Four steps to monetise unstructured data
Unstructured data is growing at a significant pace. According to the latest figures from research firm ITC, the volume of unstructured data is set to grow from 33 zettabytes in 2018 to 175 zettabytes, or 175 billion terabytes, by 2025.
The data monetisation market is growing in parallel. Data Monetization or the art of extracting revenues from data requires new skills, technology, processes, and business attitudes towards data. So how big is the market for Data Monetization? It is expected to reach US$ 708.86Bn by 2025 at a CAGR of 21.4%. Data monetisation is being added to the C-Suite agenda as a high priority but how can businesses begin to make sense of this data and monetise it?
Step 1: Having a clear data monetisation strategy
“An effective data monetization strategy recognizes data as an evolving asset living and growing within a living enterprise and NOT as a static asset that depreciates in value.”
- KPMG Research, framing a winning data management strategy
As the above statement suggests, to be able to create a strategy to monetise data, there attitude towards data needs to change. They need to view unstructured data as an asset rather than a liability or a depreciating asset. The next step is to define what monetisation of data means for the organisation.
Are they going to use he data to provide personalised, contextual services to their clients?
This gives them a competitive advantage and encourages loyalty in customers, hence bringing in more revenue.
Use the data to improve business operations in turn delivering better client experiences at reduced cost
These are two simple examples of how organisations can monetise data and achieve their business goals.
Step 2: Actioning data insights
The CEO and other top executives must be able to clearly articulate the purpose of data as we said in the first step, The next step is to translate the strategy it into action—not just in an analytics department, but throughout the organisation where the insights will be used. However, thr optimal use of data to derive maximum business value is not a static process. This data usage process presented by Mckinsey emphasises the importance of acting on data insights and feeding back the results inorder to analyse, access and further improve returns from the data investment.
Step 3: Having the technology to support the data monetisation strategy
With the growth in data, the technologies to manage and monetise data have also increased. The technologies range from:
Master data platforms – to store, manage and govern enterprise data
Data exploration tools that help build, train and test data models
Data analytics tools that help derive insights from high volume of data
Artificial intelligence, NLP and similar technologies are more versatile as they not provide insights into the data they also help repurpose the unstructured data for business decision makers to understand
RPA and workflow automation technologies help capture the data, convert unstructured data to machine readable data and route it to the right people within the organisation or to the right systems.
Which technology is best suited to achieve your business objectives? It could be one or a mix of technologies required to monetise enterprise data.
Step 4: Change management: Data monetisation is not an IT initiative
Companies that struggle to get consistent data across the organisation shouldn’t be thinking about providing it externally, but they should take the time to shore up their data foundations—strategy, design, and architecture. Internal customers/employees need to understand the value of data and not see it as an “IT Initiative”.
Putting their data to work for internal use cases, such as improving decision making or optimizing operations will be vital in building the business case and technical platform need to monetize data effectively, as well as create foundations for their data monetization models for their businesses.
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