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Below Graeme Dillane, manager, financial services, InterSystems, offers insight into best practices in the financial services industry, highlighting where current weaknesses lie and how they can overcome.

Increasing trade volumes and periods of high market volatility create technology challenges for financial services firms. This is especially true for sell-side firms, which can experience extremely high transaction volumes, since they partition already high volumes of incoming orders into an even greater number of smaller orders for execution. At the same time, they must support a high number of concurrent analytic queries to provide order status, risk management, surveillance and other information for clients.

This requirement for multi-workload processing at high scale, coupled with the highest levels of performance and reliability, has historically been difficult to satisfy. Compounding the challenge, transaction volumes grow not only incrementally and within expectations, but can also spike due to unexpected world events.

A critical component of a sell-side firm’s technology infrastructure is its transaction management and analytics platform. The platform must be reliable and highly available. A failure, or even a slowdown of the platform, can have severe consequences as it can take many hours to rebuild order state and resume normal operations after a failure. In the meantime, the firm’s ability to process additional trades and provide order status is compromised and financial losses mount.

To successfully handle growth and volatility without performance or availability issues, the platform must balance transactional workloads with the concurrent analytic demands of downstream applications at scale. Financial services organisations, particularly sell-side firms, must process millions of messages per second, while simultaneously supporting thousands of analytic queries from hundreds of systems that must report on the state of orders while performing other queries.

Currently, in-memory databases are widely used, primarily due to their ability to support high-performance data-insert operations and analytic workload processing. However, in-memory databases alone are not an ideal platform for transaction management and analytics for several reasons:

Finding a Solution

So, given these challenges, how can financial services organisations find a solution that enables them to simultaneously process transactional and analytic workloads at high scale?

The answer comes in the form of the Hybrid Transaction/Analytical Processing (HTAP) database.

Traditionally, online transaction processing (OLTP) and online analytical processing (OLAP) workloads have been handled independently, by separate databases. However, operating separate databases creates complexity and latency because data must be moved from the OLTP environment to the OLAP environment for analysis. This has led to the development of a new kind of database which can process both OLTP and OLAP workloads in a single environment without having to copy the transactional data for analysis. HTAP databases are being used in multiple industries for their ability to uncover new insights, create new revenue opportunities and improve situational awareness and overall business agility for organisations.

The best HTAP database platforms deliver the performance of an in-memory database with the persistence and reliability of a traditional operational database. They are optimised to accommodate high transactional workloads and a high volume of analytic queries on the transactional data concurrently, without incident or performance degradation, even during periods of market volatility.

They have a comprehensive, multi-model database management system (DBMS) that delivers fast transactional and analytic performance without sacrificing scalability, reliability or security. They can handle relational, object-oriented, document, key-value, hierarchical, and multi-dimensional data objects in a common, persistent storage tier.

Moreover, the best of these embody features that make them attractive for mission-critical, high-performance transaction management and analytics applications. These include:

High-performance for transactional workloads with built-in persistence – The ideal scenario is to find a data platform that includes a high-performance database that provides transactional performance equal to, or greater than, in-memory databases along with built-in persistence at scale.

Data is not lost when a machine is turned off, eliminating the need for database recovery or re-building efforts. By using an efficient, multi-dimensional data model with sparse storage techniques, data access and updates are accomplished faster, using fewer resources and less disk capacity.

High-performance for analytic workloads – Seek out solutions that provide a range of analytic capabilities, including full SQL support, enabling you to use their existing SQL-based applications with few or no changes. Since the database stores data in efficient multidimensional structures, SQL applications achieve better performance than traditional relational databases.

Consistent high-performance for concurrent transactional and analytic workloads at scale - Ideally, solutions should provide the highest levels of performance for both transactional and analytic workloads concurrently, at high scale, without compromising performance for either type of workload. Since rising order volumes increase both the transactional and analytic workloads on the system, a data platform must scale to handle such workloads without experiencing performance or availability issues.

