Interesting perspective in Wall Street & Technology last week from a performance measurement solution vendor. The premise is that data aggregation is the answer to keeping up with the ever-increasing demands of big data. At first glance, this seems like a sound concept but upon further examination, it would not hold up for the needs of an average asset management firm.<\/p>\n
First, while all buy-side firms have to reconcile their books and records to their custodians, custodian downloads only contain the basics that are used for client portfolio reporting, namely positions & transactions. This is good for a performance measurement-only system, since aggregating transaction downloads saves a lot of work, rather than trying to go to each custodian directly or trying to gather transaction data from in-house systems for use in dashboards and client reports.<\/p>\n
The problem arises in the data that is required to manage the investment process itself, namely portfolio analytics \u2013 It is not typically provided by custodians. If it is, it\u2019s only provided at a basic level and isn\u2019t useful for pre-trade investment decisions since it’s not provided until the following day, at the earliest.<\/p>\n
And this is where the data aggregation argument falls apart. Big data and the data management process itself have a much bigger role in the aggregation of data from sources other than the custodians, namely analytics and pricing services from various vendors. The challenge becomes getting access to this data. Custodial data aggregators can\u2019t get it because the data is internal to the individual investment firm. From there, the challenge increases when trying to validate this data to make sure that it\u2019s accurate before it\u2019s used to make investment decisions or before it ends up on a client report. This is where data management technology comes into play and this technology has to come from other sources either internal or external, since the data aggregators only get data from custodians.<\/p>\n
So in summary, data aggregation can help with big data in certain specialized areas but it\u2019s not the be-all, end-all answer for the big data challenge. The answer is data management technology itself, whether it’s built in-house at a huge expense to the firm or provided by third-party vendors.<\/p>\n","protected":false},"excerpt":{"rendered":"
The premise is that data aggregation is the answer to keeping up with the ever increasing demands of big data. At first glance, this seems like a sound concept but upon further examination, it would not hold up for the needs of an average asset management firm.<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_stc_notifier_status":"sent","_stc_notifier_sent_time":"2023-01-11 07:00:16","_stc_notifier_request":false,"_stc_notifier_prevent":false,"_stc_subscriber_keywords":"","_stc_subscriber_search_areas":"","content-type":"","footnotes":""},"categories":[177,71,100,38,41],"tags":[],"class_list":["post-982","post","type-post","status-publish","format-standard","hentry","category-ai-investment-management","category-big-data","category-blog-articles","category-data-management","category-technology"],"acf":[],"yoast_head":"\n