Saurabh Gupta, Co-Founder — CODEX
Your data holds a lot of secrets
Every company irrespective of being a tech or non-tech enterprise, generates voluminous data in the process of their regular business operations. While many companies ignore the need to capture that data (Yes! It’s hard to believe), many others are successfully capturing and using it further business growth. This can be related to how customers’ buying patterns are changing or how employees’ motivation levels are impacting production volumes or how/when a plant’s machinery needs preventive maintenance or if the marketing efforts are really fetching the required ROIs.
Roadmap for data monetization
Data is no longer a mere by-product of an application or transaction. It has become the most precious asset available at the disposal of the management to take not just informed decisions, but also use it in ways that generate income directly or indirectly. Monetizing data must be the ultimate goal of every digital transformation journey.
For the sake of simplicity, we would branch out the roadmap into three parts, viz. (a) putting in place the strategy and infrastructure (b) making sense out of the data, and (c ) monetizing data for organization’s growth. Let us deal with them one by one.
Data Strategy and Infrastructure
This is basic groundwork that the top management has to figure out and execute before unlocking the value from data. If this is executed in the right way, then everything else falls in place. First and foremost is to figure out a data strategy. As you are dealing with data, you need a strategy to use it. This sets out the rules and frameworks for using the data — like who should do what, SOPs for using the data, access to data, etc. In a nutshell, data strategy helps management in using and managing the data as an asset rather than a by-product.
Some of the important things that need to be addressed at this stage include:
- Data security: Over the years, data has become a very precious asset indeed. It has become the Holy Grail for companies (who understand its value) that they go to elaborate lengths to secure their data from the competition. Securing your data from the prying eyes of competitors and unwanted attention is the fundamental duty of every organization. If you do not secure your data, then someone else will make it theirs.
- Data infrastructure: This encompasses all the hardware equipment and software tools required for managing data. This also includes qualified manpower required to handle and operate this hardware and software.
- Data governance: SAP defines data governance as the set of policies and procedures that govern how data is stored, accessed, manipulated and deleted. It provides clarity on the roles and responsibilities of people using the data. Companies are required to set up policies and SOPs for every action taken on data. A certain level of standardization of procedures can help in reducing misusing the data.
- Data privacy: This broadly defines the rules and regulations imposed by the local and federal governments in terms of using customer data in ways that comply with the respective privacy laws. Companies are required to comply with these regulations to avoid illegal use of customers’ data. For instance, the European Union has stipulated that storage of customer data related to transactions done within the EU must be stored in servers installed within its geographical borders.
A good data strategy helps in:
- Identifying the required data in the most minimal time possible
- Easy storage and retrieval for future use without requiring everyone to create their own copies
- Packaging and easy sharing of data across the enterprise and to external stakeholders (as and when required)
- Making data ready to use and thus reducing the IT involvement
- Easy data governance and making sure data is used as per the policy frameworks
Analyzing & Exploiting the Data
While the first part of the roadmap deals with setting the groundwork for using the data, the second part is where the action lies. In this phase, companies have all the necessary systems and frameworks in place and go about capturing the data, storing, using, managing and exploiting it for business growth.
Data analytics, machine learning and artificial intelligence are the most common buzz words today that are associated with bringing out useful, valuable and actional insights out of the available data. Companies spend huge money in these tools and technologies that can deliver value from raw data. These technologies crunch those huge volumes of raw data and bring out highly useful insights that would have otherwise gone unnoticed.
The resulting inputs not only complement the decision-making process, but, more often than not, they prove useful in building new products and services. For example, the management of a restaurant chain can precisely decide on the amount of shrimp they should buy for a given month based on the historical data combined with changing trends. At the same time, insights from this data may also help them to come out with new shrimp-based menus that are better aligned to customers preferences.
Making Money from Data
Data monetization is a phrase that is often heard these days along with other catchy phrases like ‘data worth’ and ‘data valuation.’ In a sense all these phrases talk about the same thing — how a company can monetize its data assets.
Monetizing enterprise data assets can be done either by (a) directly selling the data to third parties who can use them; and (b) developing new products and services based on insights from the data. While the former is a direct way of monetizing data, the latter is a more refined, sustainable and long-term strategy for data monetization.
Many companies like retail and e-commerce giants generate a humungous volumes of customer data and they selectively sell chunks of data to third parties, thus making direct income from the data generated by them. The second involves deriving valuable and actionable insights derived from the data using analytics and AI tools. which in turn can be used for developing new and better incoming-generating products and services.
Obstacles to data monetization
While, many companies are realizing the value of data they generate and successfully monetizing it, many more companies are still struggling to put their act together. Barbara Wixom and Jeanne Ross in their article “How to Monetize Your Data”, published in Sloan Management Review, state that there are broadly two obstacles that hinder companies in monetizing their data — accessibility & quality.
Accessibility: The primary obstacle is the accessibility and quality of data. Wixom and Ross revealed that their research in data monetization found that only about a quarter of the companies surveyed allow their employees and customers to use the data they need most. This precisely means that locking away data in secured vaults without providing access to people who need it is definitely not a good approach to monetize it.
Data Quality: This is a major concern for every data generating organization. The quality of data has to be vetted right at the stage where it is generated. Wixom and Ross state that making the data generation teams accountable for their work can solve the issue of data quality to a large extent. The more these teams are accountable, the better the quality of data is.
So, why wait…
Data monetization is now a growing trend as more and more companies are realizing on how data can be leveraged to increase revenues. Data helps companies in not just adding new revenue streams, but totally alters the way business is done. Future holds great promise for those enterprises which readily embrace the culture of data-based decision making and design their products and services based on solid data insights.
