Building a Robust Data Strategy Roadmap – Part II
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.
- Beginner: Basic infrastructure and tools, proliferation of dashboards and reports
- Developing: Building tools and processes for historical as well as deep diving analysis to gain some insights for future actions
- Matured: Organization adoption of advanced analytical capabilities to predict future outcomes
- Business Transformation or Leader: Centralized analytics focus with capabilities to anticipate future and act in a data driven manner
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:
- Technology advancement has facilitated real time data analysis and personalized communication
- Big data technologies, cloud computing, machine intelligence and other advancements etc. have made analysis simpler & efficient
Rise of Consumerism
- Influx of more demanding consumers will force a wave of change
- Consumer engagement and experience management are key levers to success
- Daily volume of data being captured increasing rapidly
- Cost of storing data decreasing massively
- Recognition of amount of under-utilized data that can be used to derive additional value
Increasing importance of Analytics & BI
- Business Intelligence and Analytics becoming an integral part of organization’s decision making
- Pressure on profit margins are forcing increased focus on efficiency and cost reduction
- Increasing competitive pressure
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”.
- Selling raw unprocessed data to outside stakeholders
- Companies with rich pool of high quality raw data can onsell such data with little investment required
E.g. – Pharma related data or even NASDAQ’s “Data on Demand” service to its ecosystem of partners in the capital markets
- Companies collect and integrate data from multiple sources
- Data is processed, stored and leveraged in summary form
- Secure capture and transport of data
- Proper storage and management of data using a data platform
E.g. Card Advisory companies provide processed data to merchants and/ or use it for improving its operational efficiency
Business Intelligence/ Predictive Insights
- Tools and technologies such as data mining, predictive modeling and analytics convert data into insights
- Insights are made available to the stakeholders (both internal and external) to drive business decisions
E.g. Wal-Mart segments its customers into three primary groups based on purchasing patterns to spur growth
Products & Solutions Implementation
- Data-driven interactions with end users
- APIs and ability for companies to access platform and data to build comprehensive products and solutions
- Companies use the intelligence to improve product and solutions offering portfolio
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
- Continually understand the customer needs to unearth customer requirements and preferences
- Understanding the delivery and integration models that clients require in order to beneﬁt from enhancements
- Create a business model which fits into the core competency and create offerings which fir into client platforms and applications
- Invest in continuous learning and management of customers’ unmet needs ranging from enhancements to new products/ solutions
Decrypting the Enterprise Data
- Understand the enterprise data captured across all business lines and develop an enterprise wide nomenclature for the same
- Identify and map data and analytics services across business units to understand what types of capabilities can be leveraged to build new products and services using the appropriate business model
Gauging the Market Potential
- Calculate the market potential for the various opportunities identified
- Estimate the revenue potential, internal rate of return, investment required, cost reduction, efficiency etc. for the process
- Understand the key competition, factor in macro and micro factor which can affect the marketplace demand
- Seek out opportunities to enhance the core business or develop new products and services.
Deciphering the Value Chain
- Develop insights into partners and competitors across the value chain including upstream suppliers, data partners etc.
- Identify the new opportunities that can be available across the value chain
- Create a comprehensive view of the data ecosystem
Enhance the existing infrastructure
- Develop a sophisticated yet flexible architecture, suitable technology and applications which can help unlock the value that the opportunities might presents
- Put in place a data infrastructure that can provide the necessary foundation to enable the organization to unlock the value of data assets
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.