Data & Intelligence

Realizing Agile Data Management …

Years of work went into building the elusive single version of truth. Despite all the attempts from IT and business, Excel reporting and Access databases were impossible to eliminate. Excel is the number one BI tool in the industry and for the following good reasons : accessibility to the tool, speed and familiarity. Almost all the BI tools export data to Excel for those reasons. Business will produce the insight they need as soon as the data is available, manual or otherwise. It is time to come to terms with the fact change is imminent and there is no such thing as Perfect Data but only what is good enough to business. As the saying goes:

‘Perfect is the enemy of Good!’

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So waiting for all the business rules and perfect data to produce the report or analytics, is too late for the business. Speed is of essence, when the data is available, business wants it; stale data is as good as not having it.

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In the changing paradigm of Data Management, agile ideas and tools are in play. Waiting for Months, weeks or even a day to analyze the data from Data warehouse is a problem. Data Discovery through Agile BI tools which doubles as ETL, offers significant reduction in data availability. Data Virtualization provides access to data in real-time for quicker insights along with metadata. In-Memory data appliances produce analytics in fraction of the time compared to traditional Data warehouse/ BI.

We are moving from the Gourmet sit-in dining to fast food concept for Data access and analytical insights. Though both have its place, usage benefits and short comings. They complement each other in terms of use and the value they bring to the Business. In the following series let’s look at these new set of tools and how they help Agile  Data Management throughout the life cycle.

  1. Tools in play:
    1. Data Virtualization
    2. In-Memory Database (appliances)
    3. Data Life Cycle Management
    4. Data Visualization
    5. Cloud BI
    6. Big Data (Data Lake & Data Discovery)
    7. Cloud Integration (on-prem and off-prem)
    8. Information Governance (Data Quality, Metadata, Master Data)
  2. Architectural changes traditional Vs Agile
  3. Data Management Impacts
    1. Data Governance
    2. Data Security & Compliance
    3. Cloud Application Management
About the Author

Shankar RamaNathan is a Senior Enterprise Architect with 25+ years of experience in successfully developing and implementing IT strategy and Information Governance ( Master Data Management, Metadata Management, Data Quality and Data Governance) programs.

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