelligence (AI) has proven to be an invaluable tool that can help companies improve forecast accuracy.

Understanding AI

AI or Machine Learning (ML – a subset of AI), is a segment of computer science that mimics cognitive processes in order to process data, or rather to learn or solve problems. Forecasting is a natural fit for ML. Through the analysis of information from various sources, ML can generate a thorough and complete forecast. Data points can include consumer data, such as website analytics, more traditional enterprise information via a business’s finance and operations departments, data from retailers, wholesalers, vendors or other partners and even information garnered from the Internet of Things (IoT) applications and sensors.

What’s At Stake

An incorrect or inaccurate forecast can have major consequences. Failing to properly identify competition, new retail channels, or a decrease in new stores for distribution can hurt sales and leave you with more inventory and less income than you expected. This, in turn, can impact reorder points and cash flow going forward. Increased costs, out-of-stock, and markdowns from overstocking are also very real possibilities resulting from a poor forecast that can negatively impact your bottom line and the health of your business.

Common Pitfalls

Forecasting is absolutely necessary to maintain a healthy business and to expand a business while also protecting a business from the volatility of the market. While forecasting is common, it is also easy to mess up. There are four common pitfalls companies tend to fall into when building their forecasting models.

  1. Data Overload

Information is pivotal to forecasting, but too many data points can be overwhelming. Billions have been and are currently being invested in data initiatives by CPG and retail companies that have unleashed an ocean of data. When an employee is drowning in this ocean of data, it can be hard to separate or distinguish the true value of various facts and figures. Overwhelmed decision makers often fall back on gut-instinct or historical performance to guide category planning. While an overwhelmed team member may have valid reasons for making the decisions they do at that moment, their reactive response and gut-instinct is no match for data-driven findings.

  1. Reacting to Competitor Moves

Retailers often gain market data (for example, point of sale, syndicated, or 3rd party data) to see trends in their industry and make forward-looking decisions. There is value to gauging the health of and direction an industry is taking, but the problem is that competitors are also making similar bets with limited information (often from the same sources and experts). While it is foolish to ignore your competitors, it is also important to not prioritize data gleaned from your competitors too significantly. Without understanding how your competitors are collecting and interpreting data, as well as what forecasting methodologies they are using, it is hard to understand this data completely.

  1. Blindness to External Influences

Many organizations have either built or are building a robust Business Intelligence practice. However, traditional BI tools are great at accessing internal and historical data, such as past sales/revenue or marketing spend. They are not built to easily gather and analyze external data, such as economic changes or consumer behavior trends. Overwhelming resources would be needed to gain access to external data sources, like weather, consumer sentiment, unemployment figures, and so on. Building a repeatable process to acquire and leverage these sources on an ongoing basis requires dedicated time and effort.

  1. Not Using Leading Indicators

Economists frequently utilize leading indicators to predict changes in an industry. These are early signs of interruption and indicate a shift in a particular direction. Organizations can use this same methodology to understand the headwinds and tailwinds of demand for a specific category. Too often, financial leaders rely simply on historical data to dictate future performance when it comes to demand, seasonality, etc. Without incorporating leading external indicators, a forecast is missing key influencers and has the potential to miss the mark completely. This has the potential to place a business behind their competition and fighting to regain ground and catch up.

The Role of Machine Learning

ML enables companies to test multiple scenarios and validate hypothesis using a combination of internal data sources, like sales, shipments, and marketing spend; along with external data elements. When all of this information is combined, ML models have the potential to discern patterns and make accurate predictions about everything from shifting consumer opinions and habits, changes within the industry landscape, affects upcoming weather patterns will have on demand, and more.

All-Encompassing Platform

Pulling and processing different data points from various sources is a hassle and increases the odds of data fatigue or mishandling of information. ML makes it possible to track all data in one location. An example of this in practice is the Machine Learning technology being created and implemented by Prevedere (https://www.prevedere.com/). These all-inclusive platforms create a single location for data, eliminating miscommunication or compatibility issues between different platforms. Utilizing one platform typically requires easier maintenance and is also a more cost-effective solution.

The Significance of Timing

Forecasts are only useful if they are available in a timely manner. Businesses need time to interpret and react to a forecast. However, competently sorting through intensive, detail-rich data takes time. ML can quickly sort through decades worth of data, providing a concise analysis. As technology improves, the turnaround time between data acquisition and insight can be reduced even further.

Harnessing all of this data enables businesses to react in real time. A company can be agile by identifying trends that may have otherwise gone unnoticed, as well as hazards to avoid. The information provided by ML allows for accelerated responsiveness and reaction times, which improves efficiency and service throughout all departments, while also reducing costs.

Ability to Learn

Over time, organizations can begin to see the real power of ML as it repeatedly supplies additional information (i.e., data). With more data to train against, ML models improve accuracy by leveraging the existing and new datasets, identifying movements/tendencies and continue learning throughout the entire process.

Its learning capabilities enable ML models to demonstrate an understanding of the unique needs and requirements facing an industry and a specific business. This learned knowledge continues to be developed to help improve future forecasts. With refinement, these forecasts can become very tuned in to a specific business, enabling the technology to expertly leverage the available data to address the unique needs of that company.

Handling Anomalies

Anomalies are those little variances that have the potential to ruin an otherwise excellent forecast. Sometimes weird or unexplained things happen, but the thing with anomalies is that they are not always identifiable as such. Distinguishing between a freak anomaly and a bleeding-edge trend can be a challenge. Or rather, it can be a challenge when using traditional forecasting methods. ML forecasting systems can learn to spot anomalies for what they are and handle them with ease. Accurately defining an anomaly as such maintains the integrity and value of the forecast.

AI/ML Deterrents

Despite the clear benefits, the path to implementing an AI or ML solution is often difficult. Organization roadblocks, disagreement over taxonomy or rather the framework, along with lack of support from senior management and even uneven team support or understanding of the benefits all contribute to the difficulties of creating internal ML forecasting solutions. Even when everyone at a company is onboard with implementing ML forecasting solutions, finding the time to do so may be a roadblock. Many executives spend so much of their time handling routine tasks and responsibilities that finding time for strategic planning, no matter how much value this planning may deliver, can be a challenge. A recent survey conducted by Quest Mindshare in March of 2019, over half of the executives surveyed stated that they “lacked time to devote to strategic planning”.

Long-Term Solutions

Studies show many businesses currently direct some resources towards predictive analytics or AI at this time with more businesses planning to increase spending in this area in the future. Companies, however, are faced with the conundrum of deciding whether to build these solutions internally or outsource their AI needs. While an internal team may have the advantage of understanding the unique needs of their employer, working with a business that specializes in AI/ML technology and specifically forecasting may offer a better solution. An external partner may have a deeper understanding of the technology that enables a business to quickly implement effective solutions and often provide a quicker route to return on investment (ROI). If chosen correctly, the partner will work with internal subject matter experts (SMEs) to customize their technologies and systems to fit the specific needs of the business (i.e., forecasting shipments, revenue, demand).

Predicting Your Future

AI is the future of forecasting, and as the technology improves the forecasting capabilities of AI will continue to improve, as well. The right insights can inform business leaders to make important decisions. Capitalizing on AI can give CPG businesses an edge over their competition through properly leveraging and maximizing resources while also separating long-term trends from passing fads and providing even more valuable insights.

Investing in AI/ML empowers businesses to turn data into insights. By combining internal data with relevant, correlated external data sources, AI model forecasting can help you plan for tomorrow and transform your business for the better.