Tag: financial services

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erwin Expert Blog

Financial Services Data Governance: Helping Value ‘the New Currency’

For organizations operating in financial services data governance is becoming increasingly more important. When financial services industry board members and executives gathered for EY’s Financial Services Leadership Summit in early 2018, data was a major topic of conversation.

Attendees referred to data as “the new oil” and “the new currency,” and with good reason. Financial services organizations, including banks, brokerages, insurance companies, asset management firms and more, collect and store massive amounts of data.

But data is only part of the bigger picture in financial services today. Many institutions are investing heavily in IT to help transform their businesses to serve customers and partners who are quickly adopting new technologies. For example, Gartner research expects the global banking industry will spend $519 billion on IT in 2018.

The combination of more data and technology and fewer in-person experiences puts a premium on trust and customer loyalty. Trust has long been at the heart of the financial services industry. It’s why bank buildings in a bygone era were often erected as imposing stone structures that signified strength at a time before deposit insurance, when poor management or even a bank robbery could have devastating effects on a local economy.

Trust is still vital to the health of financial institutions, except today’s worst-case scenario often involves faceless hackers pillaging sensitive data to use or re-sell on the dark web. That’s why governing all of the industry’s data, and managing the risks that comes with collecting and storing such vast amounts of information, is increasingly a board-level issue.

The boards of modern financial services institutions understand three important aspects of data:

  1. Data has a tremendous amount of value to the institution in terms of helping identify the wants and needs of customers.
  2. Data is central to security and compliance, and there are potentially severe consequences for organizations that run afoul of either.
  3. Data is central to the transformation underway at many financial institutions as they work to meet the needs of the modern customer and improve their own efficiencies.

Data Management and Data Governance: Solving the Enterprise Data Dilemma

Data governance helps organizations in financial services understand their data. It’s essential to protecting that data and to helping comply with the many government and industry regulations in the industry. But financial services data governance – all data governance in fact – is about more than security and compliance; it’s about understanding the value and quality of data.

When done right and deployed in a holistic manner that’s woven into the business processes and enterprise architecture, data governance helps financial services organizations better understand where their data is, where it came from, its value, its quality, and how the data is accessed and used by people and applications.

Financial Services Data Governance: It’s Complicated

Financial services data governance is getting increasingly complicated for a number of reasons.

Mergers & Acquisitions

Deloitte’s 2018 Banking and Securities M&A Outlook described 2017 as “stuck in neutral,” but there is reason to believe the market picks up steam in 2018 and beyond, especially when it comes to financial technology (or fintech) firms. Bringing in new sets of data, new applications and new processes through mergers and acquisitions creates a great deal of complexity.

The integrations can be difficult, and there is an increased likelihood of data sprawl and data silos. Data governance not only helps organizations better understand the data, but it also helps make sense of the application portfolios of merging institutions to discover gaps and redundancies.

Regulatory Environment

There is a lengthy list of regulations and governing bodies that oversee the financial services industry, covering everything from cybersecurity to fraud protection to payment processing, all in an effort to minimize risk and protect customers.

The holistic view of data that results from a strong data governance initiative is becoming essential to regulatory compliance. According to a 2017 survey by erwin, Inc. and UBM, 60 percent of organizations said compliance drives their data governance initiatives.

More Partnerships and Networks

According to research by IBM, 45 percent of bankers say partnerships and alliances help improve their agility and competitiveness. Like consumers, today’s financial institutions are more connected than ever before, and it’s no longer couriers and cash that are being transferred in these partnerships; it’s data.

Understanding the value, quality and risk of the data shared in these alliances is essential – not only to be a good partner and derive a business benefit from the relationship, but also to evaluate whether or not an alliance or partnership makes good business sense.

Financial Services Data Governance

More Sources of Data, More Touch Points

Financial services institutions are at the forefront of the multi-channel customer experience and have been for years. People do business with institutions by phone, in person, via the Web, and using mobile devices.

