IDC forecasts the Big Data technology and services market will grow to $16.9 billion in 2015 at a rate that's about seven times the overall information and communication technology (ICT) market. Spending those funds wisely is going to be very important to manufacturers, starting with identifying the right use cases for those investments. Yet we still find manufacturers not fully understanding big data and how big data technology is something new and of value to their businesses.
Big Data - "Nothing new". That's how I'd summarize the reaction of (hopefully) a minority of the consumer goods manufacturers that attended Consumer Goods Technology (CGT)'s Sales and Marketing conference in June. Yes, I admit, I was surprised by that comment. But if I can judge interest in the topic based on how many attendees came to the Big Data panel session, then I can tell you it wasn't the opinion of all attendees. I don't think we should dismiss the "nothing new" reaction without considering why it does or doesn't make sense. I'd also like to share some of the Big Data-related discussions that took place at the CGT event and some of our research on how manufacturers will use big data technology in general.
We expect companies to spend a tremendous amount of money on big data technologies. In fact, IDC forecasts the Big Data technology and services market will grow to $16.9 billion in 2015 across the technology stack including infrastructure, software, and services. In our current forecasts, the growth rate for this market is about seven times that of the overall information and communication technology (ICT) market. Spending those funds wisely is going to be very important to manufacturers, starting with identifying the right use cases for those investments.
Almost every manufacturer will tell you they have too much data, or at least a large quantity of data that doesn't deliver a sufficient quantity of value; from that perspective big data is nothing new. (Of course, most also want better and more data, too). Unfortunately, most people that are skeptical of big data technology focus in too much on the word "big". The way we're using the term Big Data today does represent something new, at least following the "Four V's" definition, as we define each of the references by the following:
- Volume - Large quantities of data that could reach up to petabytes (and more), but again, size is not the only thing that matters.
- Variety - Multiple types of data, from structured to unstructured or semi-structured data, in combination, meaning companies could analyze a mix of data from web logs with customer information stored in a database with sensor data that provides real-time information on inventory or shipments.
- Velocity - The speed that data arrives and the speed of analysis, ranging from batch to streaming data, and keeping in mind the speed of both business processes and users' decision requirements.
- Value - More affordable technology than ever before, including open source software and decreasing hardware prices, as well as increasing business value that manufacturers can generate from the use and analysis of Big Data.
Given this definition, big data tools and technology are for more than just analyzing a large volume of data. A manufacturer could also be analyzing data from a variety of sources, delivering analysis at a greater velocity. This combination of the V's can enable manufacturers to use data as a means of making decisions faster or driving analysis that was too complex to do affordably in the past. We also make note of the fact that not all applications of Big Data technologies are for analysis of data; they also include operational workloads and information access applications.
At the CGT Sales & Marketing conference in June, I participated in a Big Data panel led by Michael Forhez from TCS, and I was joined by CIOs from Energizer and Conair. Given the conference's emphasis on sales and marketing challenges for consumer products manufacturers, many of the use cases we discussed focused on understanding the consumer better and customer service. Specific examples include trade promotion optimization, SKU optimization, and modeling marketing campaigns; more use cases in this industry segment include sentiment analysis based on behavior in ecommerce websites or comments on social media forums (blogs, Facebook, twitter, etc).
We also raised examples that were related to the supply chain as well, such as knowing when product shipments could be delayed based on ocean carrier data or other information sources for inventory in-transit. Ultimately, the value would come from knowing in advance if the current supply chain performance would impact customer orders. Our recent IDC Manufacturing Insights supply chain survey of U.S. manufacturers shows a significant interest in applying big data; 52.7% of all manufacturers (not just consumer products) consider big data tools to be important or very important to their supply chain. That number increases to 62.2% among those with supply chain titles. On a broader scale, we've identified additional use cases in manufacturing as warranty analytics, enterprise asset management, and monitoring equipment performance.
Although we see evidence that manufacturers are taking the first step to creating value from big data technology by identifying the best fit use cases, manufacturers will stay in learning mode for some time. Challenges will relate to managing data quality and data governance, selecting technology options and IT partners, and finding people with the right skills or "an appetite for analytics" as one of the CGT panel CIOs commented. We also suggest that manufacturers explore options such as big data appliances and outsourcing big data skills or even technology through the cloud. Today our research shows that each use case requires a different combination of software, hardware, and services to be most effective, but we believe that manufacturers will find that the investment in big data tools for volume, variety, and velocity is ultimately worth the "4th V" - value.