The Challenge of Data Silos

Data silos occur when different departments or systems within an organisation do not communicate effectively, leading to isolated pockets of data. We have worked with private asset businesses that rely on manual reconciliation of updates of research systems, back office and settlements, and their security master. These silos can cause numerous problems:

• Inconsistent Information: Different versions of the same data can exist across departments, leading to conflicting insights and decisions. A company could only conduct credit reviews twice a year because it would take time to pull together and reconcile all the information to present to the committee.

• Inefficiency: Manual processes are often required to reconcile these data discrepancies, wasting time and resources. One client required three people only a quarter to spend three weeks reconciling and cleaning up data before they could do client reporting.

• Missed Opportunities: Without a unified view of data, organisations can miss critical insights that drive innovation and growth. Have you ever been so slow to turn around critical data queries that you miss out on a new client, upselling to a client or an investment opportunity?

The traditional approach to solving data silos has been to create a single master data repository. However, this idea is increasingly becoming obsolete.

The Declining Concept of a Single Master of Data

Having one master data repository, where all information is centrally stored and managed, once seemed like the holy grail of data management. Yet, this approach is often impractical and insufficient in today’s dynamic and complex data environment. Here’s why:

• Scalability Issues: As organisations grow, so does their data. Managing a single repository becomes challenging, leading to potential performance bottlenecks. Coordinating across several platforms, departments, or systems to ensure agreement on a change or enhancement to an existing data set can take a long time – months, if not quarters to get right.

• Flexibility Constraints: Different departments have unique data needs and may use specialised systems that are not easily integrated into a single repository. How often have you found that one department has a different definition of a simple concept, like a customer?

• Real-Time Challenges: In a world where real-time data processing is crucial, central repositories can struggle to keep up with the demand for instant data access and updates. Data management and governance appear to be concepts that slow down data delivery rather than speed it up.

Many organisations turn to data integration as a solution to data silos. Data integration involves combining data from different sources to provide a unified view and then passing that view of data into multiple downstream applications. You introduce the concept of what is upstream and downstream of your data master.

While integration is a step in the right direction, it only takes you part of the way:

• Latency: Integrated systems can suffer latency issues, where data is not updated in real-time, leading to outdated information. Most integration solutions rely on overnight batch processes with multiple dependencies. You have to coordinate when each process can run based on when data is available from each upstream source—any failure of the batch results in inconsistencies. Even when everything goes to plan, you have systems that start the day aligned, but time drifts as the day progresses.

• One way: Integration is just one way. Any downstream changes to data do not make their way back into the upstream systems. If you spot an incorrect assumption or information in the client reporting system, you fix it and don’t push the correct back into the source systems.

• Costly: Integration typically focuses on bringing together data from key systems because it is costly and time-consuming. You only go part the way in keeping data consistent, and it can still lead to costly decisions or mistakes. Manual reconciliation processes are introduced throughout the integration.

Synchronisation: Achieving Bounded Data Integrity

This is where Blackstone DataSync and our flagship product, DataSync, come into play. DataSync goes beyond mere integration, providing a comprehensive synchronisation solution that ensures data consistency, accuracy, and accessibility across your entire organisation.

• Real-Time Updates: DataSync ensures that all your systems are updated in real-time, providing accurate and timely data for decision-making.

• Bounded Data Integrity: By synchronising data across all systems, DataSync maintains data integrity within defined bounds, ensuring that all departments work with the same accurate information. You can manage precisely what is synchronised by funds and attributes as delta or full records. You have ultimate flexibility and control over how each system interacts.

• Scalability and Flexibility: Our solution is designed to grow with your organisation, easily accommodating new data sources and systems without compromising performance.

• Simplified Management: DataSync reduces the complexity of managing data, allowing your IT team to focus on strategic initiatives rather than troubleshooting data discrepancies.

Transform Your Data with DataSync

At Blackstone DataSync, we understand data’s critical role in your business. Our DataSync solution is designed to eliminate data silos, move beyond the limitations of a single master data repository, and provide a synchronised, real-time view of your data.

Say goodbye to data that stinks and hello to data that syncs. Let us help you unlock the full potential of your data, driving efficiency, innovation, and growth across your organisation.

Ready to transform your data strategy? Contact us at Blackstone DataSync to learn more about how DataSync can synchronise your data and empower your business. Connect with us on LinkedIn and visit our website for more insights and solutions.

“Blackstone DataSync stands out by enabling companies to deliver a three-year data quality strategy in just six weeks.” This statement often elicits a mix of intrigue and disbelief from potential investors or customers. The question that naturally arises is, how is such a feat possible?

