Creating Smart
Data Insights
Through Intelligent
Data Integration
This report is based on the insights of the industry
experts who participated in the FIMA 2015 panel
“Solving Big Data Mysteries with Smart Data Clues”.
Meet the Experts
In order to address the growing importance of data governance and management in an increasingly data-driven financial environment, a panel of data management professionals met at FIMA 2015 to diagnose common data management issues in an engaging live forum. Their expertise and case examples are the basis of the analysis contained in this report. In addition, David Newman, SVP of Enterprise Architecture and IT Strategy at Wells Fargo contributed insight on topics covered by the panel.
Mark Temple-Raston
Global Head of Enterprise Architecture, Chief Data Officer
Citigroup
Anastasia Dokuchaeva is Director of Global Regulatory Solutions at
FactSet. She has spent the past several years setting up a product strategy around regulations such as Solvency II, BCBS 239, AIFMD, and others. Having developed extensive knowledge of market data during her 10 years of experience in the financial industry, Anastasia aims to look at regulatory requirements from all angles – data quality, methodology, sourcing, product packaging, and partnerships.
Moderator
Panelists
Krishna Vaidyanathan
Vice President, Corporate Research and Data Analytics
Fidelity Investments
Josh Lee
Director of Global Industry Marketing
Informatica
Saad Chafki
Associate Vice President, IT Services
Manulife
Yagmur Kanbas-Campbell
Senior Vice President,
Internal Audit, CCAR Data and Technology
Citigroup
Challenge Analysis
Regulation requirements and the desire for a greater understanding of risk are both behind the growing importance of connecting big data. However, it is important to understand that simply collecting big data does nothing to improve the quality of insight within a financial institution. It is only through identifying relevant data identifiers and breaking down the barriers between siloed repositories of information that useful intelligence can be gained. To this end, how does one go about conceptualizing the beginning of this task? The process of creating data insights starts with knowing the questions you are trying to answer, then carefully managing the data tags and integration required to paint a picture, often incorporating external reference data along with proprietary information. In this way, an organization is able to solve big data mysteries with smart data clues. While there is currently no such thing as a “pure” big data environment, firms realize the need to bring their data up to this new standard. As silos are broken down and linked internally, organizations can begin to build a more integrated and insight-driven future.
The decreasing cost of data storage is enabling a broader scope of data-driven applications to become a reality.
Key Trends
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For companies to stay competitive, formerly siloed and potentially underutilized data must be organized and connected to create insight.
Smart data requires balance between compliance and internal goals. Creating a reasonable number of data tags is also important.
Creating Smart Data Insights Through
Intelligent Data Integration
As the costs of storing large datasets become more competitive, it is increasingly feasible for organizations to leverage big data in new and interesting ways. An example of this type of application is “triggering”, wherein massive amounts of transactional data are analyzed in order to alert a bank of customer activity indicating a change in their relationship towards the bank. For example, if a customer makes a series of large withdrawals, the bank may take steps to prevent the customer from leaving. Conversely, a series of large deposits could trigger a with the customer, offering various new investment options to make the most of their money. In the past, monitoring bank activity at this level would have been prohibitively expensive based on the sheer amount of data storage required. Now, this type of example is driving a new data management paradigm, challenging firms to break down silos and begin to analyze formerly unstructured data.
One of the major challenges facing financial institutions adapting to the big data paradigm is the fact that they are frequently looking at large, siloed repositories of unstructured data with the task of managing integration, either across internal business units, or potentially across organizations.
Before this task can be readily approached, it’s
important for data managers to think strategically about
“Big Data technology allows organizations to consume and store large quantities of both structured and unstructured data for mostly analytical processing. Some potential applications include financial crime analytics, operational analytics for end-to-end infrastructure monitoring, regulatory compliance, assessments of the influence of external trends and market conditions upon customer investment portfolios, and customer insight analytics.”
– David Newman, Strategic Planning Manager, Senior Vice President Enterprise Architecture and IT Strategy,
Wells Fargo
“A few months back, I had an opportunity to speak with the CDO of one of the G-SIBs regarding challenges financial services companies face when trying to understand the data they have. This particular bank has 90+ silo databases; however, the biggest problem
How are big data applications changing the game?
What are some of the challenges surrounding the creation of
smart data insights?
“Effectively connecting and aggregating data means firms will not only save on data management, governance, and administrative costs, but will also be better positioned to understand firm-wide risk exposures, answer regulatory-related questions, and consolidate data usage across all verticals. This will create consistency, transparency, and efficiency in a business where a single-version of truth prevails.”
