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Unarguably, data and technology is truly redefining & rehashing the way companies do business. Organizations have always had data, which they have utilized to run their businesses more efficiently but recent developments have transformed the way data is utilized by such organizations.

In today’s disruptive economic environment, all leaders are vying for identifying new revenue streams and identifying existing value streams inside the organization especially data. This is where the concept of crafting a Robust Data Strategy comes in, how do we make most of the Dark Data ? Data is now being looked as an asset and business models are now being build around this vast value pool which is hidden inside the data being stored. Enterprises are now anticipating future needs based on preference insights culled out from past & present data. They are creating new products and services in tune with what their customers exactly seek. They are lending an ear to all suggestions/recommendations/feedback shared and also responding to queries/concerns in real time. They are doing it all with data and analytics.

While many companies are becoming aware of the opportunities embedded in their enterprise data, only a few have developed active strategies to monetize it successfully. Data Strategy requires companies to not only understand their data, but also to uncover gaps and evaluate suitable business model(s) for appropriately monetizing the enterprise data. To evaluate their respective monetization opportunities in a more informed and results-driven manner, companies need to assess the value of enterprise data, determine how best to maximize its potential and figure out how to get the data to the market efficiently.

Four Stages to Analytics Sophistication


Based on the current state of data affairs, any organization can be categorized as a beginner, developing, matured or leader. In the initial stages of transformation, organization typically
lacks synergies due to silo’ed efforts, is less agile and more prone to errors, with perennial data quality concerns. As they mature to be leaders in the Analytics space, data sits at the heart of business, with increasingly automated, instant, accurate and seamless data driven decision-making.

Time’s ripe to ride on the Data & Analytics wave

Enterprises capture a lot of data, most of which is often overlooked. With reducing costs of capturing and storing data, increasing data analysis capabilities and superior analytical technologies available, enterprises have started to recognize data as one of their most valuable assets. In the few years, enterprises who lead the way in reorienting their approach, initiating enterprise wide data-led transformations and effectively monetizing their data are expected to be in the forefront. Typical market forces driving widespread adoption of Analytics are:

Technological Advancement

Rise of Consumerism

Data Explosion

Increasing importance of Analytics & BI

Economic Pressures


Is your Data truly worth it?

How much business value can be created via data on which organizations are sitting on depends primarily on the following factors & to an extent determines the success of any Analytics initiative.

Predict Behaviour (Patterns)

Enterprise data should be detailed enough to build a successful data monetization strategy. E.g. Customer data should be detailed enough to be able to predict customer behavior, patterns etc.

Size of the Ecosystem

Businesses with high volume, large breadth of data have the ability to generate highest value from the data. Companies with national or global scale can easily establish market view, which makes it more meaningful and valuable

Accessibility and Actionable

Data becomes valuable only if its rich, actionable and accessible. Structured, & readily scalable data makes the process of monetization simpler and efficient, providing higher potential for data monetization

Customer Identification (Granularity)

Data becomes valuable only if it is granular enough to be able to identify the end user/ customer. Ability to identify/ profile customers helps in expanding the range of products and servives that can be offered


Uniqueness of the enterprise data is extremely valuable. It makes the products/services offered by the enterprise exclusive to the enterprise, sustainable differentiation which most organization yearn for

Stages to Data Maturity

Based on maturity of organization’s data, it can take a call what kind of a player it wants to be in the market – a “data seller” or a “full services provider”.

Raw Data

E.g. – Pharma related data or even NASDAQ’s “Data on Demand” service to its ecosystem of partners in the capital markets

Processed Data

E.g. Card Advisory companies provide processed data to merchants and/ or use it for improving its operational efficiency

Business Intelligence/ Predictive Insights

E.g. Wal-Mart segments its customers into three primary groups based on purchasing patterns to spur growth

Products & Solutions Implementation

E.g. Tesco bank uses Clubcard customer data to identify customer needs and creates new personalized offers

Key Elements to Designing a Robust Data Strategy

Unravel Customer Needs

Decrypting the Enterprise Data

Gauging the Market Potential

Deciphering the Value Chain

Enhance the existing infrastructure

The crux of the matter is that with the huge amount of data available with the enterprise’s in today’s competitive and converging business environment, they should start looking for market opportunities leveraging the data available with them. Most of the enterprises still do not consider data as an asset which they can monetize if they choose the correct business strategy and build the required capabilities. Enterprises can not only make better use of their internal data to enhance the current product and services portfolio, it can also provide new insights into the value chain and could transform the enterprise, unleashing a whole new set of products and services for the customers.

By utilizing internal data with external data, powerful generation of high margin solutions can be developed which can transform an entire organization which possesses enormous revenue potential. Done properly, data ecosystems can fund the transformation, create value for customers, and build long lasting relationships with other partners firms, 3rd party vendors & suppliers. But to ensure the true value of data is being monetized by the enterprise, it is essential that it follows a streamlined process to identify the most suitable business model(s) taking into account all constraints which the process might need addressing.

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