Tag Archives: Forrester
According to a Forbes article, the average organization will grow their data by 50 percent in the coming year. Overall corporate data is expected to grow by 94 percent. According to Informatica, data is predicted to increase by as much as 75 times the current volume by the year 2020. What is Big Data all about? Big Data is the management and analytics of an immensely growing volume, variety, and velocity of data in a digital world. A precise definition of big data from analysts like Gartner and Forrester is a hot topic right now that is covered in a lot of blogs.
In my point of view, big data is connecting the dots. It is connecting more than ever before. But what is the role of product data in a big data world?
After recently talking to our customer Halfords, the UK retailer for bicycle and auto parts revealed: All challenge Amazon. Halfords is known as the expert and friend for cyclists. Therefore they position their brand as the leading expert with the best information. They use product information as a differentiator in the market to gain customers’ trust.
This article refers to a challenge that a lot of distributors and retailers are facing. In order to better serve their B2B and B2C customers, they grow and position their product range to be the one trusted supplier. The long tail (endless aisle) strategy offers higher margins with niche products as well.
These distributors and retailers are faced with the challenge of handling 100s or 1000s of suppliers providing content for millions of products. What happens when different suppliers provide information for the same product?
A business case of big product data: Innovative distributors attempt to merge different product content to create the best and richest product information. This requires an intelligent analysis of a supplier’s product data, and intelligent automatism in order to merge this data to create superior product content. The role of the data steward in defining these rules and policies becomes more important than ever before.
How can this be solved?
Data doesn’t only come from suppliers but from other data sources as well. Basic product information might come from a data hub like GS1 or could be synchronized from the distributor’s ERP system, which in turn might be leading the creation of new products in the distributor’s master assortment.
This basic data will be enriched by data coming directly from the manufactures or the suppliers of the distributor. These different data sources provide content for the same products in different levels of quality, richness, and completeness.
Which parts of product information are used from which data sources is determined by objective data quality rules combined with a definition of trust specific to each data source. One supplier is known for accurate descriptions in English while another provides the better German information. And yet a third data source usually provides the best images.
Governance of Product Information Creates Competitive Advantages
This is when Product Information Management comes into the field: to control big product data. According to Heiler’s PIM Product Manager, Markus Schuster, these business processes can only be successful when used with intelligent, highly automated data quality proofpoints and workflows that adhere to the data governance policy.
I attended Forrester’s Customer Experience conference a couple of weeks ago to get up to speed on how different companies are changing their processes and culture to truly put the customer at the center of their world. Concepts such as voice of the customer, the buyer’s journey, and moments of truth were tossed around like popcorn. The high bar set at the conference was to achieve empathy with the customer in order to deliver true customer experience innovations. Beyond such lofty concepts, there was also a lot of discussion about the underlying practical matter of gathering the relevant data about customers in order to build the knowledge and understanding essential to creating that empathy. (more…)
According to an article written by Mark Brunelli interviewing James Kobielus of Forrester Research: Forrester’s Kobielus: It’s time for a Hadoop standards body, Hadoop is still a bit immature and needs adoption of standards. Mr. Kobielus goes on to indicate that when implementing Hadoop, “whether it’s through a data warehouse or Hadoop cluster, you’re talking about petabytes or multiple hundreds of terabytes worth of storage.” Hadoop, while designed to access these large data volumes (which can include social media data), does nothing to manage retention of that data. (more…)
According to recent Forrester research, “security concerns” and “integration challenges with other applications” are the primary reasons firms aren’t interested in software as a service (SaaS). This week Forrester’s Stefan Reid wrote about the Informatica Cloud Summer 2010 release and pointed out another reason: The USA PATRIOT Act. (more…)
Many of our customers express frustration that even though it is quite obvious how their business suffers from poor data quality, they find it difficult to convince their associates to invest in initiatives that correct the problems.
Earlier this year, we participated in Rob Karel’s Forrester research that addresses this issue. The resulting research paper is titled “A Truism for Trusted Data: Think Big, Start Small” and its getting a lot of interest. Recently, there was a nice writeup in Intelligent Enterprise where they interviewed Rob and also made mention of the Data Quality ROI calculator that we’ve developed by working alongside our customers.
The article states
While Forrester is often suspect of vendor-supplied calculators, the research firm lists Informatica as an example of a vendor that has taken an approach that matches Forrester’s bottom-up strategy. The Informatica Data Quality ROI Calculator enables customers “to capture and visualize the benefits of a data quality investment ” before that investment is made,” Forrester said.
The report is available from Forrester’s web site. It contains some nice examples of how customers have built a business case for justifying investment in Data Quality. If you would like to share some previous successes in building a Data Quality ROI, feel free to post a comment!