– Anastasia Dokuchaeva, Director of Global Regulatory Solutions, FactSet
“There are two realities regarding data integration within the big data paradigm. One is externally influenced, and the other is internally influenced. One of the key conclusions following the financial crisis is that data within financial institutions has been highly incongruent across organizations and business units when viewed from a regulatory perspective. This has led to global regulatory initiatives such as BCBS 239, also known as enterprise risk data
aggregation, which states that financial institutions must develop strategies to ensure alignment to enterprise business glossaries, taxonomies, and metadata standards. The other more internally driven reality is the recognition that in order to draw conclusions from big data, disparate
data elements that might share common meaning but are labeled and tagged differently must be systematically harmonized in order to be effectively integrated for analytic and reporting purposes. Solving the data integration challenge will require enterprises to formulate new data management strategies and make investments in new technology.”
– David Newman, Strategic Planning Manager, Senior Vice President of Enterprise Architecture and IT Strategy, Wells Fargo
“Obtaining insights from customer communications in the form of unstructured text will contain a
wealth of information that customers voluntarily share, but rarely effectively analyzed. This is often due to challenges in natural language processing and in fully understanding the context of the specific communications. However, NLP, machine learning, and semantic technologies are evolving to the point where many of these challenges will be remediated.”
– David Newman, Strategic Planning Manager, Senior Vice President of Enterprise Architecture and IT Strategy, Wells Fargo
Linking databases demands the correct approach to symbology
in order to both meet compliance requirements and provide the
right level of visibility.
When linking databases, the first required step is recognizing the correct symbology that can identify and mark common traits. However, while firms may already have internal symbology rules in place, new requirements, such as BCBS 239 compliance and LEI compatibility (which aims to create common identifies for financial institutions), require looking beyond what has sufficed for internal usage in the past. Data managers should be thinking proactively about what common identifiers can be used across databases, potentially within the context of globally used data. Often, internal data requires enrichment to create the right types of insights, so thinking about how it can be managed in such a way as to allow communication with external databases is another priority.
In order to meet the challenges of standardization and large-scale integration, we must be sure that the data we are dealing with is truly understood. For example, in the event that certain transactions within a dataset are open-ended by nature, the data framework must allow for this, so that data quality checks can still be run with confidence.
“I work in a tiny data analytics group, and I essentially have to convince my partners that data analytics are necessary. People have asked me, “Are you going to bury that big data environment or are you going to bring all the data in?” And my response has been that I’ve taken a hypothesis-oriented approach to data integration where I collect questions that our risk professionals and compliance professionals are asking over and above what the regulators are asking. And then we score these to say, “Which are ones that are the riskiest that we should analyze?” And we go after the riskiest first. So we have not taken a boil-the-ocean approach. We’ve gone towards a more targeted way of using big data, and using data analytics.” – FIMA Panelist on the importance of
evaluating data identifiers
“The principles like BCBS 239 guide firms to become better at connecting big data to drive analytics. Connectivity starts with symbology; where concordance across various identification schemas is often the challenge. Understanding the relationship between “things” is equally important. And once all of it is overcome, only then can businesses start to understand what data they have, what analytics they can produce, and what questions they can answer. Along the way, it also exposes the gaps in data that have to be enriched.”
Within a single data repository, it is often possible to run analysis and gain useful information. However, increasing standardization of data identifiers and integration into a big data environment creates the means for more compelling stories to be told by cross-repository analysis, giving a birds-eye view of previously isolated data assets. In a similar way, internal data can be coordinated with external reference data, to enrich insights through the creation of a more complete set of variables and data points.
“What is important and what has worked for me is actually knowing your data. If you don’t know the meaning of the data, find people who do; get some engineers or subject matter experts, put them in a room, and try to ask them the question, “What is the meaning of your data, and how is it connected in the distributed systems?” Find common attributes that would actually work for you to be able to create meaning behind the data. Before you even approach your big data, you really need to define it, and connect all of these dots so that you can actually run some intelligent rules to be able to analyze.”
– FIMA Panelist on defining data for integration
Integrating data from multiple sources opens up the opportunity
to tell stories through higher-level analysis.
Key Recommendations
The following recommendations are prescribed in order to assist data managers in adopting a proactive stance towards compliance, as well as a stronger approach to utilizing internal data assets. The potential uses for big data are ever-expanding, but for a firm to effectively act on emerging possibilities, the state of their internal data management must be up to standard. If not, the process of retroactively integrating massive datasets can become a roadblock that prevents firms from keeping pace.
Before connecting data, it is beneficial to take a step back and evaluate what the data really contains.
A more thorough understanding of the data contained within a repository will cut down the time and difficulty involved in preparing it for specific applications. It is also essential to validate the quality of the data during this step in order to avoid issues caused by flawed data quality within a larger system. On the scale of a big data environment, such issues can force costly steps backwards.
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Identify critical data elements required for the creation of insights and compliance. Internal data identifiers may or may not have any scope for integration into a broader data environment, therefore when integrating formerly siloed data, strategic thinking is required to determine the correct symbology to connect the information. It is also important to think of how that information can be classified in a way that complies with regulatory standards.
As silos are broken down internally, consider the ways that internal data can be coordinated with external data resources.
While uniting formerly siloed internal data can bring a firm many steps closer to the applications and insights that they are seeking to create, filling in the gaps around internal
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