It has become a problem for most enterprise managers concerned. Obviously, data analysis can be called great opportunity in the big data era. However, the data sets need to be so big?
Now widely accepted definition of the concept of big data, Gartner put forward three V's, that large number, variety and fast changes (volume, variety, velocity). The beginning of this century, became popular big data. Managers are also actively seeking to develop their own methods of large data architecture. However, managers ignored is that big data analysis problems may be enough to solve through internal deployment, and much simpler than expected.
Shaw is an example. It is a carpet manufacturer, it does not have to develop a lot of big data infrastructure companies, big money buying a third-party customer data or phone site data, but work hard in their own enterprise resource management software, customer relationship management systems and data warehouses, also succeeded in increasing sales.
Tim Baucom Shaw, vice president of the commercial division, said, "Now is a great opportunity to integrate these systems together."
Baucom expressed, Shaw hopes to solve the main problem is price fluctuations. Generally, Shaw sales process to go through six months or even longer. Price of raw materials will change during this period, if the contractor exceeded the budget, it is possible to modify the order. These have led to Shaw's sales staff is difficult to ensure profitable sales were flat.
In 2005, the company recognized the need to pass data to predict prices. Baucom Zilliant better understanding of the company, the company offers a predictive tool Zilliant customer and product data to help optimize the commercial price. Shaw deployed Zilliant MarginMax software, track quotations throughout the sales process, combined with the specific circumstances make adjustments to measure the consistency of sales pricing. The software entered all the useful information carpet manufacturer existing data systems.
The integration of these different data sources together is not a small feat. Baucom said there was no mutual infiltration between customer service, sales and site data. All of these are stored in the silo system. A beginning, Shaw relies on data scientists Zilliant opinions configure the system so that it can understand the relationship between different data systems.
But in the beginning, the team believes that net sales of this new model is concerned that some of the causal factors, such as price changes a week. Many organizations in the establishment of analysis system are facing this problem. Many times the data scientists need to define the algorithm, but tend to be business experts to determine the effectiveness of the algorithm. In this case, Baucom had to personally find meaningful and viable relationship, such as changes in profit and profit changes resulting from Applied Materials resulting from project types. By working with suppliers, he improved the system to generate results more guidance.
Baucom Shaw now represent sales managers are armed with data, they will be more effective pricing. Since the application of big data analytics , Shaw sales profit increased by 5%.
Baucom not sure if this is a big data cases. In 2005, many people think this is a case of big data, but a few days according to the standard point of view, it can not be considered a true case of big data architecture. But for Baucom, it is important that the pricing decisions based on data.
