DataTwin: Enabling Enterprises With The Ability To Discover The Accurate Cost Of Their Services, Optimize It To Find The Right Profitability Mix & Enable Continuous Growth Across Huge Data Volumes
Raghavan Durgayandi & Dilip Ramadasan
With the sole goal of empowering finance teams to win and become true business enablers, OneIntegral Technologies has meticulously designed a robust platform, DataTwin, to better understand clients' finance and operations processes via pattern recognition, optimization, and simulation in order to build a strong roadmap ahead. Enterprises can discover the accurate cost of doing business from the huge amount of data that persists with them, identify the optimal profitability mix and enable ongoing growth. This is done by tracking income and costs on a daily basis, computing complex cost schemes, commissions and incentives, attributing indirect costs, auditing processes existing, and automating payouts for enterprises.
Take us on a short trip through
OneIntegral Technologies’ journey
over the years and is it stationed in the
IT services sector?
In past avatars, the most difficult task was ensuring that the underlying data required for financial reporting was clean and accurate. With multiple systems in use and gaps in processes, the data was prone to becoming stale and erroneous. OneIntegral arose from the necessity to ensure that data related to finance and operations is always clean, allowing for accurate decision making, improved efficiency, and lower risk. In our perspective, the solution to the problem was always to design products/platforms that could handle the intrinsic complexity of the problem. Though we believe in issue productization, with enterprises as our customers, there is clearly a services component at work, but it is more in the installation of our platform/products and ensuring the customers get the maximum benefit.
Tell us about your DataTwin platform and what makes it a unique & robust platform promising financial success?
DataTwin assists finance and operations teams in managing business transaction data and automating reconciliations across revenue, receivables, taxation, fixed assets, leases, and other data, allowing them to
spot risk sooner,better,and more efficiently. The platform boosts productivity by automating operations, decreasing friction and increasing efficiency.
DataTwin is a Finance and Operations Workspace for rapidly growing businesses that unearths truth in data, enables improved decision making, enhances profits, and facilitates continuous growth
DataTwin is a finance and operations workspace for rapidly growing businesses that unearths truth in data, enables improved decision making, enhances profits, and facilitates continuous growth. We now manage data across processes such as revenue assurance, cost attribution, partner/sales commission payouts, tax management, asset management, lease management, and financial reporting within the platform.
Dilip Ramadasan, Director
What are the latest technologies embedded within the products portfolio under DataTwin and what is the difference they have been making in the industry?
DataTwin under its hood employs future proof technologies like Graph Databases, Data Engineering at scale, AI/ML algorithms for Deep Learning, Rapid Application development and Automated Process Management.
Graph Databases: It helps us build knowledge Graph based products on our platform and eases our workload with respect to complexity, scale & manage ability of our code base. It remains performant even with the explosion of the volume of data. Another huge advantage it enables for us is also ensuring the elimination of data integrity issues in transaction data within our products.
Data Engineering for AI applications: Any application doing deep learning needs highly capable and robust capabilities/ tools to churn and clean
the data which is totally missing in the traditional systems. We have built Standard Data Reconciling & Reviewing capabilities which can be applied to any data into our platform, enabling us to improve the data quality drastically which in turn helps in better outcomes in standard analysis of data and further when AI/ML algorithms are applied for deep learning.
AI/ML Algorithms for Deep Learning: We use deep learning and deep graph learning algorithms to identify patterns in data which are otherwise nonrecognisable. We use these patterns found earlier to identify and predict growth patterns, course corrections needed to mitigate financial risks related to revenue, collections and taxation.
Rapid Application Development & Autonomous Process Management: We have built these into our platform to enable our customers to build better solutions and go to market faster through automation of manual processes, link system processes seamlessly as well and attain a state where all processes are managed autonomously without any supervision.
Could you acquaint us with a recent
challenging case reflecting the success
of the company’s intervention?
We have a data platform where we execute financial data reconciliations. The enterprise data is the main protagonist here. And, data has always been a difficult child. It is only because it is constantly snuggled by several processes and people throughout the organisation. A recent use case was the reconciliation of TDS for a BFSI enterprise. The four V's of datasheer amount of transactions (volume), variety of formats existing inside the organisation for the same data set (variety), data flowing in at different times(velocity), and data veracity were the key concerns that needed to be addressed. The data had to be cleaned from the company's book of accounts and the government's tax records and also the number of permutations and combinations possible between the company's book of accounts and government tax data. We have substantially aided clients in improving their grasp of the TDS receivables and reducing their financial risk in this area.
What is the future roadmap envisioned for OneIntegral Technologies? Where is it headed for the next five years?
There are numerous use cases in Finance and Operations across industries where the need for automation and data reconciliation is critical. In the next five years, we hope to be able to identify these and develop solutions on our platform to help our customers drive performance, innovation, and growth.