In order to manage your master data, you must first measure it.
Retail chains that have brick and mortar stores as well as online platforms often struggle in identifying the customers visiting their site. Even with all the information available at their disposal, the probability of identifying the customers accessing their website is a mere 30%.
This blog post section gives an overview of GDPR, requirements, impact on business, common challenges faced by organizations and next steps recommended for GDPR compliance.
Data Generation, Analysis, and Usage - Current Scenario
Last decade has seen an exponential increase in the data being generated from across traditional as well as non-traditional data sources. International Data Corporation (IDC)report says that, data generated in the year 2020 alone will be a staggering 40 zettabytes which would constitute a 50-fold growth from 2010. The data generated per second has increased to 2.5 Quintillion bytes and with the advent of latest innovations like the Internet of Things; it is poised to grow even more rapidly. This increase in data generation coupled with growing ability to store various types of data that is being generated has ensued in a vast repository of data which is now available for scrutiny.
The objective of most MDM Hub projects is to establish a trusted source of master data. In addition to the right vendor, appropriate partner, and an efficient implementation plan, it is very important to come up with the right integration strategy. An organization's existing eco-system will typically consist of different source systems and integrating all of them with the new MDM system becomes a huge task in itself. Any small misstep here would lead to delays, cost overruns and substandard or missing data. In turn, sponsors will lose confidence in the MDM hub as a trusted source to be integrated with downstream applications.Net result: the ROI will seem a lot less attractive.
This article was featured in the Q3 2014 edition of Loyalty 360's Loyalty Management magazine.
This blog post is the second part of the Data Warehouse Migration to AR series. The first part of the blog post series Data Warehouse Migration to Amazon Redshift – Part 1 details on how Amazon Redshift can make a significant impact in lowering the cost and operational overheads of a data warehouse.
Traditional data warehouses require significant time and resource to administer, especially for large datasets. In addition, the financial cost associated with building, maintaining, and growing self-managed, on-premise data warehouses is very high. As your data grows, you need to always exchange-off what data to stack into your data warehouse and what data to archive in storage so you can oversee costs, keep ETL complexity low, and deliver good performance.
Every project comes with its own set of challenges and MDM projects are no different. At InfoTrellis, we have built an effective methodology for Project Management, through years of experience in managing MDM projects. We like to call it as “InfoTrellis Smart MDM Methodology”.