2017 Data & Analytics Solutions
In this edition of the Technology Solutions Guide series, CGT presents a comparison chart of 42 data & analytics solution providers for the consumer goods industry. Plus, experts from guide sponsors AFS Technologies and T-Pro Solutions provide thought leadership for CG manufacturers navigating the challenges and opportunities around effective, efficient data management and analytics. (Download the full PDF of the 2017 Data & Analytics Guide, including the comparison chart of solutions).
CGT: How close is the consumer goods industry to true single-view data management? What still needs to be done?
MICHAEL SCOTT, AFS TECHNOLOGIES: The quest for single-view data is more of a journey than a destination. With the immense amount of data available today, organizations must be able to map new data streams with existing data streams for effective use. For example, while the Internet of Things contains valuable data, companies must be able to connect it back to their own data to take full advantage of it. It’s great to know one of our freezers is malfunctioning, but we need connectivity with our other systems for scheduling maintenance, managing deliveries, evaluating failure rates — based on brand, geography and other factors.
Organizations can address this by knowing what data from different systems can help them run their businesses better and where that data resides. Even with masses of data, we only need a small portion of it to help us. Many CG companies have mastered pieces of the puzzle, but few have fully solved it.
WAYNE SPENCER, T-PRO SOLUTIONS: From a standpoint of true single-view data management, we do not see any consumer packaged goods manufacturers close to accomplishing this. The sticking point is a combination of antiquated enterprise resource planning systems and at least three to five disparate intelligence silos that still require a spreadsheet to achieve total corporate business analysis.
From a trade promotion optimization perspective, some solutions can deliver a single-view database with all the necessary analytical components: shipment data, event spending data, POS data, competitive data and consumer/shopper marketing initiatives. This kind of single-view TPO database enables accurate, best-in-class post-promotion analysis, in addition to predictive/optimized merchandising scenario development that enables annual customer planning optimized for revenue, volume and profit objectives.
With this in mind, the CG companies that are seeing the most data-driven success are the ones that can quickly organize data into useable analytical insight that drives action. Gone are the days where analysis only leads to a justification of spending. Instead, these companies are combining this intelligence with predictive/prescriptive modeling to build proactive strategies to manage revenue and sustain growth.
CGT: The adoption rate for IoT technology is ramping up significantly. What internal and external data streams (retailer and/or consumer) will widespread adoption facilitate?
SCOTT: IoT technology can provide near real-time data to monitor a product from the factory to the store. Manufacturers can use simple IoT sensors that monitor equipment status or even detect an out-of-stock. Many data points that required a manual visit can now be gathered remotely, in milliseconds. Businesses can be optimized with less time spent gathering data points and more time acting on the new data.
An organization might reduce its fleet of trucks if it had notifications for when visits were needed instead of just scheduling weekly visits. Fresher product and fewer out-of-stocks is always a good thing! CGs will learn how to effectively use IoT if they realize that it is still just a tool to help the business along with appropriate processes. Ask yourself, “Out of all the information I can gather with IoT, which will help my bottom line the most?”
SPENCER: We are witnessing a transformational revolution of conventional brick-and-mortar retailers being assaulted by the buying behavior of Generation X, Millennials and Generation Z. They are purchasing a larger portion of their day-to-day consumer products via the internet. The need for real-time consumer insights is at a critical juncture if conventional brick-and-mortar retail stores are going to survive.
With the onslaught of Amazon’s efforts to carve out substantial market share, there has never been a more pressing need for both conventional retailers and CPG manufacturers to begin to effectively collaborate using the extensive consumer behavior intelligence they both possess. They need to get all this intelligence in one real-time database that amounts to a single version of the truth that can mutually optimize category, brand and SKU performance. This is mandatory if the CPG sector wants to avoid being reduced to one large internet supplier and a few specialty brick-and-mortar retailers.
The good news is that the ingredients to accomplish this collaborative planning environment exist today. The cloud with Amazon Azure and Rack Space, to name a couple, can handle massive amounts of the data necessary to optimize an existing category portfolio. Furthermore, powerful modeling and artificial intelligence are available and getting more powerful with each passing month. Now all we need to do is stop talking past each other with spreadsheet-compiled intelligence and begin true collaboration that incorporates consumer insights, consumer buying behavior, and accurate planned vs. actual results.
CGT: What methods are proving most effective in helping CGs turn data and insights into action?
SCOTT: As emerging technologies generate tons more data — typically faster than it can be consumed — the trick is to figure out what in this sea of data is the most valuable for your organization — thus, the return to “small data.” Data points flow in from delivery trucks, shelf sensors, etc., and it could be argued that you can use all of it; but what specific data will significantly change your bottom line?
While a large organization might still have a “big data” mindset, internal departments might only use a small portion of the data. CGs should create a short list (emphasis on “short”) of questions (or metrics) that are most important, then determine the data needed to address those questions. For example, if I’m a dairy producer, I care about getting my product to the outlets that need it in a timely manner while keeping it cold. What is most important to your organization?
SPENCER: The need to manage “big data” will never go away.
Amazon and other massive cloud data collection entities will not allow this to happen. The key is to make using big data feel like you’re working with “small data,” but with the unlimited intelligence and powerful modeling capabilities that come with big data. The combination of massive amounts of data being modeled in relatively real time with complex constraints, artificial intelligence and substantial consumer insights within an intuitive, easy-to-use interface is the formula for accomplishing a small data feel with big data power.
The CPG sector on both sides of the spectrum needs to focus on minimizing and eventually eliminating its dependence on the spreadsheets that make data seem unmanageable. The only way we’ll be able to turn data insights into action will be to harmonize disparate intelligence silos to develop optimal joint business plans for the combined expenditures of tactical trade spend, shopper marketing initiatives, and joint digital brand messaging. This “one version of the truth” planning optimization engine will begin to mutually maximize collective merchandising/media investment and relieve significant margin erosion.
The data exists, and the ability to manage big data is here and only going to get better. Most importantly, powerful modeling and artificial intelligence predictors are getting more accurate with increased data points, allowing consumer goods companies to focus on turning insight into profitable action.