• 1. Information Management

    IM deals in translating letter, words and numbers into values information while maintaining integrity, availability and conformity, security for transactions, search, presentation, monitoring, alerts and analysis.

    Enterprise Information Architecture:

     

    Maintaining the single view of key master entities requires enterprise policies, guidelines, standards, procedures, governance models and tools and technology.

    Building a Master Data Management strategy requires planning for readiness, assessment and project management and governance mechanism to manage timelines, challenges and changes.

    Our ASG teams follow our Monitor, Maintain and Meliorate (M3) philosophy for ASM services for continuous service improvement. Our end-to-end Service Wrap teams bring in business focus and ensure that all initiatives are well aligned to customer objectives delivering operational and cost benefits. Our Application Service Management services include:

    How to do MDM- OurApproach

      Set mission, vision, objectives, assess the enablers, values and return on investment, risks aligned with the business goals.

      Define and elicit the MDM requirements for subjects and prioritization.

      Assess and identify the existing capabilities, gaps in terms of standards, policy and procedure and technology.

      Set the target MDM capabilities and define the implementation styles, identify the keymaster, reference sources and procedure to acquire data from them.

      Profile sources, address the data quality issues, and define rules, hierarchies, metrics and affiliations.

      Planning and roadmap for implementation, deployment and change management.

    Flaws in any process are bound to introduce risks to successfully achieving the objectives that drive your organization’s daily activities.Because data is of a much more dynamic nature, created and used across the different operational and analytic applications, there are additional challenges in establishing ways to assess the risks related to data failures as well as ways to monitor conformance to business user expectations.

    While we often resort to specific examples where flawed data has led to business problems, there is, frequently real evidence of hard impacts directly associated with poor data quality.

    Developing a performance management framework that helps to identify, isolate, measure, and improve the value of data within the business contexts requires correlating business impacts with data failures and then characterizing the loss of value that is attributable to poor data quality.

      Q.How do we measure data quality?

    Ans.

      Impact on the value of the information to the business and users in terms of confidence and satisfaction.

      Distinguish from high impact to low impact on productivity loss, financials on revenue, operating cost, missed opportunities, payments and fine etc., regulatory affairs, delayed reporting, impact on decision making and so on.

      Q.How to evaluate data quality issues?

    Ans.

      Business goals typically revolve around maximizing the way that the organization’s customers are served, and that suggests a process such as this:

         I. Enumerate the products and services that the organization provides;

         II. For each product or service, evaluate what data is being used as input and how the output is employed;

         III. For each product or service, determine the top 3-5 data objects that are critical to successful operation;

         IV. For the most important data items, consider what kinds of errors can be introduced into the system;

         V. For each introduced error, review how the introduction of the error affects the ability to provide optimal value.


    Our approach to fix the data quality issues

      Data quality requires enterprise wide and strategy approach.

      Identify data quality requirements, the metrics and indicators and plan review meetings.

      Data quality requires the project plan and team in place.

      Profile the sources for data anomalies like :

          a) Missing data

          b) Wrong or inaccurate data

          c) Inappropriate data

          d) Non-conforming data

          e) Duplicate data

          f) Poor data entry

      Build strategy to formalize approach and standards to solve the problems.

      Build ETL scripts, reusable components to fix the issues

      Data stewards to authorize the changes to the master data.

      Reporting the data quality metrics to the management

      Follow up the action and continuous monitoring.

     

    Data from various upstream, downstream system required optimal integration strategy and plan. The tools and technology to acquire, consolidate, retain, secure and present data to the business to meet its various business functions has undergone paradigm shift over 2 decades.

    The need to provide data in real time, inject the high volume feeds in short batches, pull the business operations data, extraction unstructured data from varied and complex systems required optimal mechanism and hardware and software technology to minimize the latency, the impact on upstream or downstream system sitting on various critical tiers.

    With virtualization of data in memory and with the help of APIs data can be transported, replicated, copied and transformed at lightning speed there by reducing the load time window into data lakes, warehouses and data stores.

    Data extraction and Loading

    Data loading to shared nothing or shared everything systems fall into basically two categories.

    Batch mode data integration

    The data acquired from the different sources using different protocols and mythologies is validated, harmonized, transformed, summarized and maintained in SQL and noSQL databases.

    The data is moved in regular batches within defined load time window.

    Challenges

    The business applications are often over loaded in data provisioning and data preparation for the downstream for consumption.

    Multistage data acquisition, transport, complex transformations and loading often make the entireprocess cumbersome to manage and inefficient.

    Solution

    Data visualization for agile data integration, using in memory technology, using custom build data integration solution from the persistent storage like S3 can help overcome the challenge to offload the critical applications.

    Regular histogram collection, using incremental, delta, change data capture, distributed and multi thread light weight data transformation routines, file formats and compressions, alerts routines and acquisition mechanism can some of the protocols which can be used to overcome inefficiencies.

    Near Real time data integration

    Messaging, streaming or event data can be captured almost instantaneously almost slight degree of latency.

    Enterprise bus, APIs, Web services models, broker application & message queues are used to ingest real time feeds into the data store and further processing using different integration styles and patterns.

    Our Approach

    Every engagement is different and has different kinds of challenges which would need tailoring the solution, solution and planning.

      Identify, assess and establish the value in the data integration in business terms.

      Suggest high level solution, approach, timelines and license and implementation cost.

      Detailed engagement approach, project management and implementation procedures. It could be mix of various methodologies or agile mode.

      Solution design, prepare project high level plan and identify the tools and technologies, resources to work on the project and baseline deliverables.