Before we unveil the innovative approach we’ve developed at Blackstone DataSync, it’s crucial to grasp the shortcomings of traditional data quality strategies. These can often take three years, if not more, a timeline that is simply not viable in today’s fast-paced business environment.

Traditional data quality projects

There are a few things that you need to have to implement a successful data quality strategy using the currently available technology:

1. A senior sponsor: You need a senior leader championing the mission of data quality. Many organisations cannot make the case for data. This requires the individual to understand the cost if you do not have good-quality data. Many companies are doing nothing about the problem, losing 20-40% of their revenue. They need to get buy-in at the most senior level to ensure implementing this is a priority for your business.

2. A data programme: Implementing a robust data quality strategy is not a simple task. It requires a dedicated data programme, complete with a project manager, business analysts, data analysts, data engineers, data governance analysts, and a data architect. This comprehensive team, which can cost £2m and above per annum, is necessary to ensure the success of the strategy.

3. Data expertise: One reason data programs fail is that the projects do not have enough people with sufficient industry-specific expertise in the data they are working with. Financial services have some of the most complex datasets. To be a good data analyst in financial services, you need to understand the data in the context of your industry. You need an understanding of investment data and how the data entities connect to deliver value to your business. You need to understand regulatory use cases and reporting cycles.

4. Technology first: Many programmes I have been part of tend to discuss technology first rather than the data. Using a particular vendor or building that meets the latest data architecture standards is more important. It really doesn’t matter if you are using a data lake or databases. The most important thing is that the data is accessible and stored in a structured way, making it easily retrievable and extendable.

Planning and scoping

You can imagine a data programme being set up with a sponsor. It would take a couple of months to determine the scope of deliveries for the programme. It would take another month to get buy-in and sign-off for this scope.

Requirement gathering and design

Analysts would need three to six months to speak with the stakeholders, understand their requirements, document them, and review them with the technical teams before designing the work. In many corporate organisations, these designs must go to a technical design authority to be reviewed and signed off.

Even if you take an agile approach, you would have smaller iterations of the same thing. An analyst may take a week to gather and document the requirements in a Jira ticket before the data architect and engineering team can look at them for design. That process would be followed religiously for several sprint cycles.

Build, Test and Deploy

How long it takes to build, test, and deploy depends on how many rules you have developed and how complex the dataset is. Depending on the volume and complexity of the rules, the build process could take three months to a year. A major challenge in testing the build is having a dataset that can be used to test those rules. Many companies do not want to use “production” data in the development processes and most likely will not have the tools to create synthetic data. Projects stall here because of insufficient data to test the validation rules.

BAU

Finally, those rules must be deployed to a team to manage the progress of exceptions. Hopefully, the team will have suitable operational processes to deal with this, which will be manageable by the volume of exceptions generated by the tool.

You also need the ability to monitor, measure, and report to a data quality committee on the work being done to improve the quality of data.

How is DataSync different?

DataSync comes with over 100 common data connectors out of the box. This means we can connect to most systems within minutes and configure the synchronisation activities. Systems can be configured in numerous ways to meet the client’s behavioural standards. For example, they can be configured to only receive changes and never contribute a change or perhaps only receive delta rows from DataSync.

DataSync uses deterministic AI to understand and structure your data to our internal model automatically. We have built an extensive library of data specifically for financial services. Within minutes of connecting to your data, DataSync will have a first pass of the data, profiling it, understanding what it is, classifying it and building data quality rules.

The first pass will not be perfect, so the first week will require us to update our models and libraries to accurately capture and map your data effectively.

At the end of this week, we will create a corrected version of data from each system to which DataSync was connected. DataSync can infer correct information from other data points within a set of information. It reconciles data between systems, looking to make them consistent. It will take a few weeks for us to review and explain the impact that DataSync will have on the data if it is run in production. We would give our clients time to review and agree with the changes before running DataSync in a production environment.

Running DataSync will ensure data consistency between the systems it connects to and data cleaning. If data that is not expected is changed, DataSync can be used to prevent or correct this change. DataSync can be configured to run in real-time or as part of a patch process.

To learn more about how DataSync works, contact Ovo at ovo@blackstonedatasync.com.

We designed DataSync with your specific needs in mind. Our focus was on helping organisations expedite their data strategy within weeks. We recognised that the primary reasons for data program failures were the excessive time and effort they consumed and the lack of expertise to implement them in every organisation. This is where DataSync stands out, offering unique solutions to these common challenges.

DataSync, our unique offering, is your instant accelerator. Blackstone DataSync has set itself the task of delivering a 3-year data quality strategy in just 4 weeks. We have developed a platform that swiftly connects to multiple applications, leveraging artificial intelligence to comprehend the data, create and execute its own data quality rules, and ensure consistent data across all systems.