All of these touch points generate data, and it is essential that organizations can tie them all together to understand their customers. This information is not only important to customer service, but also to finding opportunities to grow relationships with customers by identifying where it makes sense to upsell and cross-sell products and services.

Grow the Business, Manage the Risk

In the end, financial services organizations need to understand the ways their data can help grow the business and manage risk. Data governance plays an important role in both.

Financial services data governance can better enable:

  • The personalized, self-service, applications customers want
  • The machine learning solutions that automate decision-making and create more efficient business processes
  • Faster and more accurate identification of cross-sell and upsell opportunities
  • Better decision-making about the application portfolio, M&A targets, M&A success and more

If you’re interested in financial services data governance, or evaluating new data governance technologies for another industry, you can schedule a demo of erwin’s data mapping and data governance solutions.

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And you also might want to download our latest e-book, Solving the Enterprise Data Dilemma.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech.

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erwin Expert Blog

A New Wave in Application Development

Application development is new again.

The ever-changing business landscape – fueled by digital transformation initiatives indiscriminate of industry – demands businesses deliver innovative customer – and partner – facing solutions, not just tactical apps to support internal functions.

Therefore, application developers are playing an increasingly important role in achieving business goals. The financial services sector is a notable example, with companies like JPMorgan Chase spending millions on emerging fintech like online and mobile tools for opening accounts and completing transactions, real-time stock portfolio values, and electronic trading and cash management services.

But businesses are finding that creating market-differentiating applications to improve the customer experience, and subsequently customer satisfaction, requires some significant adjustments. For example, using non-relational database technologies, building another level of development expertise, and driving optimal data performance will be on their agendas.

Of course, all of this must be done with a focus on data governance – backed by data modeling – as the guiding principle for accurate, real-time analytics and business intelligence (BI).

Evolving Application Development Requirements

The development organization must identify which systems, processes and even jobs must evolve to meet demand. The factors it will consider include agile development, skills transformation and faster querying.

Rapid delivery is the rule, with products released in usable increments in sprints as part of ongoing, iterative development. Developers can move from conceptual models for defining high-level requirements to creating low-level physical data models to be incorporated directly into the application logic. This route facilitates dynamic change support to drive speedy baselining, fast-track sprint development cycles and quick application scaling. Logical modeling then follows.

Application Development

Agile application development usually goes hand in hand with using NoSQL databases, so developers can take advantage of more pliable data models. This technology has more dynamic and flexible schema design than relational databases and supports whatever data types and query options an application requires, processing efficiency, and scalability and performance suiting Big Data and new-age apps’ real-time requirements. However, NoSQL skills aren’t widespread so specific tools for modeling unstructured data in NoSQL databases can help staff used to RDBMS ramp up.

Finally, the shift to agile development and NoSQL technology as part of more complex data architectures is driving another shift. Storage-optimized models are moving to the backlines because a new format is available to support real-time app development. It is one that understands what’s being asked of the data and enables schemes to be structured to support application data access requirements for speedy responses to complex queries.

The NoSQL Paradigm

erwin DM NoSQL takes into account all the requirements for the new application development era. In addition to its modeling tools, the solution includes patent-pending Query-Optimized ModelingTM that replaces storage-optimized modeling, giving users guidance to build schemas for optimal performance for NoSQL applications.

erwin DM NoSQL also embraces an “any-squared” approach to data management, so “any data” from “anywhere” can be visualized for greater understanding. And the solution now supports the Couchbase Data Platform in addition to MongoDB. Used in conjunction with erwin DG, businesses also can be assured that agility, speed and flexibility will not take precedence over the equally important need to stringently manage data.

With all this in place, enterprises will be positioned to deliver unique, real-time and responsive apps to enhance the customer experience and support new digital-transformation opportunities. At the same time, they’ll be able to preserve and extend the work they’ve already done in terms of maintaining well-governed data assets.