Positive Prospects

This article has highlighted that many financial services organisations are, for a variety of reasons, currently crying out for ways in which they can simultaneously process transactional and analytic workloads at high scale. Fortunately, help is now at hand. Thanks to the latest breed of data platforms for high-performance transaction management and analytics applications, both transaction processing and analytic queries are supported concurrently, at very high scale, with built-in durability and with the highest levels of reliability – and at a low total cost of ownership.

An Interview with:

·     Marc Vollenweider, Co-founder and Chief Strategist

·     Ashutosh Gupta, Co-CEO and Global Business Unit Head for Financial Services

·     Ravi Mehrotra, Co-CEO and Global Business Unit Head for Corporates and Professional Services

 Founded as a start-up in 2001, Evalueserve is a global professional services provider offering research, analytics, and data management services. The company is powered by mind+machine – a unique combination of human expertise and best-in-class technologies that use smart algorithms to simplify and automate key tasks. This approach enables Evalueserve to design and manage processes that can generate and harness insights on a large scale, significantly cutting costs and timescales and helping businesses to overtake the competition. The company works with clients across a wide range of industries and business functions, helping them to make better decisions faster, reach new levels of efficiency and effectiveness, and see a tangible impact on their top and bottom line.

This month, Finance Monthly had the privilege of speaking to Marc Vollenweider - Co-founder and Chief Strategist, as well as the company’s new Co-CEOs - Ashutosh Gupta and Ravi Mehrotra, who tell us all about the mind+machine concept and Evalueserve’s mission.

Marc, you have recently shifted your role from the being CEO to becoming Evalueserve’s Chief Strategist, can you tell us a bit more about this transition?

Marc: After spending about 16 years as Evalueserve’s CEO, I decided that it was time for the next generation to get involved in running the business, from an operational point of view. We decided to go with a Co-CEO structure, by splitting the role between Ashutosh and Ravi, while I shifted to a full-time board role, which allows me to concentrate on innovation-related projects. I am still strongly involved in the company, but instead of dealing with the day-to-day operations, I focus on proposing strategies and examining our next steps for the future.

Tell us about the experience of writing a book while running a global business? How have mind+machine and the book influenced Evalueserve and its business?

Marc: As soon as the idea about the book came about, it was clear that I wouldn’t be able to combine writing a book with my full-time CEO role. Thus, I took some time off, between March and June 2016, and wrote the book, while Ashutosh and Ravi got to practice running the business on their own. The experience of authoring a book on its own was extremely rewarding and I am proud of the final result.

The mind+machine concept is something that we started working towards 5 years ago, realising that a people-only approach was becoming too slow and too costly. We saw an opportunity for automating tasks and processes, and using machines to run repetitive tasks. We started coming up with workflow platforms, productivity tools, analytic engines, better knowledge management, etc. and fully rebranded the business. If we compare Evalueserve today to the company it was 5 years ago, I’d say that we have reached a productivity increase of about 30-40% on a per head basis. Mind+machine makes us faster - it makes the quality of our work better, and it gives our clients new capabilities to work with.

In today’s world, clients want to see innovation, so developing mind+machine has been essential for Evalueserve.

 

What are the benefits of a dual CEO-structure?

Ashutosh: At Evalueserve, we are very client-centric and also serve a variety of different industries. The fact that I have previously worked within financial services, while Ravi comes from a more corporate and consulting-oriented background, allows us to focus on these very different client segments. It also provides us with two different points of view and diverse ideas when it comes to dealing with common areas like HR, policies, marketing, etc. So the Co-CEO structure actually works really well for us.

Ravi:  Given the scope and scale of Evalueserve, being a very large and complex business, we want to make sure that we divide the different areas of the business and responsibilities between each other.

Additionally, the dual CEO structure, although not new, is still very rare and unusual. One of the reasons why it works for us is because, before embarking on this, we already had a very strong working relationship, with both of us having spent over 6 years in Evalueserve.

 

What are the main challenges that decision makers are facing today? How can mind+machine help overcome these challenges?

Marc: There are millions of decisions that need to be made in a company on a daily basis. And I’m not talking only about the decisions that the executive board makes; at Evalueserve we’re looking at decision makers at various levels of the company - from the CEO to the service technician. So for these millions of decisions, naturally, there’s a very large number of analytic use cases.

