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 ( 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.


For years now we have heard the term “big data” — experts have predicted that data will, in essence, take over our world and drive virtually every aspect of our existence, from how we run our businesses to how we conduct our free time.

While we haven’t quite reached that pinnacle just yet, there’s no question that data is becoming more and more of a factor in our day-to-day lives. As technology develops, the amount of applications that rely on big data, data science, and data-driven analytics only increases. The sheer volume of data collected these days is staggering and growing at an almost exponential speed. It is estimated that 2020, 1.7MB of data will be created every second for every person on earth. Because of this, the question for companies in today’s world is not, “should we use data” but “how can we bring data into more and more aspects of our operations?”

In short — how can we compete in today’s data-driven world?

Answering this question is no easy task, but based on current trends and developments it is possible to take at least an educated guess as to what today’s businesses need to think about when it comes to navigating the world of big data today and in the near future.

Artificial Intelligence

Artificial Intelligence, otherwise known as AI, is the term used to describe the complex simulation of human intelligence by machines. It’s been the stuff of science-fiction for years (usually, with a terrible outcome for the humans) but in reality, it’s been a slowly developing field for some time now. AI is a wide field that primarily deals with machines learning to apply logical reasoning and self-correction while taking in data and learning about the world around them. As machines learn, the AI they apply allows them to solve problems, learn about their users or change and refine how their applications work. This self-learning can have massive implications for the business world.

Because of its inherent complexity, a lot of what falls under the realm of AI is out of reach for current developers and businesses. However, as AI becomes more and more refined, the applications become obvious and important. Be on the lookout for the term “democratizing AI” to become part of our lexicon in the not too distant future. Whether you’re developing an app for your business or using your computers to help predict future customer interactions, the uses for AI are still being formulated, and experimentation will be key to gaining a competitive advantage.

Machine Learning

Machine learning is a specific category of AI. Machine learning deals with how computers are able to learn and adapt to the needs of the world around them without any explicit programming. To accomplish this, programmers/ data scientists/ ML engineers (the job roles continue to grow) make extensive use of algorithms which can learn and adapt based on user data and input.  Machine Learning typically falls into one of three categories: supervised, which makes use of labeled data to allow computers to recognize characteristics and use them in the future; unsupervised, which leaves data unlabeled and forces machines to understand and classify various characteristics on their own; and reinforcement learning, which use algorithms to analyze the environment and learn from various causes and effects.

In business, machine learning is a vital part of any model of data analytics today. Machine learning is the driving force behind such various applications as bank fraud detection, email spam filters, and product recommendations. All of these applications can be used to make a customer’s experience and interactions more convenient and pleasant. It’s not too hard to see how each of these skills, and others like them, could either help or truly harm customer relations. As businesses look to become more data-driven, employing Machine Learning will be a vital part of the equation for many years to come.

Deep Learning

Deep Learning is just a smaller sub-set of Machine Learning. Deep Learning is designed to more closely resemble the organic neural networks of the human brain. As a result, deep-learning algorithms can tackle much more complex problems that involve unstructured data like images, speech, and text. Typically DL requires significantly more data than ML models, but the benefit is that it has the potential to increase accuracy with less cumbersome fine-tuning or feature selection/engineering.

Deep learning is still a field that is hard to describe or understand — even for those who work within it. The expected high costs and technical expertise required to implement a Deep Learning solution will preclude most organizations from introducing the technology; however, the benefits will certainly be present for an organization that identifies the right use case (i.e., autonomous vehicle, facial recognition).

As companies contemplate ways to stay competitive, some implementation of Artificial Intelligence will be needed – whether building the solution in-house or finding an external partner with a more in-depth knowledge of the technology. However, the organizations that truly begin to embrace AI and forge ahead to develop a comprehensive strategy to experiment and introduce use cases will outpace those laggards that wait.