Saturday, May 31, 2014

What is the (data) point?

We have a growing ability to measure and record our environment and we are continuously increasing our data entropy. However our data still converges to the single decision point. We integrate several pieces of information and derive a conclusion which is, at the end of the day; only as good as the data it is based on.

Moreover, with evolving technologies these days, sophisticated data analysis tools and machine learning capabilities, we further see an increase in the automation and filtering of data as well as in decision making. Think, for example, about navigation systems, which gives you optimized driving directions, taking into account route and traffic information. This makes the need for accurate and well-managed fault tolerance levels even more important than ever.

A simple, yet powerful example on how vulnerable  we still are to the outcomes of poor data management is the recent disappearance of flight MH370. As you may know, this event has led to weeks of extensive searches and to what has been referred to as the most expensive search operation in history. Yet, while it is astonishing how limited amount of information was available to analyze, it is even more incomprehensible how ineffective the global community was in interpreting the data.

Now while it may be true that lack of data sharing of information related to the disappearance of the plane may stem from political and other reasons, it does show how far we are from servicing some of the most basic collaboration needs across our specie to act towards a common purpose. Furthermore, while it is may also be true that when we have multiple sources of information, it is easier for us to collaborate, it is precisely when we have little information, that the true quality of our ability to work towards a common purpose is exposed. These are situations that lead to sometimes critical decisions, which can have definitive and far-reaching implications on individuals and communities.

To me this screams communal data management immaturity. It is almost ironic, that while technologies have evolved to manage Peta bytes of data at the speed of light – our ability to tap into the real power of information remains in its infancy, especially when we cross communities and cultures.

But do not despair. We are still in the dawn of Teneo Vulgo, and we all know that a journey has to be completed one step at a time. We need to work on strengthening our close communal data management, and work towards bridging communal knowledge across isolated groups.

In conclusion, with the increase of data entropy, and our increasing need to apply information quickly and effectively – putting our head in the sand is simply not good enough.

Let’s work towards evolving our existence into a new level of social consciousness, where the exchange of information across communities becomes a force that helps us reach common goals.

Now did I hear someone saying data is boring?

Thursday, May 15, 2014

Data Diet

I was contemplating on the title for this thought and initially got concerned with the notion that a diet often refers to reducing or eliminating type of food from ones regular consumption. However, what seems to be a more popular definition for this term is: the usual, or regulated foods and drinks consumed by humans or animals [http://www.thefreedictionary.com/diet].

First of all, I would like to affirm that it is not my intention to suggest that you should necessarily consume less data, or less of certain types of data. Instead, I want to focus on the analogy, and on one of my usual themes which is tying business value to business case and to data management.

Why do we diet? well for starters, we need food to survive. Secondly, depending on what we care about (longevity, appearance etc.) - we may choose to regulate our diet to support our health and our fit to society norms. Now while the first reason seems to carry naturally in to the realm of data management, the second reason seems emotional and perhaps disconnected from the topic of data. However, I would like to argue this as false.

Here is why: Data is ultimately managed by people and confidence that the data is well managed, leads to an increase of trust in the data, which results in cheaper and faster data certification and adaptation to change. If the business is plagued with ambiguities, inconsistencies in data quality and overly-complex data delivery solutions - the amount of time and effort needed to address data consumers request is substantially longer since clarity and confidence must prevail. If the semantics are well understood and quality is adequately controlled - the time it takes to understand changes and to collate information is significantly reduced.

Now there are many strategies for managing your diet, and there are many strategies for managing your data. Depending on your business appetite, your daily business demands and the advantages you want to gain from your business level of fitness and fit in the markets in which you operate, you are likely to have a different set of constraints and preferences.

Yes, ultimately you need to choose which data you will consume, at what quantity and which data you will avoid. However, whatever you choose - make sure it fits your budget and your business case.

At the end of the day, being data-obese or data-anorexic are probably both extremes that will harm your business.

So eat wisely, consume from all the source-groups that you need, and do not indulge with data if you are running the risk of data inefficiencies.

Now I suppose the next question is what does "Data exercise” mean in this context... but this is probably a good topic for another post.