Filed under: Business Intelligence, Data Governance, Data Integration, Data Profiling, Data Protection
This past May I had the opportunity to visit Informatica’s annual conference, Informatica World, and now that some time has passed, I thought it would be worth reflecting on three aspects of the experience. First I had the opportunity to share a presentation with Robert Shields about the criticality of data protection, and in particular I was able to convey the message about the importance of integrating data protection techniques within the framework of data governance and data stewardship. In fact, I have summarized some of those same points in an article I later wrote for TechTarget searchCompliance.
Second, I attended an executive briefing in which the new senior executives shared their thoughts and expectations for Informatica’s progress over the next year. As Informatica has recently been taken private by a private equity firm, it was good to have some visibility into their plans for how they intend to continue developing products and services that enable data utilization, especially beyond the enterprise’s firewall, as we see more organizations extending their application framework into the cloud.
Lastly, I had a brief opportunity to chat with Informatica CEO Anil Chakravarthy. It is refreshing to see a C-Level manager so directly engaged in both driving the corporate product landscape and setting high-level direction for the global organization. Overall, it was also interesting to see how the company is realigning its messaging with the big data and analytics communities. Clearly, the information economy is growing as more organizations are adopting newer data management and computation technologies like Hadoop, yet in our upcoming survey report on Hadoop productionalization, individuals at all types of companies still see Hadoop integration with established enterprise componentry as well as the enterprise data architecture to be challenging, if not very challenging. As a result, we suggest that vendors providing data management technologies continue to expand their product catalog to include tools that can simplify big data application development, and I see that Informatica’s trajectory is aligned with that sentiment.
Over the past few years, cyber-criminals have become more sophisticated in their means of attack, their targets, and pointedly, their intent. While a decade ago the most severe cyber events would have likely to have involved denial of service attacks or credit card information theft. Since 2014 we have seen what is believed to be a nation-sponsored assault on a major entertainment company, compromised access to millions of records managed by the US Office of Personnel Management (OPM), tens of millions of records managed by Ashley Madison an adult dating site, and tens of millions of Anthem health insurance member and employee records. Read more
Filed under: Business Intelligence, Data Analysis, Data Governance, Data Integration, information strategy, Metadata
Even if in reality the dividing lines for data management are not always well-defined, it is possible to organize different aspects of information management within a virtual stack that suggests the interfaces and dependencies across different functional layers, which we will examine from the bottom – up.
Filed under: Data Governance, information strategy, Recommendations
Data governance is a practice for ensuring that policies about information acquisition, use, protection, and disposition are defined, approved, and importantly, observed across the organization. Information policies are defined in relation to business policies and must be aligned with the corporate mission. Read more
Filed under: Data Governance, Data Integration, Data Quality
What is now generally referred to as “data integration” is a set of disciplines that have evolved from the methods used for populating the data systems powering business intelligence: extracting data from one or more operational systems, their transfer to a staging area for cleansing, consolidation, transformations, and reorganization in preparation for loading into the target data warehouse. This process is usually referred to as ETL: extraction, transformation, and loading.
In the early days of data warehousing, the ETL scripts were, as one might politely say, “hand-crafted.” More colloquially, each script was custom-coded in relation to the originating source, the transformation tasks to be applied, and then the consolidation, integration, and loading. And despite the evolution of rule-driven and metadata-driven ETL tools that automate the development of ETL scripts, much time has been spent writing (and rewriting) data integration scripts to extract data from different sources, apply transformations, and then load the results into a target data warehouse or an analytical appliance. Read more