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Business leaders are learning extraordinary power and strength of data: Tim Jennings, Synechron

Business leaders are learning extraordinary power and strength of data, according to Tim Jennings, Synechron

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Pradeep Chakraborty
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Synechron is a global digital consulting firm for large financial services and technology firms. It is leading modernization and digital optimization journeys with expertise that spans consulting, data, design, cloud, and engineering across various industries.

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Tim Jennings, Senior Director of Global Data Practice, Synechron, tells us more. Excerpts from an interview:

DQ: Can you give us an overview about data monetization, and how will it impact the BFSI sector?

Tim Jennings: Data monetization refers to the methods of using data with monetizable characteristics to obtain quantifiable economic benefits. The data monetization market, as projected by several prominent firms, is predicted to grow to between US$15B and US$17B by 2030.

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Data is the newest and most abundant – and therefore, most valuable commodity for businesses of all sizes and types. Estimates show that 4.4 million petabytes of data is traded annually. Data is everywhere, and advanced data-centric tools and technologies have made data collection easier than ever before.

Tim Jennings

Tim Jennings

Business leaders are learning the extraordinary power and strength of data, and how it can be leveraged for a variety of business situations, uses, and strategic planning. This includes identifying patterns, critical trends, and key insights used for predicting future business needs. Data, whether structured or unstructured can be used to better understand different aspects of a business, the overall business environment and can be utilized to gain a competitive advantage.

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However, businesses with access to large volumes of data are still trying to get their arms around ways to ingest, enrich, transform, manage, and access data across multiple offices, business lines, and users’ needs. This includes transactions, customers, payments, and public data.

Financial services firms, such as retail and commercial banks, asset managers, retirement advisors, and insurance companies are looking for ways to explore the capabilities that collective business data can offer. These organizations are looking for ways to access data across internal functions and IT solution boundaries in order to derive insight from the length and breadth of their corporate information assets.

DQ: How can banks and financial institutes leverage data monetization to grow their business?

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Tim Jennings: There are several dimensions that banks and financial institutions can explore to leverage direct data monetization. We see cloud service providers becoming interested in FS data, and establishing partnerships with, for example, CME, Goldmans, and LSEG where monetization is at the heart of the deal.

This is partly about finding new markets for data (i.e. expanding the customer base) and partly leveraging the existing CSP tooling/processes for improved efficiency, and creating or exposing new data delivery channels. This does not necessarily require CSP involvement especially for firms with a valuable dataset; there are several ways that this can be brought to market.

Another approach to monetization is to use the data to drive analytic insights, and to monetize either the process itself or the insight generated. This might include, for example, portfolio analytics tooling, where clients will pay to access the tooling across their data/trades with the firm. In this case, solutions that allow clients to bring their own data to the firm’s analytic solution in a secure and private way also become interesting.

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Just managing data effectively creates opportunity for monetization. For example, a firm that understands all the interactions a client has across their different divisions is better placed to offer the best advice, and thus increase business and improve retention for that client. For example, the lending department may not want to give a client an unsecured loan, but the credit card business may be willing to offer an alternative credit line. By knowing this, the use of data has ensured the client revenue did not leave the firm.

There are also internal monetization opportunities, where the value of the data is expressed in the decisions and strategies that the firm makes. Using data to build an informed strategy and to instrument that strategy so you can track progress is a powerful and effective approach.

DQ: What are the challenges these institutions face, while collecting and processing data, as a result affecting the data monetization strategy?

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Tim Jennings: Here are some of the notable challenges:

Data readiness

  • Data is locked in organizational silos, closed data environments and duplicated causing data accessibility and availability challenges.
  • Data privacy and regulatory restrictions on storing and sharing client data necessitates appropriate handling and use of data.
  • Data quality issues driven by lack of completeness, correctness and consistency in data prevents organizations from achieving best outcomes out of business use cases.
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Business relevance

  • Lack of awareness and understanding of the value of data across business users, making identification of relevant business use cases difficult.
  • Lack of data democratization in the organization due to limited self-service tools limits the spread of value generated from data across the organization.
  • Difficulties with managing the pace of innovation in data and analytics space leave organizations unable to monetize data effectively, unless appropriate data-related investments are made.

Technology maturity

  • Difficulties in engineering diversified and voluminous data in real-time prevents the organizations from scaling up monetization efforts.
  • Lack of technical skills and tools to deliver accurate and actionable insights necessitates making investments to acquire or build such skills and tools.
  • Limited capabilities to integrate data and analytics in customer offerings limits the organizations from showing value of monetization efforts to their target customers.

DQ: How can one overcome these challenges?

Tim Jennings: Overcoming these challenges requires a combination of strategy, governance, and architecture/engineering.

Use strategy to set a clear and common goal for how you intend to monetise the data. For example, if your business model is to sell data externally then you need to articulate the client market, the distribution channels, the commercial levers, the support model, etc. On the other hand, if you are aiming to unify the firm’s data and break down the organisational silos then you will need a strategy around a marketplace, and to define the engagement model for both publishers and subscribers’ type, you will need to incentivise the data producers who will be taking on more work in this model.

Use governance to ensure you understand what data is available, what condition it is in, who the owner is, what rules must be applied to it, what it can/cannot be used for, etc. In FS you cannot safely monetize data which you do not understand. Data governance maturity is needed to deliver the strategy irrespective of the type of strategy you have selected.

As you try to unite data from sources across the organisation you will need to use governance to drive standardisation and integration of common data items and terms. Note governance may extend beyond the traditional data governance scope; if your strategy is to move towards a data-as-a-product model then you will be changing the business operating model by creating new roles in the organisation.

As firms move to more formal data monetisation consumption patterns (whether feeding to internal or external users) we’d expect to introduce data contracts as another governance feature, and this encompasses data quality certifications and facilitates Data observability.

The architecture needs to unlock the data in the organisation. Even with supposedly common approaches (eg. Data Mesh) we see very different architectures being deployed in order to realise different strategic goals. It is through the architecture and engineering that the strategy and governance are united to provide a service realises the business ambition.

There are some standard approaches and solutions, and some well-recognised vendors who offer components of a data platform for monetization. There are some elements that could be expected for the vast majority of solutions: a data marketplace, a data catalogue, differential privacy solutions, access management solutions, a search capability, data storage, analytic environments, etc.

The specifics will vary from firm to firm and are optimised to meet different business cases. Where the solution is novel for a firm, we would recommend working with a suitable advisory and technical partner who can bring the required industry experience. This will de-risk the project significantly.

DQ: How is Synechron helping BSFI brands in setting up a data monetization strategy?

Tim Jennings: Synechron has been working with firms worldwide to realize their data monetization strategies. Our global data practice provides comprehensive consulting and technical delivery services from strategy to governance, architecture engineering and operations, and analytics and data science. We work exclusively in FS, which means we understand the data, the environment, the problems, and the constraints faced by our clients.

We have just announced our data monetisation accelerator which is based on this experience and provides working solution frameworks for some of the key elements. For example, this includes:

  • A Marketplace accelerator, which illustrates the Find/Try/Buy process for data discovery.
  • A clean-rooms solution to allow firms to unite analytics, data and compute resources, without compromising the privacy of their data.
  • A selection of analytics solutions, which provide insight based on specific data sets. This includes fraud detection in payments services, wealth planning and scenario building, and a retailer insights package.

We have delivered a wide range of data monetization projects and elements: data mesh, data management, data consolidation, data consistency, data quality, data operations. Synechron can bring this real-world experience to our clients, which improves delivery times and de-risks project outcomes.

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