Data & Information Management

"The guy with the best data wins". Prevailing evidence supports the notion that a company’s use of information has a direct impact on its success in achieving results. Companies that are more mature in information use understand the importance of enterprise data integration and quality, and they are establishing an infrastructure to support their goals. Turning information into a valuable strategic company asset positions organizations for long-term success and transforms CIOs into business innovation leaders.

This escalated demand for information is intensified by recent growth in data volumes as well as the increase in types and complexity of disparate relevant data sets, including structured, semi-structured and unstructured data. Credible sources and business findings support the hype around “Big Data”…

“Information volume is growing worldwide at a minimum rate of 59% annually”… “The size of the largest data warehouse triples approximately every two years” … The data explosion is a key CIO concern and one of their “top three challenges”, according to Gartner ... Wal-Mart executes more than 1 million customer transactions every hour, feeding databases estimated at more than 2.5 petabytes … “Last year, digital data doubled every 10 months. Next year, it is expected to double every 10 hours”…

There is a clear need for powerful architectures and frameworks to manage it, as well as powerful analytic & reporting technologies to make sense of it. 


Data Integration is the Foundation

By integrating data across the enterprise, organizations can effectively use this data for analytical purposes. Measuring/managing key business drivers, such as profitability, require integrating disparate data from sales & marketing systems, financial systems and operational systems.

InsightSPI helps organization make valuable choices to integrate enterprise and departmental data through a variety of methods, including:

  • Enterprise-class data warehouse implementations hosting large amounts of historical data
  • Departmental or subject-level data marts focused on a single functional area
  • Operational data stores containing current values of data extracted from several operational systems
  • Enterprise information integration (EII) deployments that provide a direct, real-time view of data residing in multiple operational systems
  • Distributed highly scalable schema-free document-oriented databases for unstructured information
  • Hybrid approaches of the methods above

Many organizations use and/or require a combination of these methods as part of an enterprise information architecture. InsightSPI provides guidance and accelerated data integration solutions that quickly enable timely information delivery aligned with our clients’ business needs, IT strategy (e.g. SAAS, on-premise) and evolving information management “maturity”.


Data Quality

Poor data quality/accuracy is the “kiss of death” for BI and information management initiatives. According to numerous customer surveys and analyst reports, one of the two major reasons information management projects fail is due to a lack of business trust in the data (the second major reason is user adoption of tools).

InsightSPI helps organizations ensure correct and consistent data at every touch-point, and enable data quality to scale across the enterprise. Rigorous data quality processes and technologies are part of every InsightSPI data integration solution and embedded in our customers' information management framework to automate data quality standards across business-critical applications, systems, and platforms.


A Model for Information Management

Effective enterprise information integration and data quality is best managed and delivered within an Information Management framework that spans structured, semi-structured and unstructured data assets. The basic components of an Information Management strategy include:

  1. Information Architecture – a clear definition, blueprint and roadmap of how data is modeled, structured, collected, explained (e.g. metadata), shared, maintained, and stored from both the IT and business community perspectives.
  2. Data Governance – controls the performance of information management functions, and "enforces" effective management of the enterprise data resource.
  3. Data Stewardship –ensures that information is properly defined and understood (standardized) across the enterprise.

InsightSPI delivers broad and deep capabilities, and proven solutions, that contribute to successful enterprise information management programs while reducing the complexities and risks often associated with these initiatives. Our approach and solutions enable a more agile information management infrastructure across the enterprise to streamline performance management and reporting/analytics that drive business transformation. 


Data Management Roadmap

You have to start somewhere...

CIOs adept at translating the value of data into business results are in a unique position to guide their organizations. How can IT leaders effectively invest to transform information into a strategic asset that enables better decisions and delivers tangible benefits?

InsightSPI can help you jumpstart data management efforts with quick results, or perhaps augment your current efforts. Our Data Management Roadmap accelerates strategy, planning, gap analysis and implementation of a robust data management capability. Answer a few scoping questions and we’ll provide specific costs, timeline, and a list of high-impact deliverables to help plan or enhance this important initiative.  

 

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