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Data Liquidity

Introducing Data Liquidity, a new concept in data management consultancy

Most of the conversations I have today with clients begin with a statement of fact around their owned and operated data assets. “We’re a data rich business”. Often a business considers themselves to be “data rich” or “data poor” and almost all data driven marketing strategies and use cases become a derivative of this initial value judgement.

But are these statements efficacious? How is the value of data measured, and are the current metrics sufficient to inform an accurate picture of an organisations actionable data resources?

“Data Liquidity”

Over the past several months, working with major global organisations to consult on their data and technology infrastructure, my team and I have developed a completely innovative and proprietary approach to data asset valuation, technology efficiency, and informed GAP analysis surrounding the use and utilisation of data. This approach focusses on the concept of Data Liquidity, described below:

“Data Liquidity – A data asset is deemed to be in its most liquid state when it requires no further transformation, matching, or distribution to be activated upon. For instance, a pre-matched audience stored within an active DSP is considered 100% liquid because its immediate activation is possible. Alternatively, a batch of CRM records residing in a separate data warehouse that require some form of ETL and data matching to be activated, is considered illiquid i.e. on its own, and in its current state it cannot be activated directly”

Our approach is unique, as it assesses data liquidity, and its surrounding components. It combines classic data auditing techniques with financial accountancy metrics. Not only do we evaluate the value of a specific data assets, but we also analyse metrics such as through-put, match rate and extraction rate, which indicate the efficiency with which a data asset can be activated in the short-run (keeping the client’s current technology infrastructure fixed). This gives a client a far more accurate picture of the value of the assets that they have, that can be brought to bear in media/marketing in a reasonably short period of time.

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The Data Liquidity Assessment Process

  1. Building a data asset ledger – This is a record of data assets available to a marketer. The ledger documents each asset’s record volume, density (range of attributes), method of collection, pretence for collection, privacy and compliance (consent and permissions), storage status, tactical and strategic value, and suitability for specific uses.
  2. Mapping the technology infrastructure – Producing a visual map showing how data assets are transferred from one environment, system or platform to another in the context of a specific activation. This includes how they interact with other data assets en-route to marketing activation.
  3. Calculating throughput – Assessing the efficiency of converting illiquid data sets, to liquid data sets. Throughput is a metric describing the volume of the original data asset that is available in the final activation platform, as a proportion of the total volume of the initial data assets volume, represented as a percentage.
  4. Assessing data liquidity – Once we have established the asset ledger and the throughput of the assets, we’re able to establish an aggregated view of data liquidity. Bringing us much closer to understanding the client’s actionable data sets, and ability to meet short-term marketing and media commitments.
  5. GAP analysis – A client’s liquid data asset is compared to the total audience requirement/universe to establish what can be achieved through 1st party data sets alone
  6. Data enrichment & leverage recommendations – If we have identified a shortfall in our ability to meet short-term requirements with 1st party data alone, we will make recommendations as to enrichment and extension of 1st party data sets. Recommendations come in the form on (1) data mining and systems integration recommendations aimed at increasing the overall data asset value, and the throughput ratios, and (2) data partnerships and data modelling recommendations, aimed at identifying possible 2nd and 3rd party data partners and solutions for the modelling and extrapolation of 1st party data.

Data Liquidity Assessments have enabled us to provide our clients a much more thorough and practical view of their data, its value, and its ability to be accessed and applied. It provides a baseline for evaluating all data driven marketing activations, whether they be insights driven, or audience targeting. This baseline understanding pays dividends in planning and buying exercises down the line, providing a clear understanding of what is available, and what will need to be licensed from external parters.

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