So, imagine we install DataSync between your applications in as little as a day. It analyses your data across your applications, profiling and implementing data quality rules. In our first week, we will refine the outcomes with you, and we will monitor and test the data behaviour over a period of time to refine the models and ensure they are not overcorrecting or under-detecting.

With DataSync, there are no complex rules for you to write, no need for ETL layers or integrations, and no requirement for a data architect, analyst, or engineer. Best of all, DataSync is built on a Kafka infrastructure, which means it handles all the cleaning and syncing of information in real time. A change in one application can be seamlessly integrated into DataSync and synced with all applications within seconds.

By adopting DataSync, you can say goodbye to data inconsistencies and lengthy 3-year data programs. We believe this will significantly boost your organisation’s productivity, potentially by up to 40%.

This is just the start. Blackstone DataSync is working on a fully integrated data co-pilot to improve an organisation’s access, literacy, and effectiveness when using data.

For a conversation on DataSync, email ovo@blackstonedatasync.com.

With their manual and time-intensive nature, traditional data validation and correction methodologies increasingly show their limitations. These processes, prone to human error, struggle to keep pace with the sheer volume and velocity of today’s data generation. In this context, the role of deterministic AI emerges as not just innovative but essential. Unlike its counterpart, generative AI, which is celebrated for creating new information, deterministic AI specialises in enhancing the accuracy of existing data. Its application in investment management promises a future where data quality can be assured with unprecedented precision and efficiency.

Deterministic AI distinguishes itself through its rule-based logic, sophisticated pattern recognition, and robust error detection and correction algorithms. This suite of capabilities ensures that every piece of data, regardless of its complexity, is scrutinised and refined to meet the highest standards of accuracy. Such an approach is indispensable in areas fraught with complexity and high demand for precision, like investment data analytics.

One of the most compelling applications of deterministic AI is its ability to automate bond analytics validation and ensure precise bond pricing. Traditional methodologies, bogged down by their inherent inefficiencies, often fail to accurately capture the nuances of market conditions and bond covenants. Deterministic AI, however, leverages rule-based algorithms to systematically validate and correct data in real-time. This streamlines the decision-making process and significantly mitigates the risks associated with faulty analytics and mispricing.

Transformative Benefits for Investment Management

Adopting deterministic AI in data quality management tools will revolutionise how firms approach data integrity. Investment firms can achieve a previously unattainable level of data accuracy by automating critical processes such as data detection, profiling, cleansing, and continuous monitoring. This leap in data quality comes with the added advantages of scalability and cost efficiency, liberating firms from the constraints of manual data checks and the associated overheads.

For the investment management industry, the implications are profound. Enhanced data accuracy reduces the risk associated with pivotal investment decisions. Operational efficiency is markedly improved as professionals can redirect their focus from data quality maintenance to strategic analysis and decision-making. Moreover, access to reliable, high-quality data equips firms to adapt to market changes swiftly, securing a competitive edge critical in the fast-paced world of investment management.

As we navigate the complexities of modern data management, the integration of deterministic AI into data quality tools stands as a beacon of innovation. It offers businesses, especially those in the investment management sector, the means to ensure the integrity of their data, fostering improved decision-making and a formidable competitive advantage. The era of deterministic AI is not on the horizon—it is here. Embracing its potential will define the leaders in the quest for data excellence and, ultimately, the broader investment management landscape.

In conclusion, as the digital landscape continues to evolve, the reliance on traditional data management methods becomes increasingly untenable. Deterministic AI offers a path forward, promising a future where data quality is no longer a bottleneck but a catalyst for strategic success. For investment management firms, the message is clear: the time to act is now. Embracing deterministic AI is not just an investment in technology—it’s an investment in the future.

For over fifteen years, I have collaborated with numerous organisations, all united by a common goal: to establish a consistent data framework by creating a centralised ‘single source of truth.’ This model, often called the ‘hub and spoke’ model, envisions a central hub disseminating data to various spokes. This approach has yielded significant benefits in some cases, streamlining data flow and enhancing consistency. However, it’s not a one-size-fits-all solution. In certain organisations, the effectiveness of this model rapidly diminishes as data transitions from the central hub to the spokes, where end-users frequently alter it. This modification leads to discrepancies, spawning multiple ‘versions of the truth’ and, consequently, complaints about data integrity.

This phenomenon prompts a critical question: how can we maintain data consistency across diverse financial systems such as finance systems, risk management systems, performance systems, and order management systems? The answer might lie in evolving our perspective and drawing inspiration from technological advancements in other domains.