      Engage source system subject matter experts, business analysts, technology experts in converting the business requirements into the technology requirements.

      Use best practices in implementations, testing, deployment, go live and production support.

    Tools and technologies

    Tools and technologies help in rapid application development, avoid reinventing wheels, change management, maintenance & support and IT operations.

    We expertise in COTS, open source tools and technology in the data integration space. Our custom tools and APIs for data ingestion, code checking, deployment and service management add value in ETL packaging and maintenance.

     

    Business Intelligence

    Deriving business insights by analysing trends or point in time reporting or on demand reporting has been the sole purpose of the BI reporting from years.

    The reports churned out from different data sources having its own design layer for user and data governance, report designing and managing schedules has gone paradigm shift in terms of making reporting more user friendly, faster by building in-memory model both semantically and physically and data caches.

    Our expertise in designing the multi-tenant semantic frameworks for industry verticals and portals for the data ingestion into the application database either logically or physically and integration of the reporting portal and APIs help customize the end to end business intelligence reporting, data delivery requirements.

    We always strive to exceed the expectation by using our experiences in building multi-tenant solutions to build complex dashboards, complex drill down, drill though and slice and dice reports, extracts, real time analytics is our forte.

    Tools and technologies

    Tools and technologies help in rapid application development, avoid reinventing wheels, change management, maintenance & support and IT operations.

    We have expertise in implementing various COTS products from the industry leaders, open source tools and technology in business intelligence.

    Our custom tools and APIs for data ingestion, code checking, deployment and service management add value in BI packaging and maintenance.

     

    Information collected from various sources and channels, stored on a distributed data platform for various business and non-business needs can be searched, linked and analysed.

    Solution:

      Ingest data using various APIs, connectors into the storage platforms.

      Process further into document storage files systems.

      Build a search schema for the content search and indexing engine using best practices for enterprise search requirement.

      Data exploration, navigation, summarization and analysis on top of search APIs help users achieve wider objective of searching the contents and deep analysis like understanding user behaviour based on the keywords search, the user activities, analysing and assessing based on the patterns in the data.

      Faceting, filtering can be used to curate the content in the data preparation for further analysis.

    Semantic Web Technology Standards & Promise

    The World Wide Web Consortium (W3C)'s Semantic Web standards—primarily RDF, OWL, and SPARQL—provide revolutionary flexibility for linking data together, reasoning over it, federating it, and querying it. B3ds is a leading Semantic Web platform for the enterprise.

    At its core, B3ds includes a scalable, full-featured RDF database. All data in B3ds is organized into named graphs: named graphs can be secured, versioned, and replicated. Developers can manipulate the data using Semantic Web APIs in Java, .NET, and JavaScript. All of the data linked to B3ds is modeled using OWL ontologies, can be published as Linked Data, and rendered in web dashboards using RDFa.

    B3ds offers extensive support for SPARQL for working with any structured or unstructured data linked into the platform. Data can be accessed via SPARQL 1.1 queries on the command line or via a standard SPARQL Protocol endpoint, and you can use SPARQL 1.1 Update for bulk updates. B3ds provides a large library of extension SPARQL functions that gives query authors a great deal of expressivity to access and manipulate the data. You can use SPARQL to define rules that reason over your data and infer new data automatically.

    Semantic search coupled with ontology gives visual representation in understanding the relationship between the searched contents, hierarchy and grouping and data clustering.

    RDF OWL or XML can be used to build the semantics engine based on triples and relationships.

    Semantic Web Platform

      Semantics makes it possible—B3ds makes it easy

      Leverage the flexibility of a true enterprise Semantic Web platform

      Supports all of the core W3C Semantic Web standards

      Easily and quickly consume and publish linked data

      Build and share conceptual ontologies to model your business

    Semantic Web Technology Standards & Promise

    The World Wide Web Consortium (W3C)'s Semantic Web standards—primarily RDF, OWL, and SPARQL—provide revolutionary flexibility for linking data together, reasoning over it, federating it, and querying it. B3ds is a leading Semantic Web platform for the enterprise.

    At its core, B3ds includes a scalable, full-featured RDF database. All data in B3ds is organized into named graphs: named graphs can be secured, versioned, and replicated. Developers can manipulate the data using Semantic Web APIs in Java, .NET, and JavaScript. All of the data linked to B3ds is modeled using OWL ontologies, can be published as Linked Data, and rendered in web dashboards using RDFa.

    We offer extensive support for SPARQL for working with any structured or unstructured data linked into the platform. Data can be accessed via SPARQL 1.1 queries on the command line or via a standard SPARQL Protocol endpoint, and you can use SPARQL 1.1 Update for bulk updates. B3ds provides a large library of extension SPARQL functions that gives query authors a great deal of expressivity to access and manipulate the data. You can use SPARQL to define rules that reason over your data and infer new data automatically.

    Data Science and Big Data Analytics

    Data science is now influenced by the innovation in technology and is no more looked from the core work of data cleansing, preparation and analysis but not with the intervention of big data technology to apply machine learning algorithm to draw insights from unstructured and structured data achieving higher efficiency and domain coverage.

    Application of data science has been around from years to apply mathematical and statistical modelling on data set to classify, classify, explore and understand the data patterns in decision making or drawing conclusion based on the outcomes.

    Our domain expertise and technology skills enable open a wider window to collect, prepare and analyse the data sets which were otherwise limited due to technological limitation.

    Application of Bid data and analytics in genetic engineering and research and development can cut short the long discovery cycle and thereby reduce product development efforts, reduce time to market, make product development more efficient and customer centric, apply observational analytics on evidence based data, reduce cost and so on.

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Technologies We Use

 

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