Dealing with Data

Data is gathered manually or through automated means by scraping websites. This option leverages scalability by selling the same dataset to multiple clients and customer segments. The highest recurring cost is in storage and bandwidth, which should reduce over time, and the efficiency and simplicity of the option are at the heart of its popularity. 

Data Manipulation Business Models

A store/host strategy can be especially profitable when dealing with broadening international data privacy and protection regulations. The legal and contractual burdens may be significant, but clients pay well to be relieved of the burden. Economies of scale work in the provider’s favor when the marginal cost to store data falls below the value of each client consuming data storage. This option provides value by eliminating the clients’ need to download the data for self-analysis. It is also very useful for extensive datasets or otherwise difficult to manage dataset.

The filter/refine business model adds value by handling the technical data cleansing work. In this case, clients pay for data duplicate removal, verification, or consolidation services. Normalization and downsampling are two other important refinements that could be offered.

The enhance/enrich strategy focuses on adding information instead of normalizing or removing data. A unique value proposition can be created by joining datasets or offering computationally intensive processing services on single dataset. This enhanced data can then be offered to the market with a profit. The public domain and other open dataset are ideal candidates for enhance/enrich operations. The strategy is aimed at reducing the burden of preprocessing data themselves. 

Data management solutions can also simplify access to data. Sometimes data is only available in formats that are not compatible with modern machine-readable interfaces. Data could also be hosted behind an API and made available for programmatic access to specific subsets in a machine readable format. Most of the effort in data analysis is spent on the collection, segmenting, subsetting, and cleaning processes. If you want to consume large datasets, you can either download all the data and write their extraction routines or pay for an extraction service.

Services that obscure data can also be very lucrative. Companies want to protect data-in-transit against exploitation of their data exhaust or similar data byproducts. Expanding national data sovereignty and individual data privacy concerns make protection against data scraping and aggregation efforts increasingly profitable. Businesses go to great lengths to keep data inside the corporate walls through the use of VPN access and internal websites.

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Article generated by AI in cooperation with Leaders Press based on Kevin Jackson’s Click to Transform.