The problem of decision-making includes the logistics of having the right data at the right location, at the right time.  Then, these decisions must be transferred into a workflow where they get converted into actions and create impact. How does mind+machine help with all of this?

Unfortunately, when you look at how many decisions are being made within an organisation today, you’ll still see a lot of manual work. There are a few workflows, which make the life of decision-makers significantly easier. Mind+machine provides the client with the ability to crack the analytic use cases and then put a system or a machine in place to get them done on a recurring basis, as well as a platform that has all the necessary data feeds and analytics, and most importantly, links multiple end users and decision-makers in a collaborative way. This results in fast and efficient decision-making processes, with the knowledge management being done within the platform, so it doesn’t have to be redone every time.

 

Why do analytics matter for almost all types of businesses?

Marc: In today’s world, analytics are critical, solely because a lot of decisions within an organisation depend on diverse and complex data, which wasn’t so much the case up until 15 years ago. When it comes to decision-making, we need data that has been prepared, analysed and converted into insights and decision-ready output.

Today, every business needs an increasing amount of analytics because they improve the return on investment of many processes – it’s as simple as that.

 

How can one set up the use-case thinking in the company?

Marc:  Use cases have a number of implications for the whole company. Currently organisations tend to mingle everything together in a big pot - they set up central data scientist teams and large data lakes from where the teams try to come up with analytic output. However, this approach frequently leads to White Elephants not serving the end users’ needs well, often with negative Return on Investment (RoI). Companies should move to a culture of individual-focused analytic use cases.

A use case is not just an analysis; it comprises the data flow, the analytic engine, the UX (User Experience), the knowledge management for improving re-use, and the link to the overall workflow. Only in this way can the end users get what they need and achieve positive RoI.

It is my belief that this cultural change needs to be driven by the C-Suite, including the Chief Data Officer, who should jointly agree on putting this philosophy in place. Setting up the use-case thinking in a company should start with an agreement between the highest level executives, who should then drive it into individual units, so everyone gets into this mode of thinking over time.

 

How does Evalueserve use use cases to help its clients with decision-making?

Ashutosh: At Evalueserve, we have a whole collection of analytic use cases that are well-documented. Thus, when a new client comes to us with a business problem, we can easily leverage our database where we’ve cracked similar use cases before and come up with a specific solution, while saving our client a lot of time and money.

Ravi: Additionally, our use case hub is also helpful when it comes to showing our clients what mind+machine means. We’ve gathered all of these concepts that we are developing into a product, and have created a collection of use cases that we’re able to demonstrate to our clients at all times. The use case hub also enables the Chief Data and Chief Analytics officers to scale up their analytics capabilities by harnessing the knowledge, ensuring consistency and carefully selecting use cases for wider deployment and investment, based on the RoI.

 

What is your advice for successful leaders in the modern tech-focused world?

Marc: If we look at this from a ‘what lies beyond the horizon’ perspective, my advice is to get your feet wet - look into potential trends and then take well-informed, but still risky decisions. People nowadays are myopic when it comes to considering competition and future client needs and often get stuck in their current views. Being open to change and innovation and having a portfolio of new initiatives to play with is a critical element of strategy in today’s tech-focused world.

Ashutosh: Nowadays, it can be very difficult to stay on top of and respond to the changing technology trends, while running the business. So while it’s important to have a good technology strategy, it is also very important to communicate that strategy to your clients and throughout your organisation, so everyone shares the same goals and feels they can contribute to its success.

Ravi: The two things that I’d like to add are to firstly, be comfortable with uncertainty – we live in a very dynamic world and things change all the time. Leaders need to be flexible and always prepared for change.

Secondly, nowadays, it’s very easy to get so fascinated by the technology aspect; so my piece of advice is to not forget about the basics when it comes to leadership. i.e., technology and analytics are enablers to serve the broader business goals, and not the other way around.

 

Read more about mind+machine at: http://blog.evalueserve.com/ 

 

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