by Andrew White | March 6, 2013 | Comments Off
I spied two Information Management emails this week with the headline, “How do you put a value on data?” and of course, I had to jump on those and read the articles. Fascinating stuff of course, but there is some dialog to explore here. So here we go.
In Alex Bakker’s piece (“How Do You Put a Value on Data?” he updates us on a slew of vendor announcements related to big data, big data analytics and so on. He rightly calls out how too few end user organizations have any real concrete idea for how the investments in big data this or big data that will yield any specific business value. His point is – we just don’t know. This is fair, I think. I blogged on one aspect of this problem – related to dark data – July of last year: Dark Data is like that furniture you have in that Dark Cupboard. Clearly end users need to be able to frame a specific question, or define an intended focus of an outcome, before they part with their hard earned cash for a big data thingy.
Next I spied: “We believe that most, if not all, business data has value. That value changes over time and in relation to business mission, goals and operations”. I cannot argue with that – sounds good to me. But then I spotted something I had to write about:
“One of Saugatuck’s core tenets regarding the business value of data is that its value tends to increase the more it is used”.
Think about this for a minute. Just because money changes hands more frequently, does its value increase? No, it does not. At a higher level there is the possibility of inflation however! Too much money (cash) chasing too few assets leads to the monetary version of inflation.
Also, you cannot force users to use the same piece of information more frequently. It kind of is used more frequently, or it isn’t. If you think this piece of information is valuable, and you let other know that you have it, you might increase the efficiency with which that information is used, but does its intrinsic value increase? Or is it the case that the economics that yield that value changes in that you get lower costs to expose that same value? In other words, is this just starting the obvious – Integrate once, re use over and over….? Is this just playing with words?
There is, of course, a corollary to this argument. The more an asset is used, the less valuable it becomes. Think of the price and the value humans obtain (utility) from the use of gems like diamonds? If we all wore them, they would not be so exclusive, and the ability for sellers of such stones to charge a premium would fall. But of course, this is a chicken and egg argument. Its really about demand and supply- that is the crux of Bakker’s argument I think.
If demand for a piece of information increases, and we have access to it, and we can facilitates its re-use with a common framework (what Gartner calls an Information Capability Framework), then the economic model by which such value is extracted will necessarily be more efficient than one where use is silod or distinct. This makes sense – even if most organizations do not actually look at their IT capability using with an ICF framework.
The article goes on: “Data that is used in several aspects of the business, for example, tends to be more valuable to the business.”
This again is interesting – and self evident. If we can assure that the same data is re used, its value as a re usable asset goes up. The need to govern it, protect it, secure it, to account for it, goes up. That makes sense to me.
But then the article loses me. Here is where I get lost:
- “But as we move more toward Big Data, including large volumes of data along with multiple types of structured and unstructured data from multiple sources, the value of the data becomes more difficult to identify, quantify, and realize – and storing metadata becomes an increasing challenge in its own right.
- Knowing what to do with the data, and being able to do those things with the data, is key to unlocking the value of any data, big or otherwise.
- This, we suggest, makes the ability to effectively and efficiently manage data at least as valuable to the business as the data itself.”
I followed this until the last line. The last line reads suspiciously like the same email I received back in July 2012 that led me to blog on dark data in the first place. I was calling out the blind hype about investing in technology to store dark data. I said (in July 2012) “Unless you, the business user, have an idea of what you want to ask of this dark data, there is no point worrying about it. Good ideas don’t just come out of the woodwork or spring forth from a data mart. Business people have to have an idea, a question, an argument to test, a theory to explore, a posit to push against”
The article goes on to say:
“The greater the volume of data accessed and used, multiplied by the number of instances/situations/systems in which the data is used, increases not only the value of the data but the value (and cost) of managing that data. The increased volume and range of usage also increases the financial risk of data loss to the business, further increasing the value of effective and efficient data management.”
This is all kind of a wash with me. It is self evident – but there is some point in here that warrants attention. The more you centralize your business process integrity on fewer and fewer information assets, the greater the risk on those assets (points of reliance). This also leads to an improvement in the efficiency in managing those information assets (think mass production over one-off unique boutique efforts). But this is not new – and Big Data just makes the calculations larger – that’s all.
Overall I liked what I think the point of the article was about – but I didn’t feel that it really explored the question as much as I would like. The question of how to evaluate the value of data is vexing; better yet, how to get compound returns on investments in information (assets) is even better. That’s what IT has been trying to do for years, and continues to struggle to quantify that. Along those lines….the article then ends:
“For our arguments, “data management” includes all aspects of Master Data Management, such as (but not limited to) storage, standardization, access, integration, quality, identity management, masking/protection, and uptime/availability.”
I don’t equate MDM to “data management”. There is much money spent on “data management” that is not tied to business value, or increasing business outcomes. In fact more money than any of us would care to admit. MDM is not that – if adopted effectively. MDM is just one of several forms of data management that directly link to business value. That is why MDM is not your daddy’s data management efforts.
Our very own Doug Laney thinks on this topic (of quantifying inforamtion value) long and hard; and several of us do too. Doug will be at our upcoming Gartner Master Data Management summit in Texas March 20-22. Come along and hear Doug talk about Infonomics and the Information Innovation Yield Curve. I love to apply economic theory to information theory – it is a rich source of current research. I would love to hear your thoughts on information as an asset.
Category: big-data dark-data facebook infonomics information-as-an-asset information-innovation-yield-curve information-management information-theory master-data-management mdm-summit-na
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.