For more information about how to realize value from app development in the age of digital transformation with the help of data modeling and data governance, you can download our new e-book: Application Development Is New Again.

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erwin Expert Blog

Data Governance 2.0 for Financial Services

The tempo of change for data-driven business is increasing, with the financial services industry under particular pressure. For banks, credit card, insurance, mortgage companies and the like, data governance must be done right.

Consumer trust is waning across the board, and after several high-profile data breaches, trust in the way in which organizations handle and process data is lower still.

Equifax suffered 2017’s largest breach and the fifth largest in history. The subsequent plummet in stock value should have sent a stark warning to other financial service organizations. As of November, the credit bureau reported $87.5 million in expenses following the breach, and the PR fallout plummeted profits by 27 percent.

But it could be said that Equifax was lucky. If the breach had occurred following the implementation of the General Data Protection Regulation (GDPR), it also would have been hit with hefty sanctions. Come May of 2018, fines for GDPR noncompliance will reach an upper limit of €20 million or 4 percent of annual turnover – whichever is greater.

Data governance’s purpose – knowing where your data is and who is accountable for it – is a critical factor in preventing such breaches. It’s also a prerequisite for compliance as organizations need to demonstrate they have taken reasonable precautions in governing.

Equifax’s situation clearly implies that financial services organizations need to review and improve their data governance. As a concept, data governance for regulatory compliance is widely understood. Such regulations were introduced a decade ago in response to the financial crisis.

However, data governance’s role goes far beyond just preventing data breaches and meeting compliance standards.

Data Governance 2.0 for Financial Services

Data governance has struggled to gain a foothold because the value-adds have been unclear and largely untested. After new regulations for DG were introduced for the financial services industry, most organizations didn’t bother implementing company-wide approaches, instead opting to leave it as an IT-managed program.

So IT was responsible for cataloging data elements to support search and discovery, yet they rarely knew which bits of data were related or important to the wider business. This resulted in poor data quality and completeness, and left data and its governance siloed so data-driven business was hard to do.

Now data-driven business is more common – truly data-driven business with data at the core of strategy. The precedent has been set thanks to Airbnb, Amazon and Uber being some of the first businesses to use data to turn their respective markets on their heads.

These businesses don’t just use data to target new customers, they use data to help dictate strategy, find new gaps in the market, and highlight areas for performance improvement.

With that in mind, there’s a lot the financial services industry can learn and apply. FinTech start-ups continue to shake up the sector, and although the financial services industry is a more difficult industry to topple, traditional financial organizations need to innovate to stay competitive.

Alongside compliance, the aforementioned purpose of DG – knowing where data is stored and who is accountable for it – is also a critical factor in fostering agility, squashing times to market, and improving overall business efficiency, especially in the financial services industry.

In fact, the biggest advantage of data governance for financial services is making quality and reliable data readily available to the right people, so the right decisions can be made faster. Good DG also helps these companies better capitalize on revenue opportunities, solve customer issues, and identify fraud while improving the standard for reporting on such data.

These benefits are especially important within financial services because their big decisions have big financial impacts. To make such decisions, they need to trust that the data they use is sound and efficiently traceable.

Such data accountability is paramount. To achieve it, organizations must move away from the old, ineffective Data Governance 1.0 approach to the collaborative, outcome-driven Data Governance 2.0.

This means introducing data governance to the wider business, not just leaving it to IT. It means line-of-business managers and C-level executives take leading roles in data governance. But most importantly, it means a more efficient approach to data-driven business for increased revenue. A BCG study implies that financial services could be leaving up to $30 billion on the table.

Although the temptation to just meet regulatory compliance might be strong, the financial services industry clearly has a lot to gain from taking the extra step. Therefore, new regulations don’t have to be seen as a burden but as a catalyst for greater, proactive and forward-thinking change.

For more best practices in business and IT alignment, and successfully implementing data governance, click here.

Data governance is everyone's business