Data Maturity Survey in Financial Services

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1 Percent of Responses Data Maturity Survey in Financial Services June 29, 2015 Executive Summary PanoVista.co LLC is conducting a high level, indicative survey regarding the maturity and future state of data management within Financial Services firms. The measures include respondent s opinions of current structured internal data, unstructured internal data, and unstructured external data as well as their plans to improve the current state. The early results show that structured internal data management is maturing with active projects planned or underway, while the state of unstructured internal and external data is less mature with fewer projects even though there is an awareness that change is needed. This is summarized in Chart 1, below. Chart 1 Consolidated View of Data Management Maturity 7 Strucutred Internal Unstructured Internal Unstructured External Stuctured Internal Unstructured Internal Unstructured External Maturity (Bar: 1 Low, 5 High), Project Activity (Line: 1 Low, 5 High) This confirms anecdotal evidence that only the early adopter top tier firms -- buy side, sell side and banks have started to invest in Big Data projects to enhance their unstructured internal and unstructured external data, and that the majority of smaller firms are aware they need to do something yet have not begun or are not sure where to start. This is despite the often quoted saying that 8 of Enterprise data is unstructured, and the well-publicized rapid growth of social analytics. PanoVista.co s mission is to bring the early majority financial services firms into the full digital Big Data age with innovative and practical data and analytics solutions. Survey Description This report is the first of potentially a series of updates since at the time of this writing the survey is still live at It is intended to be both a high level survey and indicative in nature. P a g e 1

2 Maturity (1 Low, 5 High) This survey was not intended to provide statistically accurate projections of the detailed processes and procedures in use or planned at the target audience. The survey was ed to selected individuals and published on LinkedIn.com and on the Panovista.co website, with responses gathered and consolidated from all three sources as follows. Structured Internal Data The goal of these questions is to understand the overall maturity level around internal structured data. The responses indicate that most of the firms have some level of aggregation and data cleansing in place, with 5 planning or actively enhancing their platforms. It is interesting to note that those currently satisfied with the current situation are those already at the top of the maturity curve. Chart 2. How do you manage your structured data (e.g., market data, research, prices, securities, positions)? 5. Have data governance oversight architecture to clean, distribute, store, report (including BI), and provide quality control for all structured data 4. Have a data architecture to clean, distribute, store and report (including BI) for most structured data 3. Do some level of data cleansing and distribution (e.g., reference data/ EDM, data hub) 2. Do basic aggregation (e.g., excel, simple SQL data store, ETL, desktop Business Intelligence) 1. Store and use only in source systems (e.g., OMS, research terminal, accounting system) 5% 1 15% 25% 35% 4 45% 5 Percent of Responses Chart 3. How would you rate your current structured data management capability? 25% 15% 1 5% 1. Current situation works well 2. Aware it could better 3. Would like to 4. Planning to enhance 5. Active project to enhance, but no active enhance plans These results are reflective of the growth of data management initiatives over the last ten years to control the cost of acquiring data and also meet the growing regulatory reporting mandates for data and investment transparency. Automating data governance is the final phase of the maturity curve, with many firms currently evaluating options in this space. P a g e 2

3 Maturity (1 Low, 5 High) Unstructured Internal Data The goal of these questions is to understand the overall maturity level around internal unstructured data. The results are a stark contrast to the previous question, showing a much lower maturity level, and an awareness that the situation is sub-optimal, though with few projects to resolve the current state. Chart 4. How do you manage your internal unstructured data (e.g., , documents, chats, video)? 5. Have an architecture to combine structured and unstructured data into common business intelligence views 4. Have a cross platform search capability 3. Have a process to integrate what is needed for reporting, business intelligence 2. Have a database (e.g., SharePoint, Documentum, nosql) 1. Store and use only in source systems (e.g., Outlook) 5% 1 15% 25% 35% 4 45% 5 Percent of Responses Chart 5. How would you rate your current internal unstructured data management capability? Current situation works well 2. Aware it could better 3. Would like to enhance, but no active plans 4. Planning to enhance 5. Active project to enhance Unstructured data often contains valuable business insight into investment research documents (street and internal), staff on-boarding, knowledge management and client communications. Thought leading firms in many industries are investing to harvest this internal information, yet it seems to be a lower priority for financial services firms, perhaps due to the current investments in structured data projects. Big Data often references the 3 V s: Volume, Velocity and Variety. Whereas Structured Data in Financial Services often contains very large data sets and real time pricing and transaction data, it s really the huge P a g e 3

4 Maturity (1 Low, 5 High) Volume (such as log surveillance) and Variety of unstructured data that adds complexity to traditional relational databases and opportunities for those firms who master it. External Unstructured Data The goal of these questions is to understand the overall maturity level around external unstructured data. The results mirror those of unstructured internal data maturity, again with relatively low maturity levels and a similar awareness of the possibilities for improvement, though with little plans to enhance. Please note that questions 3 and 4 have been swapped in this summary vs the initial survey in order to be consistent with the previous section regarding having a process in place for both internal and external unstructured data. Chart 6. How do you manage your external unstructured data (e.g., company universe-related sites to enhance investment research, client usage patterns, social media, video, twitter, chats)? 5. Have an architecture to combine structured and unstructured data into common business intelligence views 4. Have a marketing automation database (e.g., Marketo, Hubspot) 3. Have a process to integrate what is needed for reporting, business intelligence 2. Store and use only in source systems (e.g., Salesforce.com) 1. Don t look at external unstructured data 5% 1 15% 25% 35% 4 45% Percent of Responses Chart 7. How would you rate your current external unstructured data management capability? Current situation works well 2. Aware it could better 3. Would like to 4. Planning to enhance 5. Active project to enhance, but no active enhance plans Unstructured data from the web and social media is where the 3 V s of Big Data are gaining a lot of traction with early adopters within the Financial Services industry. P a g e 4

5 Marketers are creating highly segmented campaigns and using predictive analytics to increase conversion rates online and via social media optimization. Traders and portfolio managers are gaining broader insight into their investments, often before traditional data providers are aware of news or can update their feeds. Social media activity prompted the SEC to rule on April 2, 2013 that companies can use social media outlets to disclose material non-public information. Start-ups dedicated to using the power of data science and predictive analytics on vast databases to provide competitive intelligence are being funded by, and providing competitive advantage to, the largest investment banks and buy-side players. Very large research databases can be managed and mined more effectively on Big Data platforms such as Hadoop and NoSQL than on traditional relational platforms. Demographics of respondents The respondents were primarily middle to executive level Information Technologists at Investment Management firms, followed by asset owners and service providers. Chart 8. Company Type Company Type 27% 55% Investment Management Asset Owner (Pension Fund, Insurance General Account, Family Office, Corporate Treasury) Broker Dealer Service or Solution provider (Consulting, Vendor, Custodian) P a g e 5

6 Chart 9. Seniority Level Level of Seniority 36% 55% Owner/Executive/C-Level Senior Management Middle Management Chart 10. Business Area Business Area 18% 73% Operations Data Information Technology About PanoVista.co LLC PanoVista.co brings together deep financial services industry subject expertise with a portfolio of technology innovators. Together we partner to deliver practical data and analytics solutions that can add value with a short time to delivery. Please visit our website at or us at to learn more. P a g e 6

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