Consider the transformation in data synchronisation seen in personal technology over the past two decades. The advent of the first generation of smartphones, operating on Windows and utilising tools like Microsoft ActiveSync, marked the beginning of data synchronisation between devices. However, these early methods necessitated a ‘master’ device, which would override the other in case of discrepancies. This concept has been profoundly transformed in today’s interconnected world. Now, seamless synchronisation is not just a convenience but an expectation. Whether resuming a show on a different device or accessing photos across platforms, data synchronisation happens in real-time, with no ‘master’ source.

Why shouldn’t the financial services sector aspire to this level of synchronisation? This is where DataSync comes into its own, and it’s time to embrace it. Envision a scenario where each implemented system pushes out data upon modification (ideally through an API) and integrates changes seamlessly. Imagine all these systems interconnected through a cloud-based data lake, employing event-based logic to trigger actions upon data receipt or alteration. This data lake would recognise congruent data from different sources and reconcile it to deduce the most up-to-date ‘version of the truth,’ subsequently disseminating this verified data across all necessary systems. This would eradicate the need for a ‘data master,’ replacing it with a dynamic, real-time ‘data sync.’

Moreover, this approach allows for robust governance. Source systems can be equipped with controls to ensure that alterations are legitimate and authorised, creating a cascade of validated changes across the network. Audit trails, mandatory explanations for modifications, and system alerts informing stakeholders of changes and their triggers all contribute to a transparent, accountable, and efficient data management ecosystem.

In conclusion, it’s not just about adapting to the times; it’s about leading the charge towards a future where financial services data is managed and dynamically synchronised, mirroring the seamless interconnectivity we experience in our daily technological interactions. It’s time to replace the outdated ‘data master’ with a sophisticated, integrated ‘data sync’ model, paving the way for an era of enhanced efficiency, accuracy, and transparency in financial data management.

It’s not uncommon for many to mistakenly equate a technology strategy, like implementing a cloud-based data platform, with a comprehensive data strategy. However, while vital, technology is merely one facet of the broader picture. The adage “Data is the new black gold” echoes ubiquitously. Yet, the intrinsic value of data and the formulation of a strategy to harness it warrants a deeper exploration.

The cornerstone of a robust data strategy is an intimate alignment with your company’s overarching strategy or vision. Absent of this alignment, efforts to devise a standalone data strategy are akin to navigating without a compass—costly and directionless. So, how does one embark on this journey effectively?

At its core, the essence of data is insights. These insights can be categorised into three fundamental types:

1. Descriptive Data: This provides a narrative of what has happened or is unfolding within your business. Clarity on your company’s strategy is paramount here, ensuring that the metrics and narratives you derive are aligned with your business objectives.

2. Prescriptive Data: This goes beyond mere analysis, suggesting actionable steps. It’s about designing data-driven decisions, like adjusting portfolio risk based on asset concentration or ensuring liquidity to meet financial obligations.

3. Predictive Data: The crown jewel offers forecasts about future trends or outcomes. However, this domain is intricate and demands a mature data ecosystem akin to venturing into the pool’s deep end—you must know how to swim.

Underpinning these insights is the bedrock of trust in your data. Trust is fostered through rigorous data quality, governance, and management. It encompasses policies, control mechanisms, monitoring, stewardship, and rectification processes. Consistent interpretation and accessibility are also pivotal, demanding robust data engineering and infrastructure.

Let’s revisit and delve deeper into these elements:

• The crux of value in data lies in insights derived from analytics and data science. Whether it’s understanding fluctuations in operational costs or anticipating revenue trends based on external factors like seasonal shifts or market dynamics, the goal is to transform data into decisions and foresight.

• The reliability of these insights hinges on your strategy for ensuring data quality. This encompasses sourcing, ownership, usage, and establishing a single source of truth and a comprehensive data dictionary.

• Data visualisation has become indispensable. It bridges the gap between raw numbers and actionable intelligence, as exemplified by visualising cab ride terminations during lockdowns. Such visual narratives can unveil patterns and insights that raw data might obscure.

• Access to data should be seamless yet secure, ensuring the right stakeholders have the information they need at their fingertips and fostering a culture of data-driven decision-making.

• Finally, a coherent data strategy is about the ‘what’ and the ‘how.’ It’s about choosing the right technology within budgetary and timeline constraints, harnessing the right skills, and meticulously tracking progress and impact, akin to ensuring a supermarket’s success by stocking shelves and ensuring products meet customer demands and drive sales.

In conclusion, a data strategy is not a static document or a one-time initiative; it’s a dynamic, evolving journey aligned with your business’s heartbeat. It’s about ensuring that every byte of data, every insight gleaned, and every decision made is a step towards realising your company’s vision and mission. Suppose this resonates with you, or you seek a navigator to steer your organisation’s data strategy. In that case, we are here to embark on that journey with you.