Modern data analytics technologies have revolutionised companies that want to exploit the power of their data. Such tools allow business users to coherently express their logic over millions of rows of data, across multiple data sources and hundreds of rules.
Since inception we have developed an unparalleled track record in delivering solutions to our clients across the financial services industry, and more recently in a number of other verticals including retail and utilities.
The ethos remains the same, combine deep industry knowledge with cutting edge analytics techniques and technologies to deliver game changing outcomes.
Most organisations have or are recipients of fragmented data. Data for different business lines, divisions or geographical regions may reside in highly disparate repositories, in different formats and without common referencing. This fragmentation has prevented organisations from developing holistic views of their operations and interactions with their clients.
The latest data tools allow us to identify and isolate the relevant data sets, tag and normalise data. We are then free to express our business logic, derive value by identifying revenue opportunities, isolating cost concentrations or by improving efficiency processes through automation.
We let the data define the path to the solution, not user assumptions or sample data.
Data analytics principles
Leveraging the pragmatic approach described above allows us to accelerate the time-to-delivery of our solutions. In a rapidly changing competitive and regulatory environment, this cutting edge approach, is essential in driving value.
Low IT burden: we just need some data and the business users can start their analysis rather than burdening IT with change request after change request to query, clean and join up data. Business validated processes can either be directly put into production or used as a validated prototype saving time and resources.
Easily access disparate sources of data
We know that data is invariably in different places and different formats so we have out of the box connections to data sources without a need for a strategic warehouse, data model definition or a DataBase Analyst’s time.
Joining data together
The power to associate data from a number of disparate sources allows us to start to combine previously unconnected data sets in a highly efficient manner. This enrichment of previously disparate data sets allows us to start to construct complex data analytics to reveal previously unseen patterns in the data and isolate sources of value for our clients.
Drag and drop visual modelling
Pre-built analytic building blocks offering powerful functions which can be joined together by a drag and drop interface. So layers and layers of business rules that create a complex data flow can be logically organised and drag and drop means data can be rerouted to follow the most interesting highlights of any data discovery.
There is no getting away from reconciliations: they happen whenever two related data sets are joined. But keeping up with the layers of business rules, exceptions, input variations and formats makes this hard.
Reconciliations are often in two forms i) transactional (numbers and rules) and ii) text-based such as inventory and client names. Each have their own challenges.
CPRA’s data analytical tools allow it to focus on the data analysis, rather than the technical aspects of handling the data, creating a living picture of the data flow: which is precisely what a reconciliation needs.
The drive to improve the efficiency of operational processes and the move towards greater automation has become a central theme for our clients.
This often involves tying together data across systems.
The use of data analytics to identify process weaknesses and to automate manual elements has allowed us to support our clients in delivering greater efficiency.
Whether it’s proximity to competitors; points of interest (airports or water wells); risk factors (distance to rivers) or movement of bluetooth signals through shopping centres, location information is becoming more and more accessible.
CPRA has experience of the associated data issues, data gathering that can then be converted to location; visualisation and statistical analysis such as n-th nearest (euclidean) neighbour.
The common task though is the data normalisation: address validation, addresses that don’t resolve and addresses that generate multiple locations.
Revenue assurance & price disparity analysis
Reconciliation of invoice data to pricing schedules to ensure that invoices have the correct volumes and prices.
Systems mismatches, coding errors, old pricing agreements, new products that do not fit existing price models are just some of the issues that cause invoices to be issued incorrectly.
- More units delivered than charged
- Inventory sold whilst still tagged in the warehouse
- Cash settled contracts which were actually delivered
- Zero priced invoice lines automatically hidden by the invoice system so no one knew the client was getting things for free
Price disparity analysis can also quickly highlight anomalous pricing relationships. The analysis itself is not advanced but the normalisation of the products and pricing mechanisms benefits from modern data tools due to the layering of business rules on items such as product bundles, rebates and tariff combinations.