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Data as product

The opportunities unblock only when the true value of data is known. Let me tell a story.

A man went to God and asked, “What’s the value of life?” God gave him one stone and said, “Find out the value of this stone, but don’t sell it.”

The man took the stone to an Orange Seller and asked him what its cost would be. The Orange Seller saw the shiny stone and said, “You can take 12 oranges and give me the stone.”The man apologized and said that God has asked him not to sell it.

He went ahead and found a vegetable seller. “What could be the value of this stone?” he asked the vegetable seller. The seller saw the shiny stone and said, “Take one sack of potatoes and give me the stone.”The man again apologized and said he can’t sell it.

Further ahead, he went into a jewelry shop and asked about the value of the stone. The jeweler saw the stone under a lens and said, “I’ll give you 50 grand for this stone.” When the man shook his head, the jeweler said, “All right, all right, take 100 grand, but give me the stone.”

Let us make this analogy with data. Data is like a "stone". The stores are opportunity hubs. The opportunities get unblocked when the true value of data is known.

Organizations have a lot of data. The majority of the data can't get utilized well unless data gets considered as a product.

"Data as product" mindset will allow applying product life cycle principles.

It helps in:

  • Creating business value

  • Strategize data analysis

  • Identify rightful data sets

  • Dump the unwanted data

  • Measure ROI

The first step in enforcing the culture of "Data as product" is enabling "Data as a Service (DaaS)" to make data access faster and more secure.

Here are the top challenges in implementing data as a service:

  • Disparate data sources

  • Data governance

  • Lack of talent

As per recent Gartner report, lack of talent increase the risk of technology adoption from 5% to 64%

The following steps ease the data as a service:

  • Ability to connect to disparate data sources

  • Talk to any data source using a unified language

  • Row-level and column level security

  • Ability to fix data quality issues and perform data transformations

  • Dynamically mask data based on context and user

  • Ease of data access for democratization

Following advantages are foreseen with "data as product"

  • Controlled data democratization

  • Avoid data leakages

  • Faster and secure data access

  • Infrastructure & storage costs saving

  • Rapid application building

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