Information management

Data Cleansing

In smaller cleansing requirements, we use statistical patterns and clustering algorithms/techniques to identify outliers, chart confidence levels, and normalize your datasets. In larger solution needs, we use advanced statistical algorithms and techniques to optimally streamline your data inflows and outflows and improve the overall data quality from the source. Data Quality is a survival issue and we strive to follow standards such as TDQM (Total Data Quality Management) in our approach.

Metadata and MDM

Metadata and MDM are the two pillars that will ensure Data Quality. As foundational elements, they are the first initiatives to be implemented as an organization goes towards data management. Metadata is more than just building a repository of your current data. At DataCaliper, we take an innovative approach to implement methods that make the creation of the repository an evolutionary process. Business is never constant and so is your data. It is thus very critical that your repository is able to adapt to your changing business needs.

Master Data Management (MDM) is traditionally used to drive BI initiatives and provide stakeholders of the organization with the gold standard of data. However, with current explosion in the number of data types and landscape changes in cloud computing and IaaS (infrastructure as a service), MDM itself has transition from its traditional role to an enablement role. Data is no longer in RDBMS tables and data warehouses and we have moved beyond classification of data into transactional and non-transactional data. With current device penetration, all kinds of structured and unstructured data exist, including data that change so dynamically that they become irrelevant by the time they are stored. It is thus a futile approach to implement a traditional MDM solution without understanding the overall Enterprise Architecture. At DataCaliper, we bring innovative, practical and common sense solutions in ensuring you have MDM that fits with your business needs and consumer behavior in the modern world.

Data Visualization

In a complex Enterprise, several applications are in active use by the user community. A natural evolvement creates a heterogeneous solution mix of applications dependent on each other. The dependency is often due to data and massive amounts of data are moved in the form of Nightly jobs, batch jobs, schedulers, etc. This data constitutes transaction data, social media, machine data, biometric data and human generated data. The movement of data across and within the enterprise is the lifeline of the Enterprise. This provides the connectivity between critical systems in the Enterprise. Streamlining this data movement is a specialized skill that can only be done by experts. In many organizations, this area offers significant process improvement opportunities in addition to ensuring enhanced data quality. Example: Redundant data during assimilation can have an impact all the way down in the SAN (Storage Area Network). Organizations fail to realize the investment needed in this area. A visual, graphical map of the data flow is the first step in understanding this complexity.

Data Federation

Traditionally ETL(Extract, Transform and Load) processes served a key role in any organizations’ DataWarehouse strategy. However, better techniques and technologies are starting to replace the role of the ETL. One such technique is Data Federation. In layman terms, it means to logically group all the data sources and present them as an integrated system, thereby avoiding any physical data movement. In other words, physical data movement is now being replaced by logical organization. At DataCaliper, our approach to Data Federation has been at a customized level. Data Federation is currently being considered only when existing data is too vast, diverse and dynamic that ETL cannot be implemented. However, our clients have boldly marched ahead in implementing Data Federation. We’ve transformed traditional database servers into Data Federation Servers and have helped organizations leap ahead in their data management initiatives bypassing the Data Warehouse implementations.

Data Migration

DataCaliper can help your organization migrate any type of data from both structured and unstructured sources into any type of target source, such as Relational tables, NoSQL and other data sources. We write custom programs to pull data from these sources and massage them as needed and move them into desired databases. Often times, we do data migration as part of our custom software solutions. Whether you need Access data moved into Relational tables or Transactional data moved into Data Warehouses or Data marts into cloud storage, we migrate data with accuracy and efficiency.

Data Policy and Governance

DataCaliper can help you create your Data Taxanomy with minimal impact to your current business processes. By identifying structured and unstructured data and their sources, one has to ensure that data is clean at the source. This takes several iterative steps. A good practice is to always create solid metadata about your data. It lays the foundation for clean data. In addition, Master Data Management (MDM) is key to defining the golden standard of data. Together data quality issues are addressed. An effective governing body, governance principles and data stewardship is necessary to keep these initiatives effective. After several iterations, visible results of data quality can be seen. DataCaliper can help your organization define these governing principles, help identify Data Stewards and establish processes in place that ensure data is taken seriously and quality checks are measured effectively.

Data Analytics

The most revealing insights you can get from your data happens after applied Data Analytics. DataCaliper can not only help your organization gain control of your data and establish a maturity path, but can implement Analytic solutions that can help you make better business decisions. We’re experts in complex Data Analysis such as Time Series Analysis, Cluster Analysis, Multivariate Analysis, Regression Modeling, Pattern Recognition, Computational Intelligence, Bayesian Analysis and other Statistical methods/algorithms

SAS, Clinical SAS

The lifeline of the clinical industry is Data. DataCaliper has worked with Clinicians, Biostatisticians, Researchers, Data Managers, Protocol Managers, Compliance Officers, FDA and numerous other stake holders of Data in several organizations to enhance and mature Clinical Data Management. Data from CTMS, CDMS, Safety data and other historical data are organized, standardized and managed for future analysis and decision-making. Starting from the protocol design, DataCaliper can help CROs streamline their data management processes to ensure data adheres to standards such as CDISC, HL7 or custom. We’ve helped organizations integrate their operational and safety data from multiple sources and standardized it against custom data standards. As a Data Management Leader, DataCaliper is well versed with CDISC standards and FDA compliance requirements and can help your organization leverage clinical data standardization principles such as SDTM ensuring timely and accurate FDA compliance, ADaM for validation checks, ODM for helping data interchange and archival, etc.

If you’ve encounter errors or problems in your queries or data, DataCaliper can write custom software programs to surgically correct errors and implement data checks and validations.

As leaders in Clinical Data Management, we are the natural choice when it comes to solving your data needs.