In a previous blog, we discussed how Big Data is used in companies, what benefits they achieve, and what challenges they face. The starting point for generating value from Big Data typically lies in use cases. The 559 participants in our “Big Data Use Cases” survey reported more than 1,000 use cases, giving us a good overview of how companies actually tackle Big Data. I will present a summary in this blog. More details can be found in the full study (see below).
Where Big Data is employed
Figure 1: In which areas does your company use, or plan to use, Big Data analysis? Source: BARC survey “Big Data Use Cases. Getting real on data monetization”
Taking a closer look at where companies are utilizing Big Data analyses today, we see many different areas across all departments. Marketing and sales are leading the field, with about a quarter of companies stating that they have already integrated Big Data analyses in their processes (see Figure 1).
Enterprises can no longer allow their customers’ desires, motives, needs, and behavior to be an unknown. Big Data initiatives help build a complete picture of the customer by making each interaction with the company transparent. In order for this to work, enterprises must take data from many different isolated customer contact points, pool it together within a big data project, and make it ready for analysis. By creating a complete picture, they can address their prospects and customers more personally and in a more targeted manner, reduce churn, and win new customers. Also, detailed data about customers and prospects can enable companies to predict their behavior and send them better, more relevant offers.
The word clouds of all Big Data use cases entered for marketing and sales are naturally dominated by the word “customer,” with sales focusing on products and behavior while marketing leans towards the market and campaigns.
Figure 2: Word cloud from Big Data use cases in sales
Figure 3: Word cloud from Big Data use cases in marketing
After marketing and sales, the survey reported plenty of Big Data use cases in all other departments, including production, finance and controlling, and research and development. Furthermore, the study also shows differences between industries.
Let’s consider manufacturing as an example. While there is a lot of buzz around the Industrial Internet, Internet of Things, or Industry 4.0, which all need Big Data processing and analysis, applications still seems to be in their infancy: only 13 percent of participating companies in manufacturing use Big Data analysis in everyday business, compared to 27 percent of retail companies. However, manufacturing also has the highest ranking for pilot projects, with 24 percent.
This indicates that companies are at an early stage and that we will see an increased level of adoption in the near future. Manufacturing companies often find value in data for production (Figure 4), logistics/supply chain (Figure 5) and after sales. Overall, use cases in production and logistics focus much more on processes and their optimization.
Figure 4: Word cloud from Big Data use cases in production
Figure 5: Word cloud from Big Data use cases in logistics/supply chain
Also, IT departments use Big Data mostly from logs to track usage, identify problems, support security intelligence, and improve user service (Figure 6).
Figure 6: Word cloud from Big Data use cases in IT
Last but not least, finance and controlling departments are increasingly employing Big Data. Planning, forecasting, and budgeting are major processes supported by Big Data, while the main object of interest is costs (see Figure 7).
Figure 7: Word cloud from Big Data use cases in finance/controlling
The high numbers for planned Big Data deployments by department (between 34 and 56 percent overall) speak for themselves (see Figure 1). Sooner or later, Big Data will reach every corner of the business. But despite the large amount of use cases mentioned, 38 percent of all respondents find that identifying compelling use cases is one of their major challenges with Big Data.
In the next blog I will present a methodology to identify and prioritize use cases for Big Data and digitalization that can help to address this challenge.