Be liquid, my data

In 1971, Bruce Lee mentioned the famous inspirational speech "Be water, my friend" in a canadian TV show hosted by Pierre Berton, and popularised later by an advertising campaign of BMW. The speech actually applies better to one of my articles: Jazz and Adaptative Project Management, but in the current article about KPIs liquidity and data assets, be liquid is something you want for your data.

Liquidity is usually associated with economics, when referring to the ease with which an asset can be sold (market liquidity), the ability to meet cash obligations when due (accounting liquidity), the amount of money that a firm holds (liquid capital) or the risk that an asset will not have a good market liquidity (liquidity risk).

In this article we will see that KPIs, coming from the analysis of data assets, can also be demanded liquidity. And this data liquidity is key for the business intelligence and decision making when seeking for a superior performance.

Value chain

In order to do that, let's start with the value chain. The value chain is a concept describing the complete chain of company activities involved in the process of creating a product or a service.

Michael Porter, Harvard Business School professor, first described in 1985 the Value Chain in his book “Competitive Advantage: Creating and Sustaining Superior Performance”.

From the reception of initial materials, to the market delivery, and all activities in between, the framework of the value chain consists of 4 primary activies: Inbound operations, Business operations, Outbound logistics, Marketing and sales, and Services; and 4 secondary operations: Purchase and Procurement, Human resources management, Technological development and Company infrastructure.

Analysing the value chain of a company is to identify the primary and secondary activities, evaluating their efficiency. This value chain analysis may reveal linkages, dependencies and other patterns.

Data assets liquidity

Data assets are data artifacts such as databases, documents, video, images, presentations, spreadsheets, email messages, web pages, etc... carrying relevant data for the value chain, or containing an strategic or operational value.

Data asset liquidity is correct and relevant information for the value chain, arriving to the locations it is needed, at the right time.

A data asset is in its maximum liquidity when it doesn't require further transformation or adjustment, and its distribution is complete. When the data asset is key in the value chain becomes a key performance indicator (KPI). But a KPI doesn't have an inherent value in itself just for having it properly identified and stored. The value of a KPI lies in the potential use to be made of it (KPIs liquidity). For example, the goods or services that can be derived of the use of this data.

To exploit the liquidity of an asset, the best option is to invest the asset in something. For example, cashflow is very liquid asset, because we can transform it into something else, for example a house. The same applies to data: acquires value if it can be used to improve the value chain, its liquidity.

Data liquidity also means how easy it is in format and time to make use of the information generated by data assets.

Interoperability (multidevice)

Data liquidity requires our business intelligence system to get to the proper recipient, in the right way and when it is needed, which will also requires a user interface (UI) and a user experience (UX) adapted to the company department needs, depending on the position it occupies in the company value chain, the circumstances in which the KPI needs to be queried, and thus, adapted to the size and shape of the device to be used: mobile, tablet, laptop or videowall.

One of the goals of KPI liquidity is that data seeks the person, not the other way around.

The UX starts with the device used to interact with the system. Sometimes users will need to access the same data analysis system from multiple devices, usually having to display more or less detail in certain sections. For example, maps are not implemented on mobiles, but on a computer screen or a video wall.

Data Liquidity VS Interoperability

Data liquidity can be mistaken for interoperability, but a distinction is convenient. Interoperability is the way two or more systems work together, communicate with each other, in order to move information from one system to the next, while data liquidity may require interoperability to achieve a broader goal involving this and other requirements.

Interoperability does not optimise the data value for the company strategy, extracted from the interoperation of systems. This value can ve derived of the interoperation, but it is not the main goal of the interoperability.

Data liquidity uses Interoperability to pour data from one system to the next, but the with a focus on the use is going to be made downstream, with the intention of provide value to the data, then, thinking about what locations it must arrive to, and what use it is going to be made of it, in order to improve the value chain of the company.

Dashboards and Command Centers bring Data liquidity

Among the many data assets of a company, business development applications are also included, like CRMs (Customer Relation Management), ERPs (Enterprise Resources Planning), DSPs (Demand Side Platforms), etc...

These data assets, generating information stored in the company datawarehouse, is converted into liquid data (data that makes an impact in the company value chain) when it is made easy in time (it is fast) and format (it is usable).

Dashboard and Command Centers are a natural and suitable way to bring liquidity to a company's data.

If not it is just data stored without exploiting its full potential.