That green shelf? Allow me to get right to the point — I am referring, of course, to converting your data assets into money, or what many now refer to as ‘monetising data’. Data, as with furniture from IKEA, requires assembly before use (or is monetised). Trouble is, it seldom comes with any instructions, or tools. And data, mis-assembled, often remains just that — useless bits & bytes. So, in the meanwhile, freely available data is literally just ‘gathering dust’. Quite akin to stashing money under one’s pillow, I would suggest.
But what needs to be done? Well, data is the raw stuff. And being raw, it this needs to be assembled into something folk want …. desire, covet and yearn for, actually. And like all secrets …
a secret (data) is of no value, unless it is first known, and then wanted
Money under the pillow therefore, may not be that bad at least for now, as it turns out making money from data despite the increasing acceptance of analytics, and tools like machine learning or artificial intelligence and digitisation is still not as straight forward as it is made out to be. As they say, “… some assembly is required …”
Based on my work with some of Australia’s most successful businesses & recent successes suggest that all this is about to change, and quite quickly as more of us understand how this conversion works and gets started. More and more businesses are adopting the rigour of data governance (curation, storage & use) and recognising exponential dividends. And when assembly is accurate and correct, the end products usually, exponentially high dividends.
a fundamental and possibly missing piece in monetising data, I propose, remains the ability to assemble each relevant piece into something of value.
And this begins with understanding data itself, in order to deliver its value. Unlike physical assets, data is re-useable, over and over again — not unlike sharpening that pencil where value of data is repeatable. And each time data is used, sold or combined there are monetary returns that n and should be realised. Therefore, the obvious benefits that ‘productise’, ‘economicise’ and use data will be released if there is a data strategy to monetise its value. A conundrum many face is that there remains no plan, and hence no ability to do this.
I suggest some tips, in this very brief article, a handful (5) of activities or elements that have been invaluable in our own endeavours, and that may also be helpful to you. Do however, apply necessary discretion when selecting how much and when each element is used.
So, the 1st element (the thumb) is defined from a need to apply business prudence to discern, distinguish and discover data: where enterprises need to be picky, to select data and understand its meaning regardless of structure, origin or location. And a fundamental construct in order to start using and monetising data is to establish a means to identify and represent its content. And unless data value is recognisable, it cannot be used. Important to at least reference and access specific metadata associated with your data (definition, origin, location, domain values, etc.). And this is not the task of the I.T. department, rather starts with the business. What questions are we trying to answer begins at the coal face — “what product sell more when discounted at 15% compared to 10%?”, “what size jackets should be range, at this store?”, “what exercise regimes does this athlete require?”, “how long can I extend down times, if I reduced the speed of this drill bit?”, and “what should I add to my menu, for the lunch patrons at my city restaurant?”.
The 2nd element (little pinky) is to know how, where and when to keep and access data easily: making data available in a structure and location that supports easy, shared access and processing is vital. It appears that this should be a basic capability in any organisation’s I.T. portfolio but most find this a complex activity. Basic like email, point of sale, HR records are already a challenge, let alone analytical systems or even general purpose data storage is computer hard drives become just unmanageable. This reveals that “data creation” is not yet linked to data sharing and usage. Solutions include cloud, internal databases, business partners and external provider datasets. Perhaps then a sufficiently robust data strategy is needed to ensure that any data created is available for future access without requiring everyone to create their own copies? And this gap is just making the end objective of monetisation just that much more unreachable.
The 3rd element (middle finger) is a whole of enterprise want to be a known purveyor of data so it can be easily and effortlessly be reused and shared. With, of course the appropriate rules and guidelines.
Traditionally, there has been almost no thought given to sharing data across applications, as each is application delivers returns independently. Therefore data was conveniently collected, created and stored independently. But to expect people to work through the idiosyncrasies of multiple of source applications just so they can use the data is an incredible waste of time. Each time data is needed, ‘experts’ are needed to extract data by either dumping that data into a file or building a one-off program to support another application’s request — almost ludicrous in an environment where answers were needed yesterday. And, even more ridiculous if we are to make some money from data. So instead of a dozen or so systems to contend with, occasionally; we now have 100’s or 1000’s to link with constantly, all the time. And if organisations have not provided budget or staff resources to address non-transactional data sharing to transition from a courtesy service to a routine, real time activity are show stoppers. Enterprises need to be purveyors of data.
And to top things off, when data is finally shared, it is packaged at the convenience of the application developer, not for data analytics — to understand its content. Issues of security, access, missing fields, incomplete data, and poor data hygiene come into play.
… acceptable a few year’s ago when just a few systems and a couple of teams needed access but this is now, literally blocking the enterprise ability to monetise its most valuable asset — its data.
The 4th element (index/pointer) is a cultivated enterprise ability to seamlessly make data — activate, combine and unify data perhaps currently residing in disparate systems, to deliver a consistent view for distinct perspectives needed to respond to business challenges. If data is not prepared, transformed or corrected to make it “ready to use” it may as well be the bits and bytes that we all just forget.
For a manufacturer to design, build and deliver a product (a dress, for example), it must acquire a large quantity of raw ingredients (cloth, string, buttons, zip etc.) and develop a manufacturing process to build and deliver this dress to be put onto the store racks. A rack filled with pieces of string, buttons, and uncut cloth is not ready to wear; as inspiration, design, cutting, measuring, sewing and ironing are required to make this beautiful dress ready to wear and available in that apparel store on Main Street. Data generated is the cloth, thread and buttons.
At almost all the enterprises I encounter, internal and external sources of data is available, generated from 100’s of application systems along with external data from a variety of different sources (cloud applications, business partners, data providers, government agencies, etc.) carrying with it rich information. But only if this were compiled or integrated in unique combinations ready to use, all this data remains just that — raw material. Analytics and its tools can be made available to transform, correct and format the data bespoke methods (analytics, transaction processing, data sharing, etc.). This is not the traditional team that address data cleansing, standardisation, transformation and integration for a data warehouse; rather building and incorporating logic to match and link values across a multitude of sources using mathematics and statistics, an understanding of the customer and the business.
The tragedy of data integration is that this rework happens with each new project because the learnings of the past are never captured for reuse. Therefore, tools and techniques used in data analytics is invaluable.
The 5th and final element (ring finger) is an agreed framework needed to establish, manage and communicate information policies and governance mechanisms for effective data use. Not may enterprises I have had the opportunity to work with have developed any framework or method to manage data outside the context of an application and across the enterprise. Some refer to this as data governance. But even within these enterprises, data governance is a nascent activity compared to other initiatives.
Basic tactical issues like data hygiene — accuracy, business rule definition or terminology standards and are confined to localised efforts. Data governance is very often specific only to users and the analytics environment, rather than the enterprise when in fact it is an overarching set of information policies and rules that everyone must respect and follow.
When these handful of elements are in place, analytics can then be leveraged simply by more enterprises to convert their data assets into profit. Certainly, not magical nor trivial tasks at all; but then again, who said making a pineapple (the AUD 50) or real money has ever been easy.
PS. To my friends and colleagues from outside Oz, the pineapple is the $50 note, as is the orange $20 note is referred to as the prawn (or shrimp). So there — hope you enjoyed and profited from this post!