How does data on demand (for different users) change a data strategy?


Before considering how data on demand changes the data strategy (for different users), we first need to define what data on demand means. Data on demand is the delivery of information for use by consumers, when they want it. This is a tough requirement to meet for any technology team. Because when they want it may not be instantaneous, real-time data.

I like to think of data on demand like video on demand for your phone, television or laptop. By pressing a button, we rented ‘The Fate of the Furious’ last weekend, and within a couple of minutes it was showing on my television. Even with this great feature, someone, somewhere had to set up the movie and configure things properly to make it consumable across the internet and by my television.

There are certain business users that just want their data available when they need to run a report. These business users may assume there are no quality issues with the data, and that it joins nicely, Other users want all the data at their fingertips in a raw format for analysis, not for standard reporting.

Two task masters to please in our technological world

How does this change my data strategy? Well, I think it adds an addendum regarding the quality and governance of data on demand. The addendum needs to distinguish the differences in the types of data required by different types of business users. It also needs to describe the privacy and security required for each.

Let’s take an example. I have an implementation on a Hadoop platform for my point of sale information across all my national stores. Data is updated on Hadoop as soon as it is committed in my local store. Is the data stored redundantly? Yes. Is it raw data? Again, yes.

So, let’s name the business users.

Raw data business users

The raw data users do analysis of raw data that may include:

  • Preliminary campaign analysis.
  • Merchandise analysis for buyers.
  • Store sales by product analysis.

These users are familiar with the tools required to join their raw data into a format that's usable. Accessibility of the data becomes the governance factor for those business users requiring the raw data for analysis. A process and/or tool is needed to determine intake requests for access, as well as reporting who has access to what data.

Conformed data business users

The conformed data users require better quality, integrated data that can be reported to government agencies, as well as the corporation. They may require the following:

  • Correlation of sales to corporate cost centers.
  • Quality checks on the data from the local stores (product numbers, data structural checks, etc.).
  • Periodic SOX reporting.
  • Audit reporting.
  • Salesperson commissions.
  • Payroll statistics by pay grade.

It's important to note that the conformed data may include sensitive data, which should not be consumed by everyone. This, in turn, requires security-based retrieval and reporting.

As we depend more and more on new technologies, the need to report on access, security and governance will become increasingly important. I chose to create my data strategies based on data type and business user type because it seemed to be an easy way to work through each scenario of data usage. But our big data platforms are becoming more mature every day. Eventually, they will govern the data much like we already do with our rigorous access and security measures in existing corporate data assets.

With our new ways of using data today, we are involved and present in ways that can only serve to enhance our understanding (and use) of the data.

Download a paper about the 5 components of a data strategy

About Author

Joyce Norris-Montanari

President of DBTech Solutions, Inc

Joyce Norris-Montanari, CBIP-CDMP, is president of DBTech Solutions, Inc. Joyce advises clients on all aspects of architectural integration, business intelligence and data management. Joyce advises clients about technology, including tools like ETL, profiling, database, quality and metadata. Joyce speaks frequently at data warehouse conferences and is a contributor to several trade publications. She co-authored Data Warehousing and E-Business (Wiley & Sons) with William H. Inmon and others. Joyce has managed and implemented data integrations, data warehouses and operational data stores in industries like education, pharmaceutical, restaurants, telecommunications, government, health care, financial, oil and gas, insurance, research and development and retail. She can be